1804 lines
431 KiB
Plaintext
1804 lines
431 KiB
Plaintext
2024-10-14 11:02:12,150 (logging:124) INFO: the current machine is at 127.0.1.1
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2024-10-14 11:02:12,151 (logging:126) INFO: the current dir is /home/czzhangheng/code/FederatedScope
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2024-10-14 11:02:12,151 (logging:127) INFO: the output dir is exp/FedAvg_DDGCRN_on_trafficflow_lr0.01_lstep1/sub_exp_20241014110212
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2024-10-14 11:02:23,659 (config:243) INFO: the used configs are:
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aggregator:
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BFT_args:
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byzantine_node_num: 0
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inside_weight: 1.0
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num_agg_groups: 1
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num_agg_topk: []
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outside_weight: 0.0
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robust_rule: fedavg
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asyn:
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use: False
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attack:
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alpha_TV: 0.001
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alpha_prop_loss: 0
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attack_method:
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attacker_id: -1
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classifier_PIA: randomforest
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edge_num: 100
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edge_path: edge_data/
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freq: 10
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info_diff_type: l2
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inject_round: 0
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insert_round: 100000
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label_type: dirty
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max_ite: 400
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mean: [0.9637]
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mia_is_simulate_in: False
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mia_simulate_in_round: 20
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pgd_eps: 2
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pgd_lr: 0.1
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pgd_poisoning: False
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poison_ratio: 0.5
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reconstruct_lr: 0.01
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reconstruct_optim: Adam
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scale_para: 1.0
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scale_poisoning: False
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self_epoch: 6
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self_lr: 0.05
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self_opt: False
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setting: fix
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std: [0.1592]
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target_label_ind: -1
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trigger_path: trigger/
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trigger_type: edge
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backend: torch
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cfg_file:
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check_completeness: False
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criterion:
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type: L1Loss
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data:
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add_day_in_week: True
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add_time_in_day: True
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args: []
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batch_size: 64
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cSBM_phi: [0.5, 0.5, 0.5]
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cache_dir:
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column_wise: False
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consistent_label_distribution: True
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days_per_week: 7
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default_graph: True
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drop_last: False
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file_path:
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hetero_data_name: []
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hetero_synth_batch_size: 32
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hetero_synth_feat_dim: 128
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hetero_synth_prim_weight: 0.5
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horizon: 12
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is_debug: False
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lag: 12
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loader:
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max_query_len: 128
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max_seq_len: 384
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max_tgt_len: 128
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normalizer: std
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num_contrast: 0
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num_nodes: 358
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num_of_client_for_data: []
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num_steps: 30
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num_workers: 0
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pre_transform: []
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quadratic:
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dim: 1
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max_curv: 12.5
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min_curv: 0.02
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root: data/trafficflow/PeMS03
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save_data: False
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scaler: [181.375268, 144.408363]
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server_holds_all: False
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shuffle: True
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sizes: [10, 5]
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splits: [0.8, 0.1, 0.1]
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splitter: trafficflowprediction
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splitter_args: []
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steps_per_day: 288
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subsample: 1.0
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target_transform: []
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test_pre_transform: []
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test_ratio: 0.2
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test_target_transform: []
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test_transform: []
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tod: False
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transform: []
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trunc_stride: 128
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type: trafficflow
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val_pre_transform: []
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val_ratio: 0.2
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val_target_transform: []
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val_transform: []
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walk_length: 2
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dataloader:
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batch_size: 64
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drop_last: True
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num_steps: 30
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num_workers: 0
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pin_memory: False
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shuffle: True
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sizes: [10, 5]
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theta: -1
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type: trafficflow
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walk_length: 2
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device: 1
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distribute:
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use: False
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early_stop:
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delta: 0.0
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improve_indicator_mode: best
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patience: 15
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eval:
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best_res_update_round_wise_key: val_loss
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count_flops: True
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freq: 1
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metrics: ['avg_loss']
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monitoring: []
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report: ['weighted_avg', 'avg', 'fairness', 'raw']
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split: ['test', 'val']
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expname: FedAvg_DDGCRN_on_trafficflow_lr0.01_lstep1
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expname_tag:
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feat_engr:
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num_bins: 5
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scenario: hfl
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secure:
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dp:
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encrypt:
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type: dummy
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key_size: 3072
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type: encrypt
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selec_threshold: 0.05
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selec_woe_binning: quantile
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type:
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federate:
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atc_load_from:
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atc_vanilla: False
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client_num: 10
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data_weighted_aggr: False
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ignore_weight: False
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join_in_info: []
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make_global_eval: False
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master_addr: 127.0.0.1
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master_port: 29500
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merge_test_data: False
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merge_val_data: False
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method: FedAvg
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mode: standalone
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online_aggr: False
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process_num: 1
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resource_info_file:
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restore_from:
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sample_client_num: 10
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sample_client_rate: -1.0
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sampler: uniform
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save_to:
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share_local_model: False
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total_round_num: 100
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unseen_clients_rate: 0.0
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use_diff: False
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use_ss: False
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fedopt:
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use: False
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fedprox:
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use: False
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fedsageplus:
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a: 1.0
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b: 1.0
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c: 1.0
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fedgen_epoch: 200
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gen_hidden: 128
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hide_portion: 0.5
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loc_epoch: 1
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num_pred: 5
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fedswa:
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use: False
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finetune:
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batch_or_epoch: epoch
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before_eval: False
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epoch_linear: 10
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freeze_param:
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local_param: []
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local_update_steps: 1
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lr_linear: 0.005
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optimizer:
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lr: 0.1
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type: SGD
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scheduler:
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type:
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warmup_ratio: 0.0
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simple_tuning: False
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weight_decay: 0.0
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flitplus:
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factor_ema: 0.8
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lambdavat: 0.5
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tmpFed: 0.5
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weightReg: 1.0
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gcflplus:
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EPS_1: 0.05
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EPS_2: 0.1
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seq_length: 5
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standardize: False
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grad:
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grad_accum_count: 1
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grad_clip: 5.0
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hpo:
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fedex:
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cutoff: 0.0
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diff: False
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eta0: -1.0
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flatten_ss: True
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gamma: 0.0
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pi_lr: 0.01
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psn: False
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sched: auto
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ss:
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use: False
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fts:
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M: 100
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M_target: 200
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allow_load_existing_info: True
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diff: False
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fed_bo_max_iter: 50
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g_var: 1e-06
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gp_opt_schedule: 1
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local_bo_epochs: 50
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local_bo_max_iter: 50
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ls: 1.0
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obs_noise: 1e-06
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ss:
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target_clients: []
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use: False
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v_kernel: 1.0
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var: 0.1
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init_cand_num: 16
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larger_better: False
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metric: client_summarized_weighted_avg.val_loss
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num_workers: 0
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pbt:
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max_stage: 5
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perf_threshold: 0.1
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pfedhpo:
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discrete: False
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ss:
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target_fl_total_round: 1000
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train_anchor: False
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train_fl: False
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use: False
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scheduler: rs
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sha:
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budgets: []
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elim_rate: 3
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iter: 0
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ss:
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table:
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eps: 0.1
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idx: 0
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num: 27
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trial_index: 0
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working_folder: hpo
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model:
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cheb_order: 2
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contrast_temp: 1.0
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contrast_topk: 100
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downstream_tasks: []
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dropout: 0.1
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embed_dim: 10
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embed_size: 8
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gamma: 0
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graph_pooling: mean
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hidden: 256
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horizon: 12
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in_channels: 0
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input_dim: 1
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input_shape: ()
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label_smoothing: 0.1
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lambda_: 0.1
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layer: 2
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length_penalty: 2.0
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max_answer_len: 30
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max_length: 200
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max_tree_depth: 3
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min_length: 1
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model_num_per_trainer: 1
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model_type: google/bert_uncased_L-2_H-128_A-2
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n_best_size: 20
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no_repeat_ngram_size: 3
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null_score_diff_threshold: 0.0
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num_beams: 5
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num_item: 0
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num_labels: 1
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num_layers: 1
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num_nodes: 35
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num_of_trees: 10
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num_user: 0
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out_channels: 1
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output_dim: 1
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pretrain_tasks: []
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rnn_units: 64
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stage:
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task: TrafficFlowPrediction
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type: DDGCRN
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use_bias: True
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use_contrastive_loss: False
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use_day: True
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use_week: True
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nbafl:
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use: False
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outdir: exp/FedAvg_DDGCRN_on_trafficflow_lr0.01_lstep1/sub_exp_20241014110212
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personalization:
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K: 5
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beta: 1.0
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epoch_feature: 1
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epoch_linear: 2
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local_param: []
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local_update_steps: 1
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lr: 0.01
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lr_feature: 0.1
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lr_linear: 0.1
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regular_weight: 0.1
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share_non_trainable_para: False
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weight_decay: 0.0
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print_decimal_digits: 6
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quantization:
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method: none
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nbits: 8
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regularizer:
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mu: 0.0
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type:
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seed: 10
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sgdmf:
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use: False
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train:
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batch_or_epoch: epoch
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batch_size: 64
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data_para_dids: []
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early_stop: False
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early_stop_patience: 15
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epochs: 300
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grad_norm: True
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local_update_steps: 1
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loss_func: mae
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lr_decay: False
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lr_decay_rate: 0.3
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lr_decay_step: [5, 20, 40, 70]
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lr_init: 0.003
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max_grad_norm: 5
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optimizer:
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lr: 0.01
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type: Adam
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weight_decay: 0.0
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real_value: True
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scheduler:
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type:
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warmup_ratio: 0.0
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seed: 10
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weight_decay: 0
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trainer:
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disp_freq: 50
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local_entropy:
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alpha: 0.75
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eps: 0.0001
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gamma: 0.03
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inc_factor: 1.0
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log_dir: ./
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sam:
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adaptive: False
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eta: 0.0
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rho: 1.0
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type: trafficflowtrainer
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val_freq: 100000000
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use_gpu: True
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verbose: 1
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vertical:
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use: False
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wandb:
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use: False
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2024-10-14 11:02:23,834 (utils:147) INFO: The device information file is not provided
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2024-10-14 11:02:23,874 (fed_runner:173) INFO: Server has been set up ...
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2024-10-14 11:02:23,902 (fed_runner:225) INFO: Client 1 has been set up ...
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2024-10-14 11:02:23,924 (fed_runner:225) INFO: Client 2 has been set up ...
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2024-10-14 11:02:23,944 (fed_runner:225) INFO: Client 3 has been set up ...
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2024-10-14 11:02:23,964 (fed_runner:225) INFO: Client 4 has been set up ...
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2024-10-14 11:02:23,983 (fed_runner:225) INFO: Client 5 has been set up ...
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2024-10-14 11:02:24,003 (fed_runner:225) INFO: Client 6 has been set up ...
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2024-10-14 11:02:24,021 (fed_runner:225) INFO: Client 7 has been set up ...
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2024-10-14 11:02:24,039 (fed_runner:225) INFO: Client 8 has been set up ...
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2024-10-14 11:02:24,058 (fed_runner:225) INFO: Client 9 has been set up ...
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2024-10-14 11:02:24,083 (fed_runner:225) INFO: Client 10 has been set up ...
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2024-10-14 11:02:24,084 (trainer:345) INFO: Model meta-info: <class 'federatedscope.trafficflow.model.DDGCRN.DDGCRN'>.
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2024-10-14 11:02:24,085 (trainer:353) INFO: Num of original para names: 50.
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2024-10-14 11:02:24,085 (trainer:354) INFO: Num of original trainable para names: 50.
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2024-10-14 11:02:24,085 (trainer:356) INFO: Num of preserved para names in local update: 50.
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Preserved para names in local update: {'encoder1.DGCRM_cells.0.update.fc.fc1.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc3.bias', 'D_i_W_emb', 'encoder2.DGCRM_cells.0.gate.fc.fc3.bias', 'encoder1.DGCRM_cells.0.update.weights_pool', 'encoder1.DGCRM_cells.0.update.fc.fc2.weight', 'encoder1.DGCRM_cells.0.update.bias_pool', 'encoder2.DGCRM_cells.0.gate.bias_pool', 'end_conv2.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc3.weight', 'encoder2.DGCRM_cells.0.update.bias', 'encoder1.DGCRM_cells.0.update.bias', 'encoder2.DGCRM_cells.0.update.weights_pool', 'encoder1.DGCRM_cells.0.gate.fc.fc2.weight', 'encoder2.DGCRM_cells.0.gate.weights_pool', 'encoder1.DGCRM_cells.0.gate.bias', 'end_conv1.weight', 'encoder1.DGCRM_cells.0.update.fc.fc3.weight', 'node_embeddings2', 'encoder1.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder1.DGCRM_cells.0.gate.bias_pool', 'encoder2.DGCRM_cells.0.update.fc.fc2.weight', 'encoder2.DGCRM_cells.0.update.fc.fc3.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc2.bias', 'encoder1.DGCRM_cells.0.gate.weights', 'encoder1.DGCRM_cells.0.update.weights', 'encoder2.DGCRM_cells.0.gate.weights', 'encoder1.DGCRM_cells.0.gate.weights_pool', 'encoder2.DGCRM_cells.0.update.fc.fc3.bias', 'encoder2.DGCRM_cells.0.update.bias_pool', 'encoder1.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder2.DGCRM_cells.0.update.fc.fc2.bias', 'end_conv3.weight', 'T_i_D_emb', 'encoder2.DGCRM_cells.0.update.fc.fc1.weight', 'node_embeddings1', 'encoder2.DGCRM_cells.0.gate.bias', 'encoder2.DGCRM_cells.0.update.fc.fc1.bias', 'end_conv1.bias', 'end_conv2.weight', 'end_conv3.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc2.weight', 'encoder1.DGCRM_cells.0.update.fc.fc3.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc2.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc3.weight', 'encoder1.DGCRM_cells.0.update.fc.fc1.bias', 'encoder1.DGCRM_cells.0.update.fc.fc2.bias', 'encoder2.DGCRM_cells.0.update.weights'}.
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2024-10-14 11:02:24,086 (trainer:360) INFO: Num of filtered para names in local update: 0.
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Filtered para names in local update: set().
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2024-10-14 11:02:24,086 (trainer:365) INFO: After register default hooks,
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the hooks_in_train is:
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{
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"on_fit_start": [
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"_hook_on_data_parallel_init",
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"_hook_on_fit_start_init",
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"_hook_on_fit_start_calculate_model_size"
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],
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"on_epoch_start": [
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"_hook_on_epoch_start"
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],
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"on_batch_start": [
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"_hook_on_batch_start_init"
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],
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"on_batch_forward": [
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"_hook_on_batch_forward",
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"_hook_on_batch_forward_regularizer",
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"_hook_on_batch_forward_flop_count"
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],
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"on_batch_backward": [
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"_hook_on_batch_backward"
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],
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"on_batch_end": [
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"_hook_on_batch_end"
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],
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"on_fit_end": [
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"_hook_on_fit_end"
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]
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};
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the hooks_in_eval is:
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t{
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"on_fit_start": [
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"_hook_on_data_parallel_init",
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"_hook_on_fit_start_init"
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],
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"on_epoch_start": [
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"_hook_on_epoch_start"
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],
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"on_batch_start": [
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"_hook_on_batch_start_init"
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],
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"on_batch_forward": [
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"_hook_on_batch_forward"
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],
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"on_batch_end": [
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"_hook_on_batch_end"
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],
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"on_fit_end": [
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"_hook_on_fit_end"
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]
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}
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2024-10-14 11:02:24,100 (server:843) INFO: ----------- Starting training (Round #0) -------------
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2024-10-14 11:03:22,477 (client:354) INFO: {'Role': 'Client #9', 'Round': 0, 'Results_raw': {'train_loss': 26.776096, 'val_loss': 20.580183, 'test_loss': 21.220233}}
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2024-10-14 11:04:18,911 (client:354) INFO: {'Role': 'Client #3', 'Round': 0, 'Results_raw': {'train_loss': 17.055148, 'val_loss': 12.578216, 'test_loss': 13.064859}}
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2024-10-14 11:05:23,071 (client:354) INFO: {'Role': 'Client #6', 'Round': 0, 'Results_raw': {'train_loss': 24.256524, 'val_loss': 17.388443, 'test_loss': 17.926698}}
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2024-10-14 11:06:21,741 (client:354) INFO: {'Role': 'Client #7', 'Round': 0, 'Results_raw': {'train_loss': 24.477838, 'val_loss': 18.489804, 'test_loss': 18.333504}}
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2024-10-14 11:07:23,113 (client:354) INFO: {'Role': 'Client #4', 'Round': 0, 'Results_raw': {'train_loss': 23.231104, 'val_loss': 17.529725, 'test_loss': 17.728845}}
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2024-10-14 11:08:26,503 (client:354) INFO: {'Role': 'Client #2', 'Round': 0, 'Results_raw': {'train_loss': 15.828423, 'val_loss': 10.236525, 'test_loss': 10.585302}}
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2024-10-14 11:09:25,272 (client:354) INFO: {'Role': 'Client #1', 'Round': 0, 'Results_raw': {'train_loss': 18.60888, 'val_loss': 12.588376, 'test_loss': 12.907783}}
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2024-10-14 11:10:25,337 (client:354) INFO: {'Role': 'Client #8', 'Round': 0, 'Results_raw': {'train_loss': 21.088737, 'val_loss': 15.53862, 'test_loss': 15.499011}}
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2024-10-14 11:11:25,445 (client:354) INFO: {'Role': 'Client #5', 'Round': 0, 'Results_raw': {'train_loss': 24.761776, 'val_loss': 19.419424, 'test_loss': 20.566341}}
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2024-10-14 11:12:31,168 (client:354) INFO: {'Role': 'Client #10', 'Round': 0, 'Results_raw': {'train_loss': 24.246127, 'val_loss': 17.923841, 'test_loss': 18.223964}}
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2024-10-14 11:12:31,217 (server:353) INFO: Server: Starting evaluation at the end of round 0.
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2024-10-14 11:12:31,218 (server:359) INFO: ----------- Starting a new training round (Round #1) -------------
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2024-10-14 11:15:05,974 (client:354) INFO: {'Role': 'Client #6', 'Round': 1, 'Results_raw': {'train_loss': 19.883245, 'val_loss': 17.002764, 'test_loss': 17.51469}}
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2024-10-14 11:16:09,562 (client:354) INFO: {'Role': 'Client #4', 'Round': 1, 'Results_raw': {'train_loss': 19.712126, 'val_loss': 17.313235, 'test_loss': 17.433109}}
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2024-10-14 11:17:08,526 (client:354) INFO: {'Role': 'Client #5', 'Round': 1, 'Results_raw': {'train_loss': 20.660688, 'val_loss': 18.636719, 'test_loss': 20.168988}}
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2024-10-14 11:18:09,975 (client:354) INFO: {'Role': 'Client #8', 'Round': 1, 'Results_raw': {'train_loss': 17.207347, 'val_loss': 14.910196, 'test_loss': 15.361835}}
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2024-10-14 11:19:19,403 (client:354) INFO: {'Role': 'Client #7', 'Round': 1, 'Results_raw': {'train_loss': 20.18535, 'val_loss': 17.683722, 'test_loss': 17.453739}}
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2024-10-14 11:20:18,310 (client:354) INFO: {'Role': 'Client #9', 'Round': 1, 'Results_raw': {'train_loss': 22.951933, 'val_loss': 20.057874, 'test_loss': 20.305727}}
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2024-10-14 11:21:17,706 (client:354) INFO: {'Role': 'Client #10', 'Round': 1, 'Results_raw': {'train_loss': 19.712532, 'val_loss': 17.111194, 'test_loss': 17.683919}}
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2024-10-14 11:22:17,327 (client:354) INFO: {'Role': 'Client #3', 'Round': 1, 'Results_raw': {'train_loss': 13.686839, 'val_loss': 12.262396, 'test_loss': 12.797982}}
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2024-10-14 11:23:19,881 (client:354) INFO: {'Role': 'Client #2', 'Round': 1, 'Results_raw': {'train_loss': 12.302881, 'val_loss': 9.978116, 'test_loss': 10.004912}}
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2024-10-14 11:24:21,317 (client:354) INFO: {'Role': 'Client #1', 'Round': 1, 'Results_raw': {'train_loss': 14.531563, 'val_loss': 12.478991, 'test_loss': 12.790319}}
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2024-10-14 11:24:21,323 (server:615) INFO: {'Role': 'Server #', 'Round': 0, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(29.307579), 'test_loss': np.float64(151930.491974), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.318227), 'val_loss': np.float64(157169.686261)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(29.307579), 'test_loss': np.float64(151930.491974), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.318227), 'val_loss': np.float64(157169.686261)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.419558), 'test_avg_loss_bottom_decile': np.float64(23.170059), 'test_avg_loss_top_decile': np.float64(35.499718), 'test_avg_loss_min': np.float64(18.069055), 'test_avg_loss_max': np.float64(35.499718), 'test_avg_loss_bottom10%': np.float64(18.069055), 'test_avg_loss_top10%': np.float64(35.499718), 'test_avg_loss_cos1': np.float64(0.983329), 'test_avg_loss_entropy': np.float64(2.284346), 'test_loss_std': np.float64(28094.990754), 'test_loss_bottom_decile': np.float64(120113.584106), 'test_loss_top_decile': np.float64(184030.536865), 'test_loss_min': np.float64(93669.979675), 'test_loss_max': np.float64(184030.536865), 'test_loss_bottom10%': np.float64(93669.979675), 'test_loss_top10%': np.float64(184030.536865), 'test_loss_cos1': np.float64(0.983329), 'test_loss_entropy': np.float64(2.284346), 'val_avg_loss_std': np.float64(5.984593), 'val_avg_loss_bottom_decile': np.float64(23.450027), 'val_avg_loss_top_decile': np.float64(36.535143), 'val_avg_loss_min': np.float64(18.289794), 'val_avg_loss_max': np.float64(36.535143), 'val_avg_loss_bottom10%': np.float64(18.289794), 'val_avg_loss_top10%': np.float64(36.535143), 'val_avg_loss_cos1': np.float64(0.98107), 'val_avg_loss_entropy': np.float64(2.28173), 'val_loss_std': np.float64(31024.129386), 'val_loss_bottom_decile': np.float64(121564.941711), 'val_loss_top_decile': np.float64(189398.18103), 'val_loss_min': np.float64(94814.290771), 'val_loss_max': np.float64(189398.18103), 'val_loss_bottom10%': np.float64(94814.290771), 'val_loss_top10%': np.float64(189398.18103), 'val_loss_cos1': np.float64(0.98107), 'val_loss_entropy': np.float64(2.28173)}}
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2024-10-14 11:24:21,378 (server:353) INFO: Server: Starting evaluation at the end of round 1.
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2024-10-14 11:24:21,379 (server:359) INFO: ----------- Starting a new training round (Round #2) -------------
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2024-10-14 11:27:03,572 (client:354) INFO: {'Role': 'Client #2', 'Round': 2, 'Results_raw': {'train_loss': 11.36042, 'val_loss': 9.742636, 'test_loss': 10.087132}}
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2024-10-14 11:28:29,628 (client:354) INFO: {'Role': 'Client #7', 'Round': 2, 'Results_raw': {'train_loss': 18.972171, 'val_loss': 17.140703, 'test_loss': 17.215168}}
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2024-10-14 11:29:58,165 (client:354) INFO: {'Role': 'Client #3', 'Round': 2, 'Results_raw': {'train_loss': 12.95635, 'val_loss': 11.723309, 'test_loss': 12.383444}}
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2024-10-14 11:31:03,574 (client:354) INFO: {'Role': 'Client #6', 'Round': 2, 'Results_raw': {'train_loss': 18.493204, 'val_loss': 16.535201, 'test_loss': 16.753463}}
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2024-10-14 11:31:59,460 (client:354) INFO: {'Role': 'Client #8', 'Round': 2, 'Results_raw': {'train_loss': 16.090181, 'val_loss': 14.823624, 'test_loss': 15.070976}}
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2024-10-14 11:32:58,286 (client:354) INFO: {'Role': 'Client #9', 'Round': 2, 'Results_raw': {'train_loss': 21.148018, 'val_loss': 19.578298, 'test_loss': 19.995684}}
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2024-10-14 11:33:55,658 (client:354) INFO: {'Role': 'Client #10', 'Round': 2, 'Results_raw': {'train_loss': 18.311213, 'val_loss': 16.746644, 'test_loss': 17.695745}}
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2024-10-14 11:34:54,209 (client:354) INFO: {'Role': 'Client #1', 'Round': 2, 'Results_raw': {'train_loss': 13.552442, 'val_loss': 12.270604, 'test_loss': 12.933428}}
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2024-10-14 11:35:51,865 (client:354) INFO: {'Role': 'Client #4', 'Round': 2, 'Results_raw': {'train_loss': 18.284789, 'val_loss': 16.544803, 'test_loss': 16.955428}}
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2024-10-14 11:36:52,637 (client:354) INFO: {'Role': 'Client #5', 'Round': 2, 'Results_raw': {'train_loss': 19.242839, 'val_loss': 18.471215, 'test_loss': 20.194476}}
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2024-10-14 11:36:52,642 (server:615) INFO: {'Role': 'Server #', 'Round': 1, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(24.612826), 'test_loss': np.float64(127592.887842), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(25.469152), 'val_loss': np.float64(132032.081927)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(24.612826), 'test_loss': np.float64(127592.887842), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(25.469152), 'val_loss': np.float64(132032.081927)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.593048), 'test_avg_loss_bottom_decile': np.float64(19.431924), 'test_avg_loss_top_decile': np.float64(30.088815), 'test_avg_loss_min': np.float64(15.551289), 'test_avg_loss_max': np.float64(30.088815), 'test_avg_loss_bottom10%': np.float64(15.551289), 'test_avg_loss_top10%': np.float64(30.088815), 'test_avg_loss_cos1': np.float64(0.98303), 'test_avg_loss_entropy': np.float64(2.284188), 'test_loss_std': np.float64(23810.363246), 'test_loss_bottom_decile': np.float64(100735.09198), 'test_loss_top_decile': np.float64(155980.416809), 'test_loss_min': np.float64(80617.88324), 'test_loss_max': np.float64(155980.416809), 'test_loss_bottom10%': np.float64(80617.88324), 'test_loss_top10%': np.float64(155980.416809), 'test_loss_cos1': np.float64(0.98303), 'test_loss_entropy': np.float64(2.284188), 'val_avg_loss_std': np.float64(5.027127), 'val_avg_loss_bottom_decile': np.float64(19.281558), 'val_avg_loss_top_decile': np.float64(30.960659), 'val_avg_loss_min': np.float64(15.807227), 'val_avg_loss_max': np.float64(30.960659), 'val_avg_loss_bottom10%': np.float64(15.807227), 'val_avg_loss_top10%': np.float64(30.960659), 'val_avg_loss_cos1': np.float64(0.981072), 'val_avg_loss_entropy': np.float64(2.281899), 'val_loss_std': np.float64(26060.627276), 'val_loss_bottom_decile': np.float64(99955.594666), 'val_loss_top_decile': np.float64(160500.056763), 'val_loss_min': np.float64(81944.664673), 'val_loss_max': np.float64(160500.056763), 'val_loss_bottom10%': np.float64(81944.664673), 'val_loss_top10%': np.float64(160500.056763), 'val_loss_cos1': np.float64(0.981072), 'val_loss_entropy': np.float64(2.281899)}}
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2024-10-14 11:36:52,687 (server:353) INFO: Server: Starting evaluation at the end of round 2.
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2024-10-14 11:36:52,687 (server:359) INFO: ----------- Starting a new training round (Round #3) -------------
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2024-10-14 11:39:25,502 (client:354) INFO: {'Role': 'Client #1', 'Round': 3, 'Results_raw': {'train_loss': 12.975942, 'val_loss': 11.663653, 'test_loss': 12.404027}}
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2024-10-14 11:40:24,976 (client:354) INFO: {'Role': 'Client #8', 'Round': 3, 'Results_raw': {'train_loss': 15.467734, 'val_loss': 14.39042, 'test_loss': 14.776498}}
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2024-10-14 11:41:19,816 (client:354) INFO: {'Role': 'Client #6', 'Round': 3, 'Results_raw': {'train_loss': 17.633689, 'val_loss': 16.318405, 'test_loss': 16.950131}}
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2024-10-14 11:42:17,133 (client:354) INFO: {'Role': 'Client #3', 'Round': 3, 'Results_raw': {'train_loss': 12.300482, 'val_loss': 11.46821, 'test_loss': 12.288746}}
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2024-10-14 11:43:18,337 (client:354) INFO: {'Role': 'Client #4', 'Round': 3, 'Results_raw': {'train_loss': 17.572269, 'val_loss': 16.211197, 'test_loss': 16.749503}}
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2024-10-14 11:44:17,261 (client:354) INFO: {'Role': 'Client #10', 'Round': 3, 'Results_raw': {'train_loss': 17.540511, 'val_loss': 16.527521, 'test_loss': 17.609506}}
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2024-10-14 11:45:19,009 (client:354) INFO: {'Role': 'Client #7', 'Round': 3, 'Results_raw': {'train_loss': 17.809161, 'val_loss': 16.838262, 'test_loss': 16.989055}}
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2024-10-14 11:46:22,415 (client:354) INFO: {'Role': 'Client #5', 'Round': 3, 'Results_raw': {'train_loss': 18.47034, 'val_loss': 18.013594, 'test_loss': 19.894488}}
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2024-10-14 11:47:23,010 (client:354) INFO: {'Role': 'Client #9', 'Round': 3, 'Results_raw': {'train_loss': 20.300718, 'val_loss': 19.008665, 'test_loss': 19.195695}}
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2024-10-14 11:48:23,156 (client:354) INFO: {'Role': 'Client #2', 'Round': 3, 'Results_raw': {'train_loss': 10.93678, 'val_loss': 9.648989, 'test_loss': 9.999004}}
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2024-10-14 11:48:23,161 (server:615) INFO: {'Role': 'Server #', 'Round': 2, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(22.498046), 'test_loss': np.float64(116629.869638), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(23.075382), 'val_loss': np.float64(119622.779913)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(22.498046), 'test_loss': np.float64(116629.869638), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(23.075382), 'val_loss': np.float64(119622.779913)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(3.467842), 'test_avg_loss_bottom_decile': np.float64(18.560665), 'test_avg_loss_top_decile': np.float64(27.259821), 'test_avg_loss_min': np.float64(15.821115), 'test_avg_loss_max': np.float64(27.259821), 'test_avg_loss_bottom10%': np.float64(15.821115), 'test_avg_loss_top10%': np.float64(27.259821), 'test_avg_loss_cos1': np.float64(0.988328), 'test_avg_loss_entropy': np.float64(2.290298), 'test_loss_std': np.float64(17977.294689), 'test_loss_bottom_decile': np.float64(96218.484985), 'test_loss_top_decile': np.float64(141314.914246), 'test_loss_min': np.float64(82016.660706), 'test_loss_max': np.float64(141314.914246), 'test_loss_bottom10%': np.float64(82016.660706), 'test_loss_top10%': np.float64(141314.914246), 'test_loss_cos1': np.float64(0.988328), 'test_loss_entropy': np.float64(2.290298), 'val_avg_loss_std': np.float64(3.831894), 'val_avg_loss_bottom_decile': np.float64(18.705531), 'val_avg_loss_top_decile': np.float64(27.661563), 'val_avg_loss_min': np.float64(15.836462), 'val_avg_loss_max': np.float64(27.661563), 'val_avg_loss_bottom10%': np.float64(15.836462), 'val_avg_loss_top10%': np.float64(27.661563), 'val_avg_loss_cos1': np.float64(0.986491), 'val_avg_loss_entropy': np.float64(2.288231), 'val_loss_std': np.float64(19864.539996), 'val_loss_bottom_decile': np.float64(96969.471497), 'val_loss_top_decile': np.float64(143397.543945), 'val_loss_min': np.float64(82096.217773), 'val_loss_max': np.float64(143397.543945), 'val_loss_bottom10%': np.float64(82096.217773), 'val_loss_top10%': np.float64(143397.543945), 'val_loss_cos1': np.float64(0.986491), 'val_loss_entropy': np.float64(2.288231)}}
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2024-10-14 11:48:23,217 (server:353) INFO: Server: Starting evaluation at the end of round 3.
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2024-10-14 11:48:23,217 (server:359) INFO: ----------- Starting a new training round (Round #4) -------------
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2024-10-14 11:50:58,470 (client:354) INFO: {'Role': 'Client #3', 'Round': 4, 'Results_raw': {'train_loss': 11.958844, 'val_loss': 11.335305, 'test_loss': 12.186206}}
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2024-10-14 11:51:56,310 (client:354) INFO: {'Role': 'Client #1', 'Round': 4, 'Results_raw': {'train_loss': 12.454124, 'val_loss': 11.44892, 'test_loss': 12.123936}}
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2024-10-14 11:52:53,464 (client:354) INFO: {'Role': 'Client #10', 'Round': 4, 'Results_raw': {'train_loss': 17.123136, 'val_loss': 16.462135, 'test_loss': 17.821813}}
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2024-10-14 11:53:52,715 (client:354) INFO: {'Role': 'Client #6', 'Round': 4, 'Results_raw': {'train_loss': 17.21555, 'val_loss': 16.042974, 'test_loss': 16.561433}}
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2024-10-14 11:54:53,196 (client:354) INFO: {'Role': 'Client #5', 'Round': 4, 'Results_raw': {'train_loss': 18.000797, 'val_loss': 17.702108, 'test_loss': 19.539275}}
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2024-10-14 11:55:59,923 (client:354) INFO: {'Role': 'Client #2', 'Round': 4, 'Results_raw': {'train_loss': 10.436838, 'val_loss': 9.458317, 'test_loss': 9.814006}}
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2024-10-14 11:56:57,051 (client:354) INFO: {'Role': 'Client #8', 'Round': 4, 'Results_raw': {'train_loss': 15.039615, 'val_loss': 14.039078, 'test_loss': 14.503819}}
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2024-10-14 11:57:57,170 (client:354) INFO: {'Role': 'Client #9', 'Round': 4, 'Results_raw': {'train_loss': 19.907574, 'val_loss': 19.04614, 'test_loss': 19.510896}}
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2024-10-14 11:58:57,440 (client:354) INFO: {'Role': 'Client #7', 'Round': 4, 'Results_raw': {'train_loss': 17.408881, 'val_loss': 16.502459, 'test_loss': 16.77107}}
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2024-10-14 11:59:54,197 (client:354) INFO: {'Role': 'Client #4', 'Round': 4, 'Results_raw': {'train_loss': 17.089163, 'val_loss': 15.928887, 'test_loss': 16.507953}}
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2024-10-14 11:59:54,202 (server:615) INFO: {'Role': 'Server #', 'Round': 3, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(22.121286), 'test_loss': np.float64(114676.748672), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(22.558995), 'val_loss': np.float64(116945.831927)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(22.121286), 'test_loss': np.float64(114676.748672), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(22.558995), 'val_loss': np.float64(116945.831927)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(3.106748), 'test_avg_loss_bottom_decile': np.float64(18.62244), 'test_avg_loss_top_decile': np.float64(26.818705), 'test_avg_loss_min': np.float64(16.154225), 'test_avg_loss_max': np.float64(26.818705), 'test_avg_loss_bottom10%': np.float64(16.154225), 'test_avg_loss_top10%': np.float64(26.818705), 'test_avg_loss_cos1': np.float64(0.990282), 'test_avg_loss_entropy': np.float64(2.292474), 'test_loss_std': np.float64(16105.383379), 'test_loss_bottom_decile': np.float64(96538.729065), 'test_loss_top_decile': np.float64(139028.164856), 'test_loss_min': np.float64(83743.500092), 'test_loss_max': np.float64(139028.164856), 'test_loss_bottom10%': np.float64(83743.500092), 'test_loss_top10%': np.float64(139028.164856), 'test_loss_cos1': np.float64(0.990282), 'test_loss_entropy': np.float64(2.292474), 'val_avg_loss_std': np.float64(3.408404), 'val_avg_loss_bottom_decile': np.float64(18.643754), 'val_avg_loss_top_decile': np.float64(27.076322), 'val_avg_loss_min': np.float64(16.181637), 'val_avg_loss_max': np.float64(27.076322), 'val_avg_loss_bottom10%': np.float64(16.181637), 'val_avg_loss_top10%': np.float64(27.076322), 'val_avg_loss_cos1': np.float64(0.988778), 'val_avg_loss_entropy': np.float64(2.290829), 'val_loss_std': np.float64(17669.164645), 'val_loss_bottom_decile': np.float64(96649.221893), 'val_loss_top_decile': np.float64(140363.653931), 'val_loss_min': np.float64(83885.60434), 'val_loss_max': np.float64(140363.653931), 'val_loss_bottom10%': np.float64(83885.60434), 'val_loss_top10%': np.float64(140363.653931), 'val_loss_cos1': np.float64(0.988778), 'val_loss_entropy': np.float64(2.290829)}}
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2024-10-14 11:59:54,248 (server:353) INFO: Server: Starting evaluation at the end of round 4.
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2024-10-14 11:59:54,249 (server:359) INFO: ----------- Starting a new training round (Round #5) -------------
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2024-10-14 12:02:13,364 (client:354) INFO: {'Role': 'Client #2', 'Round': 5, 'Results_raw': {'train_loss': 10.136042, 'val_loss': 9.17152, 'test_loss': 9.55757}}
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2024-10-14 12:03:01,907 (client:354) INFO: {'Role': 'Client #5', 'Round': 5, 'Results_raw': {'train_loss': 17.763112, 'val_loss': 17.595227, 'test_loss': 19.487313}}
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2024-10-14 12:03:52,327 (client:354) INFO: {'Role': 'Client #10', 'Round': 5, 'Results_raw': {'train_loss': 16.846146, 'val_loss': 16.202215, 'test_loss': 17.430244}}
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2024-10-14 12:04:43,411 (client:354) INFO: {'Role': 'Client #6', 'Round': 5, 'Results_raw': {'train_loss': 16.907767, 'val_loss': 15.753186, 'test_loss': 16.647285}}
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2024-10-14 12:05:37,886 (client:354) INFO: {'Role': 'Client #4', 'Round': 5, 'Results_raw': {'train_loss': 16.841841, 'val_loss': 16.394791, 'test_loss': 16.783585}}
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2024-10-14 12:06:34,834 (client:354) INFO: {'Role': 'Client #1', 'Round': 5, 'Results_raw': {'train_loss': 12.267347, 'val_loss': 12.089549, 'test_loss': 12.704796}}
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2024-10-14 12:07:28,300 (client:354) INFO: {'Role': 'Client #9', 'Round': 5, 'Results_raw': {'train_loss': 19.645773, 'val_loss': 18.871255, 'test_loss': 19.355893}}
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2024-10-14 12:08:18,445 (client:354) INFO: {'Role': 'Client #8', 'Round': 5, 'Results_raw': {'train_loss': 14.858688, 'val_loss': 14.189496, 'test_loss': 14.588103}}
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2024-10-14 12:09:08,782 (client:354) INFO: {'Role': 'Client #7', 'Round': 5, 'Results_raw': {'train_loss': 17.127682, 'val_loss': 16.397957, 'test_loss': 16.647077}}
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2024-10-14 12:10:00,341 (client:354) INFO: {'Role': 'Client #3', 'Round': 5, 'Results_raw': {'train_loss': 11.592022, 'val_loss': 11.415016, 'test_loss': 12.262941}}
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2024-10-14 12:10:00,344 (server:615) INFO: {'Role': 'Server #', 'Round': 4, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(21.97338), 'test_loss': np.float64(113910.002078), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(22.406309), 'val_loss': np.float64(116154.306241)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(21.97338), 'test_loss': np.float64(113910.002078), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(22.406309), 'val_loss': np.float64(116154.306241)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(3.148569), 'test_avg_loss_bottom_decile': np.float64(18.238777), 'test_avg_loss_top_decile': np.float64(26.911451), 'test_avg_loss_min': np.float64(16.147504), 'test_avg_loss_max': np.float64(26.911451), 'test_avg_loss_bottom10%': np.float64(16.147504), 'test_avg_loss_top10%': np.float64(26.911451), 'test_avg_loss_cos1': np.float64(0.989889), 'test_avg_loss_entropy': np.float64(2.292108), 'test_loss_std': np.float64(16322.183236), 'test_loss_bottom_decile': np.float64(94549.817474), 'test_loss_top_decile': np.float64(139508.964539), 'test_loss_min': np.float64(83708.661255), 'test_loss_max': np.float64(139508.964539), 'test_loss_bottom10%': np.float64(83708.661255), 'test_loss_top10%': np.float64(139508.964539), 'test_loss_cos1': np.float64(0.989889), 'test_loss_entropy': np.float64(2.292108), 'val_avg_loss_std': np.float64(3.443101), 'val_avg_loss_bottom_decile': np.float64(18.207627), 'val_avg_loss_top_decile': np.float64(27.172035), 'val_avg_loss_min': np.float64(16.197805), 'val_avg_loss_max': np.float64(27.172035), 'val_avg_loss_bottom10%': np.float64(16.197805), 'val_avg_loss_top10%': np.float64(27.172035), 'val_avg_loss_cos1': np.float64(0.988398), 'val_avg_loss_entropy': np.float64(2.290475), 'val_loss_std': np.float64(17849.038016), 'val_loss_bottom_decile': np.float64(94388.336426), 'val_loss_top_decile': np.float64(140859.831665), 'val_loss_min': np.float64(83969.419373), 'val_loss_max': np.float64(140859.831665), 'val_loss_bottom10%': np.float64(83969.419373), 'val_loss_top10%': np.float64(140859.831665), 'val_loss_cos1': np.float64(0.988398), 'val_loss_entropy': np.float64(2.290475)}}
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2024-10-14 12:10:00,378 (server:353) INFO: Server: Starting evaluation at the end of round 5.
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2024-10-14 12:10:00,378 (server:359) INFO: ----------- Starting a new training round (Round #6) -------------
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2024-10-14 12:12:25,366 (client:354) INFO: {'Role': 'Client #6', 'Round': 6, 'Results_raw': {'train_loss': 16.633197, 'val_loss': 15.881511, 'test_loss': 16.866261}}
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2024-10-14 12:13:14,904 (client:354) INFO: {'Role': 'Client #5', 'Round': 6, 'Results_raw': {'train_loss': 17.589511, 'val_loss': 17.433163, 'test_loss': 19.258864}}
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2024-10-14 12:14:06,478 (client:354) INFO: {'Role': 'Client #9', 'Round': 6, 'Results_raw': {'train_loss': 19.432653, 'val_loss': 18.782212, 'test_loss': 19.264222}}
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2024-10-14 12:14:56,876 (client:354) INFO: {'Role': 'Client #2', 'Round': 6, 'Results_raw': {'train_loss': 9.980496, 'val_loss': 9.01271, 'test_loss': 9.500356}}
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2024-10-14 12:15:47,201 (client:354) INFO: {'Role': 'Client #4', 'Round': 6, 'Results_raw': {'train_loss': 16.71446, 'val_loss': 15.831844, 'test_loss': 16.471789}}
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2024-10-14 12:16:36,876 (client:354) INFO: {'Role': 'Client #8', 'Round': 6, 'Results_raw': {'train_loss': 14.619509, 'val_loss': 13.83226, 'test_loss': 14.33357}}
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2024-10-14 12:17:27,385 (client:354) INFO: {'Role': 'Client #7', 'Round': 6, 'Results_raw': {'train_loss': 16.923622, 'val_loss': 16.123152, 'test_loss': 16.496381}}
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2024-10-14 12:18:17,957 (client:354) INFO: {'Role': 'Client #1', 'Round': 6, 'Results_raw': {'train_loss': 12.043848, 'val_loss': 11.152419, 'test_loss': 11.948657}}
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2024-10-14 12:19:07,956 (client:354) INFO: {'Role': 'Client #3', 'Round': 6, 'Results_raw': {'train_loss': 11.395071, 'val_loss': 11.252342, 'test_loss': 12.201283}}
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2024-10-14 12:19:57,737 (client:354) INFO: {'Role': 'Client #10', 'Round': 6, 'Results_raw': {'train_loss': 16.556039, 'val_loss': 16.24413, 'test_loss': 17.362019}}
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2024-10-14 12:19:57,740 (server:615) INFO: {'Role': 'Server #', 'Round': 5, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(21.787435), 'test_loss': np.float64(112946.064432), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(22.187252), 'val_loss': np.float64(115018.71539)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(21.787435), 'test_loss': np.float64(112946.064432), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(22.187252), 'val_loss': np.float64(115018.71539)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(3.049839), 'test_avg_loss_bottom_decile': np.float64(18.112504), 'test_avg_loss_top_decile': np.float64(26.68409), 'test_avg_loss_min': np.float64(16.294881), 'test_avg_loss_max': np.float64(26.68409), 'test_avg_loss_bottom10%': np.float64(16.294881), 'test_avg_loss_top10%': np.float64(26.68409), 'test_avg_loss_cos1': np.float64(0.990344), 'test_avg_loss_entropy': np.float64(2.292619), 'test_loss_std': np.float64(15810.363848), 'test_loss_bottom_decile': np.float64(93895.2229), 'test_loss_top_decile': np.float64(138330.322693), 'test_loss_min': np.float64(84472.66153), 'test_loss_max': np.float64(138330.322693), 'test_loss_bottom10%': np.float64(84472.66153), 'test_loss_top10%': np.float64(138330.322693), 'test_loss_cos1': np.float64(0.990344), 'test_loss_entropy': np.float64(2.292619), 'val_avg_loss_std': np.float64(3.328606), 'val_avg_loss_bottom_decile': np.float64(18.035973), 'val_avg_loss_top_decile': np.float64(26.928449), 'val_avg_loss_min': np.float64(16.369728), 'val_avg_loss_max': np.float64(26.928449), 'val_avg_loss_bottom10%': np.float64(16.369728), 'val_avg_loss_top10%': np.float64(26.928449), 'val_avg_loss_cos1': np.float64(0.988933), 'val_avg_loss_entropy': np.float64(2.291079), 'val_loss_std': np.float64(17255.494158), 'val_loss_bottom_decile': np.float64(93498.485657), 'val_loss_top_decile': np.float64(139597.081238), 'val_loss_min': np.float64(84860.668396), 'val_loss_max': np.float64(139597.081238), 'val_loss_bottom10%': np.float64(84860.668396), 'val_loss_top10%': np.float64(139597.081238), 'val_loss_cos1': np.float64(0.988933), 'val_loss_entropy': np.float64(2.291079)}}
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2024-10-14 12:19:57,773 (server:353) INFO: Server: Starting evaluation at the end of round 6.
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2024-10-14 12:19:57,774 (server:359) INFO: ----------- Starting a new training round (Round #7) -------------
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2024-10-14 12:22:13,406 (client:354) INFO: {'Role': 'Client #8', 'Round': 7, 'Results_raw': {'train_loss': 14.509627, 'val_loss': 13.79046, 'test_loss': 14.443368}}
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2024-10-14 12:23:03,927 (client:354) INFO: {'Role': 'Client #4', 'Round': 7, 'Results_raw': {'train_loss': 16.557224, 'val_loss': 15.940103, 'test_loss': 16.800075}}
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2024-10-14 12:23:54,938 (client:354) INFO: {'Role': 'Client #6', 'Round': 7, 'Results_raw': {'train_loss': 16.542167, 'val_loss': 15.588037, 'test_loss': 16.447981}}
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2024-10-14 12:24:46,708 (client:354) INFO: {'Role': 'Client #10', 'Round': 7, 'Results_raw': {'train_loss': 16.411046, 'val_loss': 16.243851, 'test_loss': 17.45374}}
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2024-10-14 12:25:37,730 (client:354) INFO: {'Role': 'Client #9', 'Round': 7, 'Results_raw': {'train_loss': 19.300588, 'val_loss': 18.739281, 'test_loss': 19.252348}}
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2024-10-14 12:26:37,663 (client:354) INFO: {'Role': 'Client #7', 'Round': 7, 'Results_raw': {'train_loss': 16.725379, 'val_loss': 16.215898, 'test_loss': 16.483932}}
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2024-10-14 12:27:37,051 (client:354) INFO: {'Role': 'Client #1', 'Round': 7, 'Results_raw': {'train_loss': 11.869506, 'val_loss': 11.135758, 'test_loss': 12.097588}}
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2024-10-14 12:28:35,297 (client:354) INFO: {'Role': 'Client #3', 'Round': 7, 'Results_raw': {'train_loss': 11.224908, 'val_loss': 11.172438, 'test_loss': 12.104944}}
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2024-10-14 12:29:31,276 (client:354) INFO: {'Role': 'Client #5', 'Round': 7, 'Results_raw': {'train_loss': 17.340077, 'val_loss': 17.70129, 'test_loss': 19.797142}}
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2024-10-14 12:30:32,139 (client:354) INFO: {'Role': 'Client #2', 'Round': 7, 'Results_raw': {'train_loss': 9.739506, 'val_loss': 9.048946, 'test_loss': 9.358611}}
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2024-10-14 12:30:32,150 (server:615) INFO: {'Role': 'Server #', 'Round': 6, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(21.400536), 'test_loss': np.float64(110940.376541), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(21.75586), 'val_loss': np.float64(112782.37825)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(21.400536), 'test_loss': np.float64(110940.376541), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(21.75586), 'val_loss': np.float64(112782.37825)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.93126), 'test_avg_loss_bottom_decile': np.float64(17.904208), 'test_avg_loss_top_decile': np.float64(26.151913), 'test_avg_loss_min': np.float64(16.153239), 'test_avg_loss_max': np.float64(26.151913), 'test_avg_loss_bottom10%': np.float64(16.153239), 'test_avg_loss_top10%': np.float64(26.151913), 'test_avg_loss_cos1': np.float64(0.990749), 'test_avg_loss_entropy': np.float64(2.29306), 'test_loss_std': np.float64(15195.65087), 'test_loss_bottom_decile': np.float64(92815.414856), 'test_loss_top_decile': np.float64(135571.515869), 'test_loss_min': np.float64(83738.389374), 'test_loss_max': np.float64(135571.515869), 'test_loss_bottom10%': np.float64(83738.389374), 'test_loss_top10%': np.float64(135571.515869), 'test_loss_cos1': np.float64(0.990749), 'test_loss_entropy': np.float64(2.29306), 'val_avg_loss_std': np.float64(3.203649), 'val_avg_loss_bottom_decile': np.float64(17.77642), 'val_avg_loss_top_decile': np.float64(26.346766), 'val_avg_loss_min': np.float64(16.185125), 'val_avg_loss_max': np.float64(26.346766), 'val_avg_loss_bottom10%': np.float64(16.185125), 'val_avg_loss_top10%': np.float64(26.346766), 'val_avg_loss_cos1': np.float64(0.989331), 'val_avg_loss_entropy': np.float64(2.291517), 'val_loss_std': np.float64(16607.716211), 'val_loss_bottom_decile': np.float64(92152.95929), 'val_loss_top_decile': np.float64(136581.634644), 'val_loss_min': np.float64(83903.688934), 'val_loss_max': np.float64(136581.634644), 'val_loss_bottom10%': np.float64(83903.688934), 'val_loss_top10%': np.float64(136581.634644), 'val_loss_cos1': np.float64(0.989331), 'val_loss_entropy': np.float64(2.291517)}}
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2024-10-14 12:30:32,188 (server:353) INFO: Server: Starting evaluation at the end of round 7.
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2024-10-14 12:30:32,189 (server:359) INFO: ----------- Starting a new training round (Round #8) -------------
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2024-10-14 12:33:03,349 (client:354) INFO: {'Role': 'Client #6', 'Round': 8, 'Results_raw': {'train_loss': 16.349273, 'val_loss': 15.462745, 'test_loss': 16.265038}}
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2024-10-14 12:34:00,685 (client:354) INFO: {'Role': 'Client #7', 'Round': 8, 'Results_raw': {'train_loss': 16.607144, 'val_loss': 16.03076, 'test_loss': 16.283068}}
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2024-10-14 12:34:57,626 (client:354) INFO: {'Role': 'Client #2', 'Round': 8, 'Results_raw': {'train_loss': 9.690446, 'val_loss': 9.015858, 'test_loss': 9.454853}}
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2024-10-14 12:35:57,798 (client:354) INFO: {'Role': 'Client #4', 'Round': 8, 'Results_raw': {'train_loss': 16.399583, 'val_loss': 15.695371, 'test_loss': 16.635523}}
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2024-10-14 12:36:52,892 (client:354) INFO: {'Role': 'Client #1', 'Round': 8, 'Results_raw': {'train_loss': 11.731357, 'val_loss': 11.099666, 'test_loss': 11.861982}}
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2024-10-14 12:37:49,944 (client:354) INFO: {'Role': 'Client #8', 'Round': 8, 'Results_raw': {'train_loss': 14.451314, 'val_loss': 13.746618, 'test_loss': 14.326483}}
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2024-10-14 12:39:02,746 (client:354) INFO: {'Role': 'Client #5', 'Round': 8, 'Results_raw': {'train_loss': 17.288019, 'val_loss': 17.325558, 'test_loss': 19.222748}}
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2024-10-14 12:40:00,634 (client:354) INFO: {'Role': 'Client #3', 'Round': 8, 'Results_raw': {'train_loss': 11.319572, 'val_loss': 11.273166, 'test_loss': 12.307289}}
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2024-10-14 12:41:00,308 (client:354) INFO: {'Role': 'Client #10', 'Round': 8, 'Results_raw': {'train_loss': 16.22931, 'val_loss': 15.883432, 'test_loss': 17.181478}}
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2024-10-14 12:41:57,728 (client:354) INFO: {'Role': 'Client #9', 'Round': 8, 'Results_raw': {'train_loss': 19.185014, 'val_loss': 18.463817, 'test_loss': 18.825732}}
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2024-10-14 12:41:57,731 (server:615) INFO: {'Role': 'Server #', 'Round': 7, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(21.075709), 'test_loss': np.float64(109256.473624), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(21.337367), 'val_loss': np.float64(110612.911411)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(21.075709), 'test_loss': np.float64(109256.473624), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(21.337367), 'val_loss': np.float64(110612.911411)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.746701), 'test_avg_loss_bottom_decile': np.float64(17.858959), 'test_avg_loss_top_decile': np.float64(25.669693), 'test_avg_loss_min': np.float64(16.311542), 'test_avg_loss_max': np.float64(25.669693), 'test_avg_loss_bottom10%': np.float64(16.311542), 'test_avg_loss_top10%': np.float64(25.669693), 'test_avg_loss_cos1': np.float64(0.991614), 'test_avg_loss_entropy': np.float64(2.294008), 'test_loss_std': np.float64(14238.900175), 'test_loss_bottom_decile': np.float64(92580.843353), 'test_loss_top_decile': np.float64(133071.688171), 'test_loss_min': np.float64(84559.034515), 'test_loss_max': np.float64(133071.688171), 'test_loss_bottom10%': np.float64(84559.034515), 'test_loss_top10%': np.float64(133071.688171), 'test_loss_cos1': np.float64(0.991614), 'test_loss_entropy': np.float64(2.294008), 'val_avg_loss_std': np.float64(2.973503), 'val_avg_loss_bottom_decile': np.float64(17.666991), 'val_avg_loss_top_decile': np.float64(25.745855), 'val_avg_loss_min': np.float64(16.332254), 'val_avg_loss_max': np.float64(25.745855), 'val_avg_loss_bottom10%': np.float64(16.332254), 'val_avg_loss_top10%': np.float64(25.745855), 'val_avg_loss_cos1': np.float64(0.990429), 'val_avg_loss_entropy': np.float64(2.292727), 'val_loss_std': np.float64(15414.637199), 'val_loss_bottom_decile': np.float64(91585.682037), 'val_loss_top_decile': np.float64(133466.512451), 'val_loss_min': np.float64(84666.402466), 'val_loss_max': np.float64(133466.512451), 'val_loss_bottom10%': np.float64(84666.402466), 'val_loss_top10%': np.float64(133466.512451), 'val_loss_cos1': np.float64(0.990429), 'val_loss_entropy': np.float64(2.292727)}}
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2024-10-14 12:41:57,773 (server:353) INFO: Server: Starting evaluation at the end of round 8.
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2024-10-14 12:41:57,774 (server:359) INFO: ----------- Starting a new training round (Round #9) -------------
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2024-10-14 12:44:28,511 (client:354) INFO: {'Role': 'Client #2', 'Round': 9, 'Results_raw': {'train_loss': 9.593362, 'val_loss': 8.887284, 'test_loss': 9.277928}}
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2024-10-14 12:45:21,928 (client:354) INFO: {'Role': 'Client #9', 'Round': 9, 'Results_raw': {'train_loss': 19.004733, 'val_loss': 18.364938, 'test_loss': 19.055199}}
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2024-10-14 12:46:24,581 (client:354) INFO: {'Role': 'Client #8', 'Round': 9, 'Results_raw': {'train_loss': 14.240292, 'val_loss': 13.804693, 'test_loss': 14.176786}}
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2024-10-14 12:47:22,285 (client:354) INFO: {'Role': 'Client #5', 'Round': 9, 'Results_raw': {'train_loss': 17.059978, 'val_loss': 17.329654, 'test_loss': 19.553608}}
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2024-10-14 12:48:22,335 (client:354) INFO: {'Role': 'Client #1', 'Round': 9, 'Results_raw': {'train_loss': 11.750668, 'val_loss': 11.186512, 'test_loss': 12.119553}}
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2024-10-14 12:49:19,209 (client:354) INFO: {'Role': 'Client #4', 'Round': 9, 'Results_raw': {'train_loss': 16.212142, 'val_loss': 15.739622, 'test_loss': 16.331369}}
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2024-10-14 12:50:16,186 (client:354) INFO: {'Role': 'Client #3', 'Round': 9, 'Results_raw': {'train_loss': 11.013257, 'val_loss': 10.920479, 'test_loss': 11.849431}}
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2024-10-14 12:51:19,032 (client:354) INFO: {'Role': 'Client #6', 'Round': 9, 'Results_raw': {'train_loss': 16.263958, 'val_loss': 15.522182, 'test_loss': 16.251822}}
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2024-10-14 12:52:16,449 (client:354) INFO: {'Role': 'Client #10', 'Round': 9, 'Results_raw': {'train_loss': 16.08479, 'val_loss': 15.715125, 'test_loss': 17.089584}}
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2024-10-14 12:53:15,239 (client:354) INFO: {'Role': 'Client #7', 'Round': 9, 'Results_raw': {'train_loss': 16.442193, 'val_loss': 15.936117, 'test_loss': 16.239464}}
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2024-10-14 12:53:15,244 (server:615) INFO: {'Role': 'Server #', 'Round': 8, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.990563), 'test_loss': np.float64(108815.078989), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(21.270421), 'val_loss': np.float64(110265.862775)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.990563), 'test_loss': np.float64(108815.078989), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(21.270421), 'val_loss': np.float64(110265.862775)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.782182), 'test_avg_loss_bottom_decile': np.float64(17.553871), 'test_avg_loss_top_decile': np.float64(25.624056), 'test_avg_loss_min': np.float64(16.345199), 'test_avg_loss_max': np.float64(25.624056), 'test_avg_loss_bottom10%': np.float64(16.345199), 'test_avg_loss_top10%': np.float64(25.624056), 'test_avg_loss_cos1': np.float64(0.99133), 'test_avg_loss_entropy': np.float64(2.29372), 'test_loss_std': np.float64(14422.833116), 'test_loss_bottom_decile': np.float64(90999.26828), 'test_loss_top_decile': np.float64(132835.105286), 'test_loss_min': np.float64(84733.511169), 'test_loss_max': np.float64(132835.105286), 'test_loss_bottom10%': np.float64(84733.511169), 'test_loss_top10%': np.float64(132835.105286), 'test_loss_cos1': np.float64(0.99133), 'test_loss_entropy': np.float64(2.29372), 'val_avg_loss_std': np.float64(3.047459), 'val_avg_loss_bottom_decile': np.float64(17.319697), 'val_avg_loss_top_decile': np.float64(25.730558), 'val_avg_loss_min': np.float64(16.326724), 'val_avg_loss_max': np.float64(25.730558), 'val_avg_loss_bottom10%': np.float64(16.326724), 'val_avg_loss_top10%': np.float64(25.730558), 'val_avg_loss_cos1': np.float64(0.989892), 'val_avg_loss_entropy': np.float64(2.292163), 'val_loss_std': np.float64(15798.028449), 'val_loss_bottom_decile': np.float64(89785.307861), 'val_loss_top_decile': np.float64(133387.214417), 'val_loss_min': np.float64(84637.737793), 'val_loss_max': np.float64(133387.214417), 'val_loss_bottom10%': np.float64(84637.737793), 'val_loss_top10%': np.float64(133387.214417), 'val_loss_cos1': np.float64(0.989892), 'val_loss_entropy': np.float64(2.292163)}}
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2024-10-14 12:53:15,285 (server:353) INFO: Server: Starting evaluation at the end of round 9.
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2024-10-14 12:53:15,286 (server:359) INFO: ----------- Starting a new training round (Round #10) -------------
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2024-10-14 12:56:07,558 (client:354) INFO: {'Role': 'Client #6', 'Round': 10, 'Results_raw': {'train_loss': 16.086095, 'val_loss': 15.216238, 'test_loss': 16.067656}}
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2024-10-14 12:57:15,798 (client:354) INFO: {'Role': 'Client #10', 'Round': 10, 'Results_raw': {'train_loss': 15.967696, 'val_loss': 15.911184, 'test_loss': 17.467054}}
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2024-10-14 12:58:11,554 (client:354) INFO: {'Role': 'Client #4', 'Round': 10, 'Results_raw': {'train_loss': 16.072892, 'val_loss': 15.797774, 'test_loss': 16.363331}}
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2024-10-14 12:59:11,968 (client:354) INFO: {'Role': 'Client #9', 'Round': 10, 'Results_raw': {'train_loss': 18.87681, 'val_loss': 18.483876, 'test_loss': 18.805834}}
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2024-10-14 13:00:11,393 (client:354) INFO: {'Role': 'Client #3', 'Round': 10, 'Results_raw': {'train_loss': 11.06458, 'val_loss': 10.980382, 'test_loss': 12.022881}}
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2024-10-14 13:01:11,452 (client:354) INFO: {'Role': 'Client #1', 'Round': 10, 'Results_raw': {'train_loss': 11.608075, 'val_loss': 10.967186, 'test_loss': 11.776392}}
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2024-10-14 13:02:12,502 (client:354) INFO: {'Role': 'Client #5', 'Round': 10, 'Results_raw': {'train_loss': 17.052129, 'val_loss': 16.977619, 'test_loss': 18.934585}}
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2024-10-14 13:03:10,409 (client:354) INFO: {'Role': 'Client #8', 'Round': 10, 'Results_raw': {'train_loss': 14.174242, 'val_loss': 13.642572, 'test_loss': 14.27612}}
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2024-10-14 13:04:10,430 (client:354) INFO: {'Role': 'Client #2', 'Round': 10, 'Results_raw': {'train_loss': 9.511295, 'val_loss': 8.932953, 'test_loss': 9.313782}}
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2024-10-14 13:05:14,119 (client:354) INFO: {'Role': 'Client #7', 'Round': 10, 'Results_raw': {'train_loss': 16.303456, 'val_loss': 16.056965, 'test_loss': 16.720749}}
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2024-10-14 13:05:14,122 (server:615) INFO: {'Role': 'Server #', 'Round': 9, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.943583), 'test_loss': np.float64(108571.53176), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(21.194076), 'val_loss': np.float64(109870.088425)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.943583), 'test_loss': np.float64(108571.53176), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(21.194076), 'val_loss': np.float64(109870.088425)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.600435), 'test_avg_loss_bottom_decile': np.float64(17.639215), 'test_avg_loss_top_decile': np.float64(25.45416), 'test_avg_loss_min': np.float64(16.771906), 'test_avg_loss_max': np.float64(25.45416), 'test_avg_loss_bottom10%': np.float64(16.771906), 'test_avg_loss_top10%': np.float64(25.45416), 'test_avg_loss_cos1': np.float64(0.99238), 'test_avg_loss_entropy': np.float64(2.294846), 'test_loss_std': np.float64(13480.656352), 'test_loss_bottom_decile': np.float64(91441.692932), 'test_loss_top_decile': np.float64(131954.365784), 'test_loss_min': np.float64(86945.561035), 'test_loss_max': np.float64(131954.365784), 'test_loss_bottom10%': np.float64(86945.561035), 'test_loss_top10%': np.float64(131954.365784), 'test_loss_cos1': np.float64(0.99238), 'test_loss_entropy': np.float64(2.294846), 'val_avg_loss_std': np.float64(2.850933), 'val_avg_loss_bottom_decile': np.float64(17.371621), 'val_avg_loss_top_decile': np.float64(25.538555), 'val_avg_loss_min': np.float64(16.762747), 'val_avg_loss_max': np.float64(25.538555), 'val_avg_loss_bottom10%': np.float64(16.762747), 'val_avg_loss_top10%': np.float64(25.538555), 'val_avg_loss_cos1': np.float64(0.991074), 'val_avg_loss_entropy': np.float64(2.293443), 'val_loss_std': np.float64(14779.238834), 'val_loss_bottom_decile': np.float64(90054.485504), 'val_loss_top_decile': np.float64(132391.867065), 'val_loss_min': np.float64(86898.080322), 'val_loss_max': np.float64(132391.867065), 'val_loss_bottom10%': np.float64(86898.080322), 'val_loss_top10%': np.float64(132391.867065), 'val_loss_cos1': np.float64(0.991074), 'val_loss_entropy': np.float64(2.293443)}}
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2024-10-14 13:05:14,165 (server:353) INFO: Server: Starting evaluation at the end of round 10.
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2024-10-14 13:05:14,166 (server:359) INFO: ----------- Starting a new training round (Round #11) -------------
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2024-10-14 13:07:51,213 (client:354) INFO: {'Role': 'Client #4', 'Round': 11, 'Results_raw': {'train_loss': 16.022715, 'val_loss': 16.262089, 'test_loss': 17.406524}}
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2024-10-14 13:08:50,359 (client:354) INFO: {'Role': 'Client #9', 'Round': 11, 'Results_raw': {'train_loss': 18.80627, 'val_loss': 18.203848, 'test_loss': 18.882411}}
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2024-10-14 13:09:51,769 (client:354) INFO: {'Role': 'Client #7', 'Round': 11, 'Results_raw': {'train_loss': 16.185782, 'val_loss': 15.897428, 'test_loss': 16.231334}}
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2024-10-14 13:10:52,786 (client:354) INFO: {'Role': 'Client #5', 'Round': 11, 'Results_raw': {'train_loss': 16.89311, 'val_loss': 17.110882, 'test_loss': 19.039096}}
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2024-10-14 13:11:52,956 (client:354) INFO: {'Role': 'Client #8', 'Round': 11, 'Results_raw': {'train_loss': 14.049243, 'val_loss': 13.636532, 'test_loss': 14.280225}}
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2024-10-14 13:12:52,583 (client:354) INFO: {'Role': 'Client #10', 'Round': 11, 'Results_raw': {'train_loss': 15.930903, 'val_loss': 15.649014, 'test_loss': 16.863396}}
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2024-10-14 13:13:52,726 (client:354) INFO: {'Role': 'Client #1', 'Round': 11, 'Results_raw': {'train_loss': 11.489687, 'val_loss': 10.809614, 'test_loss': 11.66897}}
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2024-10-14 13:14:51,024 (client:354) INFO: {'Role': 'Client #6', 'Round': 11, 'Results_raw': {'train_loss': 16.05724, 'val_loss': 15.2756, 'test_loss': 16.114218}}
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2024-10-14 13:15:50,451 (client:354) INFO: {'Role': 'Client #3', 'Round': 11, 'Results_raw': {'train_loss': 10.860882, 'val_loss': 11.088479, 'test_loss': 12.11937}}
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2024-10-14 13:16:47,597 (client:354) INFO: {'Role': 'Client #2', 'Round': 11, 'Results_raw': {'train_loss': 9.4271, 'val_loss': 8.9567, 'test_loss': 9.521965}}
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2024-10-14 13:16:47,602 (server:615) INFO: {'Role': 'Server #', 'Round': 10, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.73), 'test_loss': np.float64(107464.320715), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.994564), 'val_loss': np.float64(108835.819934)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.73), 'test_loss': np.float64(107464.320715), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.994564), 'val_loss': np.float64(108835.819934)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.654238), 'test_avg_loss_bottom_decile': np.float64(17.227481), 'test_avg_loss_top_decile': np.float64(25.27341), 'test_avg_loss_min': np.float64(16.583764), 'test_avg_loss_max': np.float64(25.27341), 'test_avg_loss_bottom10%': np.float64(16.583764), 'test_avg_loss_top10%': np.float64(25.27341), 'test_avg_loss_cos1': np.float64(0.991903), 'test_avg_loss_entropy': np.float64(2.29435), 'test_loss_std': np.float64(13759.568139), 'test_loss_bottom_decile': np.float64(89307.263641), 'test_loss_top_decile': np.float64(131017.358521), 'test_loss_min': np.float64(85970.234131), 'test_loss_max': np.float64(131017.358521), 'test_loss_bottom10%': np.float64(85970.234131), 'test_loss_top10%': np.float64(131017.358521), 'test_loss_cos1': np.float64(0.991903), 'test_loss_entropy': np.float64(2.29435), 'val_avg_loss_std': np.float64(2.935705), 'val_avg_loss_bottom_decile': np.float64(16.935723), 'val_avg_loss_top_decile': np.float64(25.396108), 'val_avg_loss_min': np.float64(16.533804), 'val_avg_loss_max': np.float64(25.396108), 'val_avg_loss_bottom10%': np.float64(16.533804), 'val_avg_loss_top10%': np.float64(25.396108), 'val_avg_loss_cos1': np.float64(0.990365), 'val_avg_loss_entropy': np.float64(2.292693), 'val_loss_std': np.float64(15218.695678), 'val_loss_bottom_decile': np.float64(87794.790375), 'val_loss_top_decile': np.float64(131653.4245), 'val_loss_min': np.float64(85711.23877), 'val_loss_max': np.float64(131653.4245), 'val_loss_bottom10%': np.float64(85711.23877), 'val_loss_top10%': np.float64(131653.4245), 'val_loss_cos1': np.float64(0.990365), 'val_loss_entropy': np.float64(2.292693)}}
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2024-10-14 13:16:47,644 (server:353) INFO: Server: Starting evaluation at the end of round 11.
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2024-10-14 13:16:47,644 (server:359) INFO: ----------- Starting a new training round (Round #12) -------------
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2024-10-14 13:19:18,388 (client:354) INFO: {'Role': 'Client #9', 'Round': 12, 'Results_raw': {'train_loss': 18.660539, 'val_loss': 18.237549, 'test_loss': 18.909392}}
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2024-10-14 13:20:17,901 (client:354) INFO: {'Role': 'Client #2', 'Round': 12, 'Results_raw': {'train_loss': 9.394188, 'val_loss': 8.810556, 'test_loss': 9.3124}}
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2024-10-14 13:21:19,327 (client:354) INFO: {'Role': 'Client #10', 'Round': 12, 'Results_raw': {'train_loss': 15.798238, 'val_loss': 15.643806, 'test_loss': 17.089488}}
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2024-10-14 13:22:21,234 (client:354) INFO: {'Role': 'Client #7', 'Round': 12, 'Results_raw': {'train_loss': 16.133181, 'val_loss': 15.812669, 'test_loss': 16.487445}}
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2024-10-14 13:23:20,120 (client:354) INFO: {'Role': 'Client #3', 'Round': 12, 'Results_raw': {'train_loss': 10.893222, 'val_loss': 11.008491, 'test_loss': 12.020767}}
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2024-10-14 13:24:15,941 (client:354) INFO: {'Role': 'Client #5', 'Round': 12, 'Results_raw': {'train_loss': 16.836247, 'val_loss': 16.926333, 'test_loss': 18.887402}}
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2024-10-14 13:25:14,874 (client:354) INFO: {'Role': 'Client #1', 'Round': 12, 'Results_raw': {'train_loss': 11.525941, 'val_loss': 10.871942, 'test_loss': 11.763122}}
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2024-10-14 13:26:13,923 (client:354) INFO: {'Role': 'Client #6', 'Round': 12, 'Results_raw': {'train_loss': 15.927868, 'val_loss': 15.667576, 'test_loss': 16.939316}}
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2024-10-14 13:27:11,635 (client:354) INFO: {'Role': 'Client #4', 'Round': 12, 'Results_raw': {'train_loss': 15.962989, 'val_loss': 15.47305, 'test_loss': 16.285767}}
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2024-10-14 13:28:12,496 (client:354) INFO: {'Role': 'Client #8', 'Round': 12, 'Results_raw': {'train_loss': 13.988262, 'val_loss': 13.694223, 'test_loss': 14.445117}}
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2024-10-14 13:28:12,500 (server:615) INFO: {'Role': 'Server #', 'Round': 11, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.575073), 'test_loss': np.float64(106661.178287), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.802372), 'val_loss': np.float64(107839.495767)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.575073), 'test_loss': np.float64(106661.178287), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.802372), 'val_loss': np.float64(107839.495767)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.573766), 'test_avg_loss_bottom_decile': np.float64(17.219184), 'test_avg_loss_top_decile': np.float64(25.014879), 'test_avg_loss_min': np.float64(16.581619), 'test_avg_loss_max': np.float64(25.014879), 'test_avg_loss_bottom10%': np.float64(16.581619), 'test_avg_loss_top10%': np.float64(25.014879), 'test_avg_loss_cos1': np.float64(0.992267), 'test_avg_loss_entropy': np.float64(2.294736), 'test_loss_std': np.float64(13342.400987), 'test_loss_bottom_decile': np.float64(89264.24765), 'test_loss_top_decile': np.float64(129677.132446), 'test_loss_min': np.float64(85959.113464), 'test_loss_max': np.float64(129677.132446), 'test_loss_bottom10%': np.float64(85959.113464), 'test_loss_top10%': np.float64(129677.132446), 'test_loss_cos1': np.float64(0.992267), 'test_loss_entropy': np.float64(2.294736), 'val_avg_loss_std': np.float64(2.837219), 'val_avg_loss_bottom_decile': np.float64(16.890802), 'val_avg_loss_top_decile': np.float64(25.112788), 'val_avg_loss_min': np.float64(16.551252), 'val_avg_loss_max': np.float64(25.112788), 'val_avg_loss_bottom10%': np.float64(16.551252), 'val_avg_loss_top10%': np.float64(25.112788), 'val_avg_loss_cos1': np.float64(0.990827), 'val_avg_loss_entropy': np.float64(2.293191), 'val_loss_std': np.float64(14708.142741), 'val_loss_bottom_decile': np.float64(87561.919769), 'val_loss_top_decile': np.float64(130184.692505), 'val_loss_min': np.float64(85801.691742), 'val_loss_max': np.float64(130184.692505), 'val_loss_bottom10%': np.float64(85801.691742), 'val_loss_top10%': np.float64(130184.692505), 'val_loss_cos1': np.float64(0.990827), 'val_loss_entropy': np.float64(2.293191)}}
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2024-10-14 13:28:12,542 (server:353) INFO: Server: Starting evaluation at the end of round 12.
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2024-10-14 13:28:12,543 (server:359) INFO: ----------- Starting a new training round (Round #13) -------------
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2024-10-14 13:30:56,457 (client:354) INFO: {'Role': 'Client #10', 'Round': 13, 'Results_raw': {'train_loss': 15.80222, 'val_loss': 15.724791, 'test_loss': 17.036025}}
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2024-10-14 13:31:54,722 (client:354) INFO: {'Role': 'Client #5', 'Round': 13, 'Results_raw': {'train_loss': 16.738193, 'val_loss': 16.872009, 'test_loss': 18.862966}}
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2024-10-14 13:32:54,539 (client:354) INFO: {'Role': 'Client #7', 'Round': 13, 'Results_raw': {'train_loss': 16.066706, 'val_loss': 15.752619, 'test_loss': 16.360463}}
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2024-10-14 13:33:49,102 (client:354) INFO: {'Role': 'Client #8', 'Round': 13, 'Results_raw': {'train_loss': 13.914698, 'val_loss': 13.603885, 'test_loss': 14.112281}}
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2024-10-14 13:34:46,349 (client:354) INFO: {'Role': 'Client #1', 'Round': 13, 'Results_raw': {'train_loss': 11.3356, 'val_loss': 10.76611, 'test_loss': 11.634541}}
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2024-10-14 13:35:41,251 (client:354) INFO: {'Role': 'Client #9', 'Round': 13, 'Results_raw': {'train_loss': 18.651028, 'val_loss': 18.211713, 'test_loss': 18.702151}}
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2024-10-14 13:36:39,765 (client:354) INFO: {'Role': 'Client #6', 'Round': 13, 'Results_raw': {'train_loss': 15.863524, 'val_loss': 15.257102, 'test_loss': 16.050538}}
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2024-10-14 13:37:36,369 (client:354) INFO: {'Role': 'Client #3', 'Round': 13, 'Results_raw': {'train_loss': 10.715067, 'val_loss': 10.928481, 'test_loss': 12.020313}}
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2024-10-14 13:38:40,724 (client:354) INFO: {'Role': 'Client #2', 'Round': 13, 'Results_raw': {'train_loss': 9.240365, 'val_loss': 8.678799, 'test_loss': 9.123569}}
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2024-10-14 13:39:39,083 (client:354) INFO: {'Role': 'Client #4', 'Round': 13, 'Results_raw': {'train_loss': 15.842036, 'val_loss': 15.675868, 'test_loss': 16.433242}}
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2024-10-14 13:39:39,089 (server:615) INFO: {'Role': 'Server #', 'Round': 12, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.540374), 'test_loss': np.float64(106481.298044), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.740166), 'val_loss': np.float64(107517.020883)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.540374), 'test_loss': np.float64(106481.298044), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.740166), 'val_loss': np.float64(107517.020883)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.465462), 'test_avg_loss_bottom_decile': np.float64(17.194288), 'test_avg_loss_top_decile': np.float64(24.872026), 'test_avg_loss_min': np.float64(16.927388), 'test_avg_loss_max': np.float64(24.872026), 'test_avg_loss_bottom10%': np.float64(16.927388), 'test_avg_loss_top10%': np.float64(24.872026), 'test_avg_loss_cos1': np.float64(0.992873), 'test_avg_loss_entropy': np.float64(2.295378), 'test_loss_std': np.float64(12780.956836), 'test_loss_bottom_decile': np.float64(89135.186768), 'test_loss_top_decile': np.float64(128936.581665), 'test_loss_min': np.float64(87751.581055), 'test_loss_max': np.float64(128936.581665), 'test_loss_bottom10%': np.float64(87751.581055), 'test_loss_top10%': np.float64(128936.581665), 'test_loss_cos1': np.float64(0.992873), 'test_loss_entropy': np.float64(2.295378), 'val_avg_loss_std': np.float64(2.725073), 'val_avg_loss_bottom_decile': np.float64(16.876044), 'val_avg_loss_top_decile': np.float64(24.924098), 'val_avg_loss_min': np.float64(16.833657), 'val_avg_loss_max': np.float64(24.924098), 'val_avg_loss_bottom10%': np.float64(16.833657), 'val_avg_loss_top10%': np.float64(24.924098), 'val_avg_loss_cos1': np.float64(0.991478), 'val_avg_loss_entropy': np.float64(2.293887), 'val_loss_std': np.float64(14126.776384), 'val_loss_bottom_decile': np.float64(87485.410645), 'val_loss_top_decile': np.float64(129206.526306), 'val_loss_min': np.float64(87265.677979), 'val_loss_max': np.float64(129206.526306), 'val_loss_bottom10%': np.float64(87265.677979), 'val_loss_top10%': np.float64(129206.526306), 'val_loss_cos1': np.float64(0.991478), 'val_loss_entropy': np.float64(2.293887)}}
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2024-10-14 13:39:39,139 (server:353) INFO: Server: Starting evaluation at the end of round 13.
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2024-10-14 13:39:39,140 (server:359) INFO: ----------- Starting a new training round (Round #14) -------------
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2024-10-14 13:42:08,944 (client:354) INFO: {'Role': 'Client #9', 'Round': 14, 'Results_raw': {'train_loss': 18.593789, 'val_loss': 17.959562, 'test_loss': 18.439096}}
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2024-10-14 13:43:16,539 (client:354) INFO: {'Role': 'Client #4', 'Round': 14, 'Results_raw': {'train_loss': 15.818813, 'val_loss': 15.374845, 'test_loss': 16.131208}}
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2024-10-14 13:44:11,257 (client:354) INFO: {'Role': 'Client #8', 'Round': 14, 'Results_raw': {'train_loss': 13.86811, 'val_loss': 13.649464, 'test_loss': 14.371397}}
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2024-10-14 13:45:09,155 (client:354) INFO: {'Role': 'Client #6', 'Round': 14, 'Results_raw': {'train_loss': 15.826594, 'val_loss': 15.485944, 'test_loss': 16.976435}}
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2024-10-14 13:46:07,418 (client:354) INFO: {'Role': 'Client #7', 'Round': 14, 'Results_raw': {'train_loss': 15.959053, 'val_loss': 15.76151, 'test_loss': 16.17935}}
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2024-10-14 13:47:09,906 (client:354) INFO: {'Role': 'Client #5', 'Round': 14, 'Results_raw': {'train_loss': 16.685638, 'val_loss': 17.049318, 'test_loss': 18.924243}}
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2024-10-14 13:48:07,226 (client:354) INFO: {'Role': 'Client #3', 'Round': 14, 'Results_raw': {'train_loss': 10.809355, 'val_loss': 10.782227, 'test_loss': 11.921685}}
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2024-10-14 13:49:08,403 (client:354) INFO: {'Role': 'Client #10', 'Round': 14, 'Results_raw': {'train_loss': 15.655225, 'val_loss': 15.504953, 'test_loss': 16.778176}}
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2024-10-14 13:50:06,492 (client:354) INFO: {'Role': 'Client #1', 'Round': 14, 'Results_raw': {'train_loss': 11.317039, 'val_loss': 10.776219, 'test_loss': 11.677135}}
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2024-10-14 13:51:06,344 (client:354) INFO: {'Role': 'Client #2', 'Round': 14, 'Results_raw': {'train_loss': 9.232436, 'val_loss': 8.720211, 'test_loss': 9.259988}}
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2024-10-14 13:51:06,349 (server:615) INFO: {'Role': 'Server #', 'Round': 13, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.617782), 'test_loss': np.float64(106882.581406), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.846551), 'val_loss': np.float64(108068.518036)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.617782), 'test_loss': np.float64(106882.581406), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.846551), 'val_loss': np.float64(108068.518036)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.49937), 'test_avg_loss_bottom_decile': np.float64(17.132883), 'test_avg_loss_top_decile': np.float64(24.959446), 'test_avg_loss_min': np.float64(16.938554), 'test_avg_loss_max': np.float64(24.959446), 'test_avg_loss_bottom10%': np.float64(16.938554), 'test_avg_loss_top10%': np.float64(24.959446), 'test_avg_loss_cos1': np.float64(0.992732), 'test_avg_loss_entropy': np.float64(2.295224), 'test_loss_std': np.float64(12956.734797), 'test_loss_bottom_decile': np.float64(88816.864258), 'test_loss_top_decile': np.float64(129389.768127), 'test_loss_min': np.float64(87809.465942), 'test_loss_max': np.float64(129389.768127), 'test_loss_bottom10%': np.float64(87809.465942), 'test_loss_top10%': np.float64(129389.768127), 'test_loss_cos1': np.float64(0.992732), 'test_loss_entropy': np.float64(2.295224), 'val_avg_loss_std': np.float64(2.781524), 'val_avg_loss_bottom_decile': np.float64(16.854158), 'val_avg_loss_top_decile': np.float64(25.059917), 'val_avg_loss_min': np.float64(16.791752), 'val_avg_loss_max': np.float64(25.059917), 'val_avg_loss_bottom10%': np.float64(16.791752), 'val_avg_loss_top10%': np.float64(25.059917), 'val_avg_loss_cos1': np.float64(0.991216), 'val_avg_loss_entropy': np.float64(2.293599), 'val_loss_std': np.float64(14419.422541), 'val_loss_bottom_decile': np.float64(87371.953735), 'val_loss_top_decile': np.float64(129910.611023), 'val_loss_min': np.float64(87048.442719), 'val_loss_max': np.float64(129910.611023), 'val_loss_bottom10%': np.float64(87048.442719), 'val_loss_top10%': np.float64(129910.611023), 'val_loss_cos1': np.float64(0.991216), 'val_loss_entropy': np.float64(2.293599)}}
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2024-10-14 13:51:06,394 (server:353) INFO: Server: Starting evaluation at the end of round 14.
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2024-10-14 13:51:06,395 (server:359) INFO: ----------- Starting a new training round (Round #15) -------------
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2024-10-14 13:53:37,467 (client:354) INFO: {'Role': 'Client #7', 'Round': 15, 'Results_raw': {'train_loss': 15.966863, 'val_loss': 15.736892, 'test_loss': 16.25601}}
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2024-10-14 13:54:38,325 (client:354) INFO: {'Role': 'Client #3', 'Round': 15, 'Results_raw': {'train_loss': 10.76099, 'val_loss': 11.187115, 'test_loss': 12.48036}}
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2024-10-14 13:55:43,299 (client:354) INFO: {'Role': 'Client #4', 'Round': 15, 'Results_raw': {'train_loss': 15.76253, 'val_loss': 15.49818, 'test_loss': 16.167905}}
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2024-10-14 13:56:43,988 (client:354) INFO: {'Role': 'Client #5', 'Round': 15, 'Results_raw': {'train_loss': 16.652604, 'val_loss': 16.913387, 'test_loss': 18.956711}}
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2024-10-14 13:57:43,439 (client:354) INFO: {'Role': 'Client #9', 'Round': 15, 'Results_raw': {'train_loss': 18.482342, 'val_loss': 18.17685, 'test_loss': 18.82}}
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2024-10-14 13:58:41,219 (client:354) INFO: {'Role': 'Client #2', 'Round': 15, 'Results_raw': {'train_loss': 9.199241, 'val_loss': 8.784378, 'test_loss': 9.300295}}
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2024-10-14 13:59:39,679 (client:354) INFO: {'Role': 'Client #6', 'Round': 15, 'Results_raw': {'train_loss': 15.771556, 'val_loss': 15.175402, 'test_loss': 16.332124}}
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2024-10-14 14:00:37,678 (client:354) INFO: {'Role': 'Client #8', 'Round': 15, 'Results_raw': {'train_loss': 13.81202, 'val_loss': 13.569376, 'test_loss': 14.059495}}
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2024-10-14 14:01:35,680 (client:354) INFO: {'Role': 'Client #10', 'Round': 15, 'Results_raw': {'train_loss': 15.652365, 'val_loss': 15.582392, 'test_loss': 17.062219}}
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2024-10-14 14:02:36,805 (client:354) INFO: {'Role': 'Client #1', 'Round': 15, 'Results_raw': {'train_loss': 11.306714, 'val_loss': 10.827182, 'test_loss': 11.804868}}
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2024-10-14 14:02:36,810 (server:615) INFO: {'Role': 'Server #', 'Round': 14, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.480429), 'test_loss': np.float64(106170.543311), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.699611), 'val_loss': np.float64(107306.784946)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.480429), 'test_loss': np.float64(106170.543311), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.699611), 'val_loss': np.float64(107306.784946)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.402156), 'test_avg_loss_bottom_decile': np.float64(17.059152), 'test_avg_loss_top_decile': np.float64(24.700232), 'test_avg_loss_min': np.float64(17.047914), 'test_avg_loss_max': np.float64(24.700232), 'test_avg_loss_bottom10%': np.float64(17.047914), 'test_avg_loss_top10%': np.float64(24.700232), 'test_avg_loss_cos1': np.float64(0.993192), 'test_avg_loss_entropy': np.float64(2.295698), 'test_loss_std': np.float64(12452.774229), 'test_loss_bottom_decile': np.float64(88434.642242), 'test_loss_top_decile': np.float64(128046.00061), 'test_loss_min': np.float64(88376.384949), 'test_loss_max': np.float64(128046.00061), 'test_loss_bottom10%': np.float64(88376.384949), 'test_loss_top10%': np.float64(128046.00061), 'test_loss_cos1': np.float64(0.993192), 'test_loss_entropy': np.float64(2.295698), 'val_avg_loss_std': np.float64(2.685504), 'val_avg_loss_bottom_decile': np.float64(16.95445), 'val_avg_loss_top_decile': np.float64(24.797463), 'val_avg_loss_min': np.float64(16.696136), 'val_avg_loss_max': np.float64(24.797463), 'val_avg_loss_bottom10%': np.float64(16.696136), 'val_avg_loss_top10%': np.float64(24.797463), 'val_avg_loss_cos1': np.float64(0.991689), 'val_avg_loss_entropy': np.float64(2.294092), 'val_loss_std': np.float64(13921.654131), 'val_loss_bottom_decile': np.float64(87891.867065), 'val_loss_top_decile': np.float64(128550.046326), 'val_loss_min': np.float64(86552.768921), 'val_loss_max': np.float64(128550.046326), 'val_loss_bottom10%': np.float64(86552.768921), 'val_loss_top10%': np.float64(128550.046326), 'val_loss_cos1': np.float64(0.991689), 'val_loss_entropy': np.float64(2.294092)}}
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2024-10-14 14:02:36,855 (server:353) INFO: Server: Starting evaluation at the end of round 15.
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2024-10-14 14:02:36,856 (server:359) INFO: ----------- Starting a new training round (Round #16) -------------
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2024-10-14 14:05:09,738 (client:354) INFO: {'Role': 'Client #2', 'Round': 16, 'Results_raw': {'train_loss': 9.192938, 'val_loss': 8.637235, 'test_loss': 9.212269}}
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2024-10-14 14:06:09,995 (client:354) INFO: {'Role': 'Client #9', 'Round': 16, 'Results_raw': {'train_loss': 18.448281, 'val_loss': 18.043613, 'test_loss': 18.549547}}
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2024-10-14 14:07:08,849 (client:354) INFO: {'Role': 'Client #5', 'Round': 16, 'Results_raw': {'train_loss': 16.592473, 'val_loss': 16.824701, 'test_loss': 18.559261}}
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2024-10-14 14:08:09,634 (client:354) INFO: {'Role': 'Client #8', 'Round': 16, 'Results_raw': {'train_loss': 13.774712, 'val_loss': 13.401044, 'test_loss': 14.093895}}
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2024-10-14 14:09:11,302 (client:354) INFO: {'Role': 'Client #6', 'Round': 16, 'Results_raw': {'train_loss': 15.701878, 'val_loss': 15.193139, 'test_loss': 16.274641}}
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2024-10-14 14:10:08,609 (client:354) INFO: {'Role': 'Client #10', 'Round': 16, 'Results_raw': {'train_loss': 15.556944, 'val_loss': 15.701773, 'test_loss': 17.114206}}
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2024-10-14 14:11:08,891 (client:354) INFO: {'Role': 'Client #4', 'Round': 16, 'Results_raw': {'train_loss': 15.739596, 'val_loss': 15.411424, 'test_loss': 16.476525}}
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2024-10-14 14:12:05,969 (client:354) INFO: {'Role': 'Client #3', 'Round': 16, 'Results_raw': {'train_loss': 10.708818, 'val_loss': 10.731947, 'test_loss': 11.932496}}
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2024-10-14 14:13:04,194 (client:354) INFO: {'Role': 'Client #1', 'Round': 16, 'Results_raw': {'train_loss': 11.233451, 'val_loss': 10.791336, 'test_loss': 11.791966}}
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2024-10-14 14:14:04,122 (client:354) INFO: {'Role': 'Client #7', 'Round': 16, 'Results_raw': {'train_loss': 15.900244, 'val_loss': 15.760256, 'test_loss': 16.2261}}
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2024-10-14 14:14:04,126 (server:615) INFO: {'Role': 'Server #', 'Round': 15, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.385795), 'test_loss': np.float64(105679.959836), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.578767), 'val_loss': np.float64(106680.326202)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.385795), 'test_loss': np.float64(105679.959836), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.578767), 'val_loss': np.float64(106680.326202)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.346309), 'test_avg_loss_bottom_decile': np.float64(17.181718), 'test_avg_loss_top_decile': np.float64(24.539879), 'test_avg_loss_min': np.float64(16.928383), 'test_avg_loss_max': np.float64(24.539879), 'test_avg_loss_bottom10%': np.float64(16.928383), 'test_avg_loss_top10%': np.float64(24.539879), 'test_avg_loss_cos1': np.float64(0.993442), 'test_avg_loss_entropy': np.float64(2.295965), 'test_loss_std': np.float64(12163.266134), 'test_loss_bottom_decile': np.float64(89070.027283), 'test_loss_top_decile': np.float64(127214.733032), 'test_loss_min': np.float64(87756.736481), 'test_loss_max': np.float64(127214.733032), 'test_loss_bottom10%': np.float64(87756.736481), 'test_loss_top10%': np.float64(127214.733032), 'test_loss_cos1': np.float64(0.993442), 'test_loss_entropy': np.float64(2.295965), 'val_avg_loss_std': np.float64(2.623028), 'val_avg_loss_bottom_decile': np.float64(17.062852), 'val_avg_loss_top_decile': np.float64(24.604309), 'val_avg_loss_min': np.float64(16.545671), 'val_avg_loss_max': np.float64(24.604309), 'val_avg_loss_bottom10%': np.float64(16.545671), 'val_avg_loss_top10%': np.float64(24.604309), 'val_avg_loss_cos1': np.float64(0.991974), 'val_avg_loss_entropy': np.float64(2.294401), 'val_loss_std': np.float64(13597.775846), 'val_loss_bottom_decile': np.float64(88453.823303), 'val_loss_top_decile': np.float64(127548.739563), 'val_loss_min': np.float64(85772.756012), 'val_loss_max': np.float64(127548.739563), 'val_loss_bottom10%': np.float64(85772.756012), 'val_loss_top10%': np.float64(127548.739563), 'val_loss_cos1': np.float64(0.991974), 'val_loss_entropy': np.float64(2.294401)}}
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2024-10-14 14:14:04,164 (server:353) INFO: Server: Starting evaluation at the end of round 16.
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2024-10-14 14:14:04,165 (server:359) INFO: ----------- Starting a new training round (Round #17) -------------
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2024-10-14 14:16:39,976 (client:354) INFO: {'Role': 'Client #10', 'Round': 17, 'Results_raw': {'train_loss': 15.495512, 'val_loss': 15.398009, 'test_loss': 16.56014}}
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2024-10-14 14:17:38,943 (client:354) INFO: {'Role': 'Client #5', 'Round': 17, 'Results_raw': {'train_loss': 16.549832, 'val_loss': 16.870148, 'test_loss': 18.792581}}
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2024-10-14 14:18:41,236 (client:354) INFO: {'Role': 'Client #3', 'Round': 17, 'Results_raw': {'train_loss': 10.618399, 'val_loss': 10.722219, 'test_loss': 11.957781}}
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2024-10-14 14:19:46,431 (client:354) INFO: {'Role': 'Client #7', 'Round': 17, 'Results_raw': {'train_loss': 15.85844, 'val_loss': 15.710617, 'test_loss': 16.127135}}
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2024-10-14 14:20:43,471 (client:354) INFO: {'Role': 'Client #2', 'Round': 17, 'Results_raw': {'train_loss': 9.041928, 'val_loss': 8.832848, 'test_loss': 9.134435}}
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2024-10-14 14:21:44,338 (client:354) INFO: {'Role': 'Client #4', 'Round': 17, 'Results_raw': {'train_loss': 15.629415, 'val_loss': 15.380492, 'test_loss': 16.106405}}
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2024-10-14 14:22:45,132 (client:354) INFO: {'Role': 'Client #8', 'Round': 17, 'Results_raw': {'train_loss': 13.754778, 'val_loss': 13.485495, 'test_loss': 13.955444}}
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2024-10-14 14:23:45,407 (client:354) INFO: {'Role': 'Client #9', 'Round': 17, 'Results_raw': {'train_loss': 18.439618, 'val_loss': 18.008255, 'test_loss': 18.73953}}
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2024-10-14 14:24:41,441 (client:354) INFO: {'Role': 'Client #6', 'Round': 17, 'Results_raw': {'train_loss': 15.637532, 'val_loss': 15.228197, 'test_loss': 16.51995}}
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2024-10-14 14:25:40,950 (client:354) INFO: {'Role': 'Client #1', 'Round': 17, 'Results_raw': {'train_loss': 11.166091, 'val_loss': 10.822746, 'test_loss': 11.70529}}
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2024-10-14 14:25:40,954 (server:615) INFO: {'Role': 'Server #', 'Round': 16, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.246264), 'test_loss': np.float64(104956.630573), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.422767), 'val_loss': np.float64(105871.625827)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.246264), 'test_loss': np.float64(104956.630573), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.422767), 'val_loss': np.float64(105871.625827)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.348822), 'test_avg_loss_bottom_decile': np.float64(17.043997), 'test_avg_loss_top_decile': np.float64(24.434879), 'test_avg_loss_min': np.float64(16.832642), 'test_avg_loss_max': np.float64(24.434879), 'test_avg_loss_bottom10%': np.float64(16.832642), 'test_avg_loss_top10%': np.float64(24.434879), 'test_avg_loss_cos1': np.float64(0.993338), 'test_avg_loss_entropy': np.float64(2.295861), 'test_loss_std': np.float64(12176.290851), 'test_loss_bottom_decile': np.float64(88356.08255), 'test_loss_top_decile': np.float64(126670.410217), 'test_loss_min': np.float64(87260.415771), 'test_loss_max': np.float64(126670.410217), 'test_loss_bottom10%': np.float64(87260.415771), 'test_loss_top10%': np.float64(126670.410217), 'test_loss_cos1': np.float64(0.993338), 'test_loss_entropy': np.float64(2.295861), 'val_avg_loss_std': np.float64(2.619857), 'val_avg_loss_bottom_decile': np.float64(16.917729), 'val_avg_loss_top_decile': np.float64(24.471192), 'val_avg_loss_min': np.float64(16.432741), 'val_avg_loss_max': np.float64(24.471192), 'val_avg_loss_bottom10%': np.float64(16.432741), 'val_avg_loss_top10%': np.float64(24.471192), 'val_avg_loss_cos1': np.float64(0.991872), 'val_avg_loss_entropy': np.float64(2.294297), 'val_loss_std': np.float64(13581.337462), 'val_loss_bottom_decile': np.float64(87701.507751), 'val_loss_top_decile': np.float64(126858.657532), 'val_loss_min': np.float64(85187.326965), 'val_loss_max': np.float64(126858.657532), 'val_loss_bottom10%': np.float64(85187.326965), 'val_loss_top10%': np.float64(126858.657532), 'val_loss_cos1': np.float64(0.991872), 'val_loss_entropy': np.float64(2.294297)}}
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2024-10-14 14:25:41,000 (server:353) INFO: Server: Starting evaluation at the end of round 17.
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2024-10-14 14:25:41,001 (server:359) INFO: ----------- Starting a new training round (Round #18) -------------
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2024-10-14 14:28:16,661 (client:354) INFO: {'Role': 'Client #3', 'Round': 18, 'Results_raw': {'train_loss': 10.575164, 'val_loss': 10.771904, 'test_loss': 12.055381}}
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2024-10-14 14:29:13,432 (client:354) INFO: {'Role': 'Client #5', 'Round': 18, 'Results_raw': {'train_loss': 16.509854, 'val_loss': 16.815505, 'test_loss': 18.789992}}
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2024-10-14 14:30:11,232 (client:354) INFO: {'Role': 'Client #7', 'Round': 18, 'Results_raw': {'train_loss': 15.796124, 'val_loss': 15.88919, 'test_loss': 16.639024}}
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2024-10-14 14:31:09,067 (client:354) INFO: {'Role': 'Client #9', 'Round': 18, 'Results_raw': {'train_loss': 18.337359, 'val_loss': 17.917217, 'test_loss': 18.452458}}
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2024-10-14 14:32:12,131 (client:354) INFO: {'Role': 'Client #10', 'Round': 18, 'Results_raw': {'train_loss': 15.465047, 'val_loss': 15.448837, 'test_loss': 16.785339}}
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2024-10-14 14:33:11,484 (client:354) INFO: {'Role': 'Client #1', 'Round': 18, 'Results_raw': {'train_loss': 11.217733, 'val_loss': 10.734883, 'test_loss': 11.672701}}
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2024-10-14 14:34:10,348 (client:354) INFO: {'Role': 'Client #4', 'Round': 18, 'Results_raw': {'train_loss': 15.62337, 'val_loss': 15.301433, 'test_loss': 16.146216}}
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2024-10-14 14:35:06,500 (client:354) INFO: {'Role': 'Client #8', 'Round': 18, 'Results_raw': {'train_loss': 13.660162, 'val_loss': 13.475399, 'test_loss': 14.00611}}
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2024-10-14 14:36:05,181 (client:354) INFO: {'Role': 'Client #2', 'Round': 18, 'Results_raw': {'train_loss': 9.180191, 'val_loss': 8.65977, 'test_loss': 9.137857}}
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2024-10-14 14:37:03,101 (client:354) INFO: {'Role': 'Client #6', 'Round': 18, 'Results_raw': {'train_loss': 15.591549, 'val_loss': 15.151564, 'test_loss': 16.443406}}
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2024-10-14 14:37:03,105 (server:615) INFO: {'Role': 'Server #', 'Round': 17, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.089485), 'test_loss': np.float64(104143.891699), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.31255), 'val_loss': np.float64(105300.260858)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.089485), 'test_loss': np.float64(104143.891699), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.31255), 'val_loss': np.float64(105300.260858)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.524587), 'test_avg_loss_bottom_decile': np.float64(16.574295), 'test_avg_loss_top_decile': np.float64(24.439979), 'test_avg_loss_min': np.float64(16.410423), 'test_avg_loss_max': np.float64(24.439979), 'test_avg_loss_bottom10%': np.float64(16.410423), 'test_avg_loss_top10%': np.float64(24.439979), 'test_avg_loss_cos1': np.float64(0.992196), 'test_avg_loss_entropy': np.float64(2.294666), 'test_loss_std': np.float64(13087.460276), 'test_loss_bottom_decile': np.float64(85921.147583), 'test_loss_top_decile': np.float64(126696.851868), 'test_loss_min': np.float64(85071.633392), 'test_loss_max': np.float64(126696.851868), 'test_loss_bottom10%': np.float64(85071.633392), 'test_loss_top10%': np.float64(126696.851868), 'test_loss_cos1': np.float64(0.992196), 'test_loss_entropy': np.float64(2.294666), 'val_avg_loss_std': np.float64(2.809211), 'val_avg_loss_bottom_decile': np.float64(16.321369), 'val_avg_loss_top_decile': np.float64(24.48904), 'val_avg_loss_min': np.float64(16.205463), 'val_avg_loss_max': np.float64(24.48904), 'val_avg_loss_bottom10%': np.float64(16.205463), 'val_avg_loss_top10%': np.float64(24.48904), 'val_avg_loss_cos1': np.float64(0.990572), 'val_avg_loss_entropy': np.float64(2.292912), 'val_loss_std': np.float64(14562.949459), 'val_loss_bottom_decile': np.float64(84609.97699), 'val_loss_top_decile': np.float64(126951.181091), 'val_loss_min': np.float64(84009.119049), 'val_loss_max': np.float64(126951.181091), 'val_loss_bottom10%': np.float64(84009.119049), 'val_loss_top10%': np.float64(126951.181091), 'val_loss_cos1': np.float64(0.990572), 'val_loss_entropy': np.float64(2.292912)}}
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2024-10-14 14:37:03,145 (server:353) INFO: Server: Starting evaluation at the end of round 18.
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2024-10-14 14:37:03,146 (server:359) INFO: ----------- Starting a new training round (Round #19) -------------
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2024-10-14 14:39:35,512 (client:354) INFO: {'Role': 'Client #3', 'Round': 19, 'Results_raw': {'train_loss': 10.514706, 'val_loss': 10.790286, 'test_loss': 11.989441}}
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2024-10-14 14:40:34,994 (client:354) INFO: {'Role': 'Client #7', 'Round': 19, 'Results_raw': {'train_loss': 15.714249, 'val_loss': 15.586351, 'test_loss': 16.019754}}
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2024-10-14 14:41:34,278 (client:354) INFO: {'Role': 'Client #2', 'Round': 19, 'Results_raw': {'train_loss': 9.029544, 'val_loss': 8.652915, 'test_loss': 9.050728}}
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2024-10-14 14:42:32,588 (client:354) INFO: {'Role': 'Client #4', 'Round': 19, 'Results_raw': {'train_loss': 15.538924, 'val_loss': 15.258335, 'test_loss': 16.098386}}
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2024-10-14 14:43:30,015 (client:354) INFO: {'Role': 'Client #5', 'Round': 19, 'Results_raw': {'train_loss': 16.425647, 'val_loss': 16.961223, 'test_loss': 18.706604}}
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2024-10-14 14:44:29,382 (client:354) INFO: {'Role': 'Client #9', 'Round': 19, 'Results_raw': {'train_loss': 18.283298, 'val_loss': 18.028817, 'test_loss': 18.530885}}
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2024-10-14 14:45:30,682 (client:354) INFO: {'Role': 'Client #10', 'Round': 19, 'Results_raw': {'train_loss': 15.439621, 'val_loss': 15.559731, 'test_loss': 16.863246}}
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2024-10-14 14:46:40,143 (client:354) INFO: {'Role': 'Client #1', 'Round': 19, 'Results_raw': {'train_loss': 11.158475, 'val_loss': 10.636151, 'test_loss': 11.55492}}
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2024-10-14 14:47:38,579 (client:354) INFO: {'Role': 'Client #8', 'Round': 19, 'Results_raw': {'train_loss': 13.602405, 'val_loss': 13.380398, 'test_loss': 14.029186}}
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2024-10-14 14:48:37,704 (client:354) INFO: {'Role': 'Client #6', 'Round': 19, 'Results_raw': {'train_loss': 15.521117, 'val_loss': 15.165687, 'test_loss': 16.37388}}
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2024-10-14 14:48:37,715 (server:615) INFO: {'Role': 'Server #', 'Round': 18, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.214289), 'test_loss': np.float64(104790.872287), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.402811), 'val_loss': np.float64(105768.17352)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.214289), 'test_loss': np.float64(104790.872287), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.402811), 'val_loss': np.float64(105768.17352)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.397425), 'test_avg_loss_bottom_decile': np.float64(16.90995), 'test_avg_loss_top_decile': np.float64(24.458957), 'test_avg_loss_min': np.float64(16.727548), 'test_avg_loss_max': np.float64(24.458957), 'test_avg_loss_bottom10%': np.float64(16.727548), 'test_avg_loss_top10%': np.float64(24.458957), 'test_avg_loss_cos1': np.float64(0.99304), 'test_avg_loss_entropy': np.float64(2.295547), 'test_loss_std': np.float64(12428.248616), 'test_loss_bottom_decile': np.float64(87661.17865), 'test_loss_top_decile': np.float64(126795.235474), 'test_loss_min': np.float64(86715.607666), 'test_loss_max': np.float64(126795.235474), 'test_loss_bottom10%': np.float64(86715.607666), 'test_loss_top10%': np.float64(126795.235474), 'test_loss_cos1': np.float64(0.99304), 'test_loss_entropy': np.float64(2.295547), 'val_avg_loss_std': np.float64(2.666968), 'val_avg_loss_bottom_decile': np.float64(16.811066), 'val_avg_loss_top_decile': np.float64(24.477649), 'val_avg_loss_min': np.float64(16.340033), 'val_avg_loss_max': np.float64(24.477649), 'val_avg_loss_bottom10%': np.float64(16.340033), 'val_avg_loss_top10%': np.float64(24.477649), 'val_avg_loss_cos1': np.float64(0.991565), 'val_avg_loss_entropy': np.float64(2.293965), 'val_loss_std': np.float64(13825.560709), 'val_loss_bottom_decile': np.float64(87148.563782), 'val_loss_top_decile': np.float64(126892.131042), 'val_loss_min': np.float64(84706.733154), 'val_loss_max': np.float64(126892.131042), 'val_loss_bottom10%': np.float64(84706.733154), 'val_loss_top10%': np.float64(126892.131042), 'val_loss_cos1': np.float64(0.991565), 'val_loss_entropy': np.float64(2.293965)}}
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2024-10-14 14:48:37,747 (server:353) INFO: Server: Starting evaluation at the end of round 19.
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2024-10-14 14:48:37,748 (server:359) INFO: ----------- Starting a new training round (Round #20) -------------
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2024-10-14 14:51:19,345 (client:354) INFO: {'Role': 'Client #3', 'Round': 20, 'Results_raw': {'train_loss': 10.435553, 'val_loss': 10.778322, 'test_loss': 11.993979}}
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2024-10-14 14:52:21,157 (client:354) INFO: {'Role': 'Client #4', 'Round': 20, 'Results_raw': {'train_loss': 15.503909, 'val_loss': 15.264948, 'test_loss': 16.130618}}
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2024-10-14 14:53:21,822 (client:354) INFO: {'Role': 'Client #1', 'Round': 20, 'Results_raw': {'train_loss': 11.058132, 'val_loss': 10.779908, 'test_loss': 11.800549}}
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2024-10-14 14:54:22,188 (client:354) INFO: {'Role': 'Client #5', 'Round': 20, 'Results_raw': {'train_loss': 16.442082, 'val_loss': 16.728146, 'test_loss': 18.646164}}
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2024-10-14 14:55:22,265 (client:354) INFO: {'Role': 'Client #9', 'Round': 20, 'Results_raw': {'train_loss': 18.282686, 'val_loss': 17.902199, 'test_loss': 18.377486}}
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2024-10-14 14:56:20,181 (client:354) INFO: {'Role': 'Client #8', 'Round': 20, 'Results_raw': {'train_loss': 13.620684, 'val_loss': 13.42107, 'test_loss': 14.001707}}
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2024-10-14 14:57:17,962 (client:354) INFO: {'Role': 'Client #7', 'Round': 20, 'Results_raw': {'train_loss': 15.681304, 'val_loss': 15.792094, 'test_loss': 16.782253}}
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2024-10-14 14:58:17,712 (client:354) INFO: {'Role': 'Client #6', 'Round': 20, 'Results_raw': {'train_loss': 15.457548, 'val_loss': 15.0647, 'test_loss': 16.346638}}
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2024-10-14 14:59:19,094 (client:354) INFO: {'Role': 'Client #2', 'Round': 20, 'Results_raw': {'train_loss': 8.985364, 'val_loss': 8.741246, 'test_loss': 9.304645}}
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2024-10-14 15:00:15,873 (client:354) INFO: {'Role': 'Client #10', 'Round': 20, 'Results_raw': {'train_loss': 15.405022, 'val_loss': 15.871509, 'test_loss': 17.187616}}
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2024-10-14 15:00:15,884 (server:615) INFO: {'Role': 'Server #', 'Round': 19, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.116861), 'test_loss': np.float64(104285.807541), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.345513), 'val_loss': np.float64(105471.140491)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.116861), 'test_loss': np.float64(104285.807541), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.345513), 'val_loss': np.float64(105471.140491)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.536438), 'test_avg_loss_bottom_decile': np.float64(16.487437), 'test_avg_loss_top_decile': np.float64(24.494265), 'test_avg_loss_min': np.float64(16.482404), 'test_avg_loss_max': np.float64(24.494265), 'test_avg_loss_bottom10%': np.float64(16.482404), 'test_avg_loss_top10%': np.float64(24.494265), 'test_avg_loss_cos1': np.float64(0.992145), 'test_avg_loss_entropy': np.float64(2.294613), 'test_loss_std': np.float64(13148.895144), 'test_loss_bottom_decile': np.float64(85470.873383), 'test_loss_top_decile': np.float64(126978.270203), 'test_loss_min': np.float64(85444.781281), 'test_loss_max': np.float64(126978.270203), 'test_loss_bottom10%': np.float64(85444.781281), 'test_loss_top10%': np.float64(126978.270203), 'test_loss_cos1': np.float64(0.992145), 'test_loss_entropy': np.float64(2.294613), 'val_avg_loss_std': np.float64(2.822639), 'val_avg_loss_bottom_decile': np.float64(16.408328), 'val_avg_loss_top_decile': np.float64(24.543823), 'val_avg_loss_min': np.float64(16.114972), 'val_avg_loss_max': np.float64(24.543823), 'val_avg_loss_bottom10%': np.float64(16.114972), 'val_avg_loss_top10%': np.float64(24.543823), 'val_avg_loss_cos1': np.float64(0.990513), 'val_avg_loss_entropy': np.float64(2.292852), 'val_loss_std': np.float64(14632.561029), 'val_loss_bottom_decile': np.float64(85060.771332), 'val_loss_top_decile': np.float64(127235.177673), 'val_loss_min': np.float64(83540.012604), 'val_loss_max': np.float64(127235.177673), 'val_loss_bottom10%': np.float64(83540.012604), 'val_loss_top10%': np.float64(127235.177673), 'val_loss_cos1': np.float64(0.990513), 'val_loss_entropy': np.float64(2.292852)}}
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2024-10-14 15:00:15,925 (server:353) INFO: Server: Starting evaluation at the end of round 20.
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2024-10-14 15:00:15,926 (server:359) INFO: ----------- Starting a new training round (Round #21) -------------
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2024-10-14 15:02:49,580 (client:354) INFO: {'Role': 'Client #5', 'Round': 21, 'Results_raw': {'train_loss': 16.358757, 'val_loss': 16.697946, 'test_loss': 18.820054}}
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2024-10-14 15:03:48,508 (client:354) INFO: {'Role': 'Client #3', 'Round': 21, 'Results_raw': {'train_loss': 10.432577, 'val_loss': 10.635424, 'test_loss': 11.946646}}
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2024-10-14 15:04:47,964 (client:354) INFO: {'Role': 'Client #6', 'Round': 21, 'Results_raw': {'train_loss': 15.503839, 'val_loss': 15.091418, 'test_loss': 16.43034}}
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2024-10-14 15:05:49,030 (client:354) INFO: {'Role': 'Client #9', 'Round': 21, 'Results_raw': {'train_loss': 18.203451, 'val_loss': 17.871766, 'test_loss': 18.532164}}
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2024-10-14 15:06:47,348 (client:354) INFO: {'Role': 'Client #4', 'Round': 21, 'Results_raw': {'train_loss': 15.505539, 'val_loss': 15.386445, 'test_loss': 16.214408}}
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2024-10-14 15:07:47,209 (client:354) INFO: {'Role': 'Client #1', 'Round': 21, 'Results_raw': {'train_loss': 11.060715, 'val_loss': 10.715005, 'test_loss': 11.71055}}
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2024-10-14 15:08:48,800 (client:354) INFO: {'Role': 'Client #7', 'Round': 21, 'Results_raw': {'train_loss': 15.668777, 'val_loss': 15.459222, 'test_loss': 15.914982}}
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2024-10-14 15:09:50,119 (client:354) INFO: {'Role': 'Client #2', 'Round': 21, 'Results_raw': {'train_loss': 9.002761, 'val_loss': 8.570373, 'test_loss': 9.228169}}
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2024-10-14 15:10:45,063 (client:354) INFO: {'Role': 'Client #10', 'Round': 21, 'Results_raw': {'train_loss': 15.331204, 'val_loss': 15.50574, 'test_loss': 16.835825}}
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2024-10-14 15:11:53,461 (client:354) INFO: {'Role': 'Client #8', 'Round': 21, 'Results_raw': {'train_loss': 13.610309, 'val_loss': 13.444033, 'test_loss': 14.095713}}
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2024-10-14 15:11:53,464 (server:615) INFO: {'Role': 'Server #', 'Round': 20, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.029087), 'test_loss': np.float64(103830.785532), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.211827), 'val_loss': np.float64(104778.111057)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.029087), 'test_loss': np.float64(103830.785532), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.211827), 'val_loss': np.float64(104778.111057)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.388301), 'test_avg_loss_bottom_decile': np.float64(16.822704), 'test_avg_loss_top_decile': np.float64(24.206439), 'test_avg_loss_min': np.float64(16.510749), 'test_avg_loss_max': np.float64(24.206439), 'test_avg_loss_bottom10%': np.float64(16.510749), 'test_avg_loss_top10%': np.float64(24.206439), 'test_avg_loss_cos1': np.float64(0.992966), 'test_avg_loss_entropy': np.float64(2.29547), 'test_loss_std': np.float64(12380.951419), 'test_loss_bottom_decile': np.float64(87208.897736), 'test_loss_top_decile': np.float64(125486.181458), 'test_loss_min': np.float64(85591.725189), 'test_loss_max': np.float64(125486.181458), 'test_loss_bottom10%': np.float64(85591.725189), 'test_loss_top10%': np.float64(125486.181458), 'test_loss_cos1': np.float64(0.992966), 'test_loss_entropy': np.float64(2.29547), 'val_avg_loss_std': np.float64(2.667262), 'val_avg_loss_bottom_decile': np.float64(16.715619), 'val_avg_loss_top_decile': np.float64(24.228635), 'val_avg_loss_min': np.float64(16.11092), 'val_avg_loss_max': np.float64(24.228635), 'val_avg_loss_bottom10%': np.float64(16.11092), 'val_avg_loss_top10%': np.float64(24.228635), 'val_avg_loss_cos1': np.float64(0.991405), 'val_avg_loss_entropy': np.float64(2.2938), 'val_loss_std': np.float64(13827.084506), 'val_loss_bottom_decile': np.float64(86653.767395), 'val_loss_top_decile': np.float64(125601.242615), 'val_loss_min': np.float64(83519.010345), 'val_loss_max': np.float64(125601.242615), 'val_loss_bottom10%': np.float64(83519.010345), 'val_loss_top10%': np.float64(125601.242615), 'val_loss_cos1': np.float64(0.991405), 'val_loss_entropy': np.float64(2.2938)}}
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2024-10-14 15:11:53,499 (server:353) INFO: Server: Starting evaluation at the end of round 21.
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2024-10-14 15:11:53,499 (server:359) INFO: ----------- Starting a new training round (Round #22) -------------
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2024-10-14 15:14:20,178 (client:354) INFO: {'Role': 'Client #4', 'Round': 22, 'Results_raw': {'train_loss': 15.434536, 'val_loss': 15.282165, 'test_loss': 16.351312}}
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2024-10-14 15:15:15,571 (client:354) INFO: {'Role': 'Client #8', 'Round': 22, 'Results_raw': {'train_loss': 13.537028, 'val_loss': 13.265929, 'test_loss': 13.856826}}
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2024-10-14 15:16:10,696 (client:354) INFO: {'Role': 'Client #2', 'Round': 22, 'Results_raw': {'train_loss': 9.045344, 'val_loss': 8.594565, 'test_loss': 9.092957}}
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2024-10-14 15:17:14,105 (client:354) INFO: {'Role': 'Client #6', 'Round': 22, 'Results_raw': {'train_loss': 15.409627, 'val_loss': 15.189315, 'test_loss': 16.387661}}
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2024-10-14 15:18:13,047 (client:354) INFO: {'Role': 'Client #3', 'Round': 22, 'Results_raw': {'train_loss': 10.445428, 'val_loss': 10.767877, 'test_loss': 11.91201}}
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2024-10-14 15:19:13,010 (client:354) INFO: {'Role': 'Client #10', 'Round': 22, 'Results_raw': {'train_loss': 15.326512, 'val_loss': 15.377288, 'test_loss': 16.659409}}
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2024-10-14 15:20:13,157 (client:354) INFO: {'Role': 'Client #1', 'Round': 22, 'Results_raw': {'train_loss': 10.987545, 'val_loss': 10.639297, 'test_loss': 11.572195}}
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2024-10-14 15:21:12,405 (client:354) INFO: {'Role': 'Client #9', 'Round': 22, 'Results_raw': {'train_loss': 18.164708, 'val_loss': 17.829725, 'test_loss': 18.558227}}
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2024-10-14 15:22:10,534 (client:354) INFO: {'Role': 'Client #7', 'Round': 22, 'Results_raw': {'train_loss': 15.644887, 'val_loss': 15.687991, 'test_loss': 16.38879}}
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2024-10-14 15:23:10,320 (client:354) INFO: {'Role': 'Client #5', 'Round': 22, 'Results_raw': {'train_loss': 16.321839, 'val_loss': 16.892845, 'test_loss': 18.983148}}
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2024-10-14 15:23:10,324 (server:615) INFO: {'Role': 'Server #', 'Round': 21, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.102547), 'test_loss': np.float64(104211.606107), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.28015), 'val_loss': np.float64(105132.29971)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.102547), 'test_loss': np.float64(104211.606107), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.28015), 'val_loss': np.float64(105132.29971)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.331939), 'test_avg_loss_bottom_decile': np.float64(17.037347), 'test_avg_loss_top_decile': np.float64(24.298397), 'test_avg_loss_min': np.float64(16.616327), 'test_avg_loss_max': np.float64(24.298397), 'test_avg_loss_bottom10%': np.float64(16.616327), 'test_avg_loss_top10%': np.float64(24.298397), 'test_avg_loss_cos1': np.float64(0.993339), 'test_avg_loss_entropy': np.float64(2.295864), 'test_loss_std': np.float64(12088.770225), 'test_loss_bottom_decile': np.float64(88321.609222), 'test_loss_top_decile': np.float64(125962.892151), 'test_loss_min': np.float64(86139.039612), 'test_loss_max': np.float64(125962.892151), 'test_loss_bottom10%': np.float64(86139.039612), 'test_loss_top10%': np.float64(125962.892151), 'test_loss_cos1': np.float64(0.993339), 'test_loss_entropy': np.float64(2.295864), 'val_avg_loss_std': np.float64(2.601051), 'val_avg_loss_bottom_decile': np.float64(16.913243), 'val_avg_loss_top_decile': np.float64(24.296028), 'val_avg_loss_min': np.float64(16.213397), 'val_avg_loss_max': np.float64(24.296028), 'val_avg_loss_bottom10%': np.float64(16.213397), 'val_avg_loss_top10%': np.float64(24.296028), 'val_avg_loss_cos1': np.float64(0.991875), 'val_avg_loss_entropy': np.float64(2.294297), 'val_loss_std': np.float64(13483.84595), 'val_loss_bottom_decile': np.float64(87678.250397), 'val_loss_top_decile': np.float64(125950.608459), 'val_loss_min': np.float64(84050.251678), 'val_loss_max': np.float64(125950.608459), 'val_loss_bottom10%': np.float64(84050.251678), 'val_loss_top10%': np.float64(125950.608459), 'val_loss_cos1': np.float64(0.991875), 'val_loss_entropy': np.float64(2.294297)}}
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2024-10-14 15:23:10,358 (server:353) INFO: Server: Starting evaluation at the end of round 22.
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2024-10-14 15:23:10,359 (server:359) INFO: ----------- Starting a new training round (Round #23) -------------
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2024-10-14 15:25:44,950 (client:354) INFO: {'Role': 'Client #5', 'Round': 23, 'Results_raw': {'train_loss': 16.285761, 'val_loss': 16.774007, 'test_loss': 18.730891}}
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2024-10-14 15:26:43,052 (client:354) INFO: {'Role': 'Client #6', 'Round': 23, 'Results_raw': {'train_loss': 15.403419, 'val_loss': 15.209406, 'test_loss': 16.439742}}
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2024-10-14 15:27:42,642 (client:354) INFO: {'Role': 'Client #9', 'Round': 23, 'Results_raw': {'train_loss': 18.157023, 'val_loss': 17.802524, 'test_loss': 18.467963}}
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2024-10-14 15:28:38,714 (client:354) INFO: {'Role': 'Client #1', 'Round': 23, 'Results_raw': {'train_loss': 11.001074, 'val_loss': 10.599866, 'test_loss': 11.612371}}
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2024-10-14 15:29:38,350 (client:354) INFO: {'Role': 'Client #8', 'Round': 23, 'Results_raw': {'train_loss': 13.577807, 'val_loss': 13.459661, 'test_loss': 14.097624}}
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2024-10-14 15:30:41,118 (client:354) INFO: {'Role': 'Client #3', 'Round': 23, 'Results_raw': {'train_loss': 10.392646, 'val_loss': 10.752534, 'test_loss': 12.023447}}
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2024-10-14 15:31:40,739 (client:354) INFO: {'Role': 'Client #4', 'Round': 23, 'Results_raw': {'train_loss': 15.336884, 'val_loss': 15.229665, 'test_loss': 16.19719}}
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2024-10-14 15:32:39,444 (client:354) INFO: {'Role': 'Client #7', 'Round': 23, 'Results_raw': {'train_loss': 15.621376, 'val_loss': 15.611902, 'test_loss': 16.381917}}
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2024-10-14 15:33:36,985 (client:354) INFO: {'Role': 'Client #10', 'Round': 23, 'Results_raw': {'train_loss': 15.315762, 'val_loss': 15.422368, 'test_loss': 16.845435}}
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2024-10-14 15:34:34,484 (client:354) INFO: {'Role': 'Client #2', 'Round': 23, 'Results_raw': {'train_loss': 8.967658, 'val_loss': 8.680178, 'test_loss': 9.127499}}
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2024-10-14 15:34:34,489 (server:615) INFO: {'Role': 'Server #', 'Round': 22, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.979855), 'test_loss': np.float64(103575.569833), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.126373), 'val_loss': np.float64(104335.116315)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.979855), 'test_loss': np.float64(103575.569833), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.126373), 'val_loss': np.float64(104335.116315)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.243096), 'test_avg_loss_bottom_decile': np.float64(17.126171), 'test_avg_loss_top_decile': np.float64(23.967244), 'test_avg_loss_min': np.float64(16.589979), 'test_avg_loss_max': np.float64(23.967244), 'test_avg_loss_bottom10%': np.float64(16.589979), 'test_avg_loss_top10%': np.float64(23.967244), 'test_avg_loss_cos1': np.float64(0.993757), 'test_avg_loss_entropy': np.float64(2.296292), 'test_loss_std': np.float64(11628.209808), 'test_loss_bottom_decile': np.float64(88782.069458), 'test_loss_top_decile': np.float64(124246.193481), 'test_loss_min': np.float64(86002.449524), 'test_loss_max': np.float64(124246.193481), 'test_loss_bottom10%': np.float64(86002.449524), 'test_loss_top10%': np.float64(124246.193481), 'test_loss_cos1': np.float64(0.993757), 'test_loss_entropy': np.float64(2.296292), 'val_avg_loss_std': np.float64(2.499154), 'val_avg_loss_bottom_decile': np.float64(16.976577), 'val_avg_loss_top_decile': np.float64(23.912472), 'val_avg_loss_min': np.float64(16.188789), 'val_avg_loss_max': np.float64(23.912472), 'val_avg_loss_bottom10%': np.float64(16.188789), 'val_avg_loss_top10%': np.float64(23.912472), 'val_avg_loss_cos1': np.float64(0.992379), 'val_avg_loss_entropy': np.float64(2.294824), 'val_loss_std': np.float64(12955.612829), 'val_loss_bottom_decile': np.float64(88006.575958), 'val_loss_top_decile': np.float64(123962.255066), 'val_loss_min': np.float64(83922.684082), 'val_loss_max': np.float64(123962.255066), 'val_loss_bottom10%': np.float64(83922.684082), 'val_loss_top10%': np.float64(123962.255066), 'val_loss_cos1': np.float64(0.992379), 'val_loss_entropy': np.float64(2.294824)}}
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2024-10-14 15:34:34,537 (server:353) INFO: Server: Starting evaluation at the end of round 23.
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2024-10-14 15:34:34,538 (server:359) INFO: ----------- Starting a new training round (Round #24) -------------
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2024-10-14 15:37:16,952 (client:354) INFO: {'Role': 'Client #4', 'Round': 24, 'Results_raw': {'train_loss': 15.394181, 'val_loss': 15.241931, 'test_loss': 16.143488}}
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2024-10-14 15:38:10,609 (client:354) INFO: {'Role': 'Client #2', 'Round': 24, 'Results_raw': {'train_loss': 8.906314, 'val_loss': 8.5656, 'test_loss': 9.16327}}
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2024-10-14 15:39:06,598 (client:354) INFO: {'Role': 'Client #10', 'Round': 24, 'Results_raw': {'train_loss': 15.259741, 'val_loss': 15.397248, 'test_loss': 16.791555}}
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2024-10-14 15:40:03,729 (client:354) INFO: {'Role': 'Client #6', 'Round': 24, 'Results_raw': {'train_loss': 15.362516, 'val_loss': 15.053422, 'test_loss': 16.209827}}
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2024-10-14 15:41:00,349 (client:354) INFO: {'Role': 'Client #9', 'Round': 24, 'Results_raw': {'train_loss': 18.102483, 'val_loss': 17.832956, 'test_loss': 18.449213}}
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2024-10-14 15:42:05,547 (client:354) INFO: {'Role': 'Client #8', 'Round': 24, 'Results_raw': {'train_loss': 13.524023, 'val_loss': 13.424089, 'test_loss': 14.047773}}
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2024-10-14 15:43:03,536 (client:354) INFO: {'Role': 'Client #3', 'Round': 24, 'Results_raw': {'train_loss': 10.365706, 'val_loss': 10.688612, 'test_loss': 11.866658}}
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2024-10-14 15:44:02,209 (client:354) INFO: {'Role': 'Client #7', 'Round': 24, 'Results_raw': {'train_loss': 15.641786, 'val_loss': 15.824976, 'test_loss': 16.419401}}
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2024-10-14 15:45:02,439 (client:354) INFO: {'Role': 'Client #1', 'Round': 24, 'Results_raw': {'train_loss': 10.922194, 'val_loss': 10.689117, 'test_loss': 11.660415}}
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2024-10-14 15:45:59,616 (client:354) INFO: {'Role': 'Client #5', 'Round': 24, 'Results_raw': {'train_loss': 16.262602, 'val_loss': 16.798479, 'test_loss': 18.570882}}
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2024-10-14 15:45:59,620 (server:615) INFO: {'Role': 'Server #', 'Round': 23, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.038249), 'test_loss': np.float64(103878.280612), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.175463), 'val_loss': np.float64(104589.60036)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(20.038249), 'test_loss': np.float64(103878.280612), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.175463), 'val_loss': np.float64(104589.60036)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.20565), 'test_avg_loss_bottom_decile': np.float64(17.207473), 'test_avg_loss_top_decile': np.float64(23.985935), 'test_avg_loss_min': np.float64(16.747414), 'test_avg_loss_max': np.float64(23.985935), 'test_avg_loss_bottom10%': np.float64(16.747414), 'test_avg_loss_top10%': np.float64(23.985935), 'test_avg_loss_cos1': np.float64(0.993997), 'test_avg_loss_entropy': np.float64(2.296538), 'test_loss_std': np.float64(11434.091129), 'test_loss_bottom_decile': np.float64(89203.538269), 'test_loss_top_decile': np.float64(124343.089172), 'test_loss_min': np.float64(86818.594604), 'test_loss_max': np.float64(124343.089172), 'test_loss_bottom10%': np.float64(86818.594604), 'test_loss_top10%': np.float64(124343.089172), 'test_loss_cos1': np.float64(0.993997), 'test_loss_entropy': np.float64(2.296538), 'val_avg_loss_std': np.float64(2.457994), 'val_avg_loss_bottom_decile': np.float64(17.056636), 'val_avg_loss_top_decile': np.float64(23.936485), 'val_avg_loss_min': np.float64(16.340538), 'val_avg_loss_max': np.float64(23.936485), 'val_avg_loss_bottom10%': np.float64(16.340538), 'val_avg_loss_top10%': np.float64(23.936485), 'val_avg_loss_cos1': np.float64(0.99266), 'val_avg_loss_entropy': np.float64(2.295116), 'val_loss_std': np.float64(12742.238803), 'val_loss_bottom_decile': np.float64(88421.598938), 'val_loss_top_decile': np.float64(124086.737915), 'val_loss_min': np.float64(84709.35025), 'val_loss_max': np.float64(124086.737915), 'val_loss_bottom10%': np.float64(84709.35025), 'val_loss_top10%': np.float64(124086.737915), 'val_loss_cos1': np.float64(0.99266), 'val_loss_entropy': np.float64(2.295116)}}
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2024-10-14 15:45:59,669 (server:353) INFO: Server: Starting evaluation at the end of round 24.
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2024-10-14 15:45:59,669 (server:359) INFO: ----------- Starting a new training round (Round #25) -------------
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2024-10-14 15:48:25,275 (client:354) INFO: {'Role': 'Client #5', 'Round': 25, 'Results_raw': {'train_loss': 16.199481, 'val_loss': 16.838297, 'test_loss': 18.713046}}
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2024-10-14 15:49:29,939 (client:354) INFO: {'Role': 'Client #2', 'Round': 25, 'Results_raw': {'train_loss': 8.889479, 'val_loss': 8.574443, 'test_loss': 9.130031}}
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2024-10-14 15:50:31,353 (client:354) INFO: {'Role': 'Client #4', 'Round': 25, 'Results_raw': {'train_loss': 15.34587, 'val_loss': 15.284713, 'test_loss': 16.238979}}
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2024-10-14 15:51:29,210 (client:354) INFO: {'Role': 'Client #8', 'Round': 25, 'Results_raw': {'train_loss': 13.448387, 'val_loss': 13.329572, 'test_loss': 13.986617}}
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2024-10-14 15:52:28,364 (client:354) INFO: {'Role': 'Client #10', 'Round': 25, 'Results_raw': {'train_loss': 15.236218, 'val_loss': 15.596512, 'test_loss': 17.217267}}
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2024-10-14 15:53:27,640 (client:354) INFO: {'Role': 'Client #3', 'Round': 25, 'Results_raw': {'train_loss': 10.345595, 'val_loss': 10.723025, 'test_loss': 11.997186}}
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2024-10-14 15:54:25,339 (client:354) INFO: {'Role': 'Client #9', 'Round': 25, 'Results_raw': {'train_loss': 18.052485, 'val_loss': 17.820739, 'test_loss': 18.371911}}
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2024-10-14 15:55:17,957 (client:354) INFO: {'Role': 'Client #1', 'Round': 25, 'Results_raw': {'train_loss': 10.977905, 'val_loss': 10.64283, 'test_loss': 11.703918}}
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2024-10-14 15:56:11,084 (client:354) INFO: {'Role': 'Client #6', 'Round': 25, 'Results_raw': {'train_loss': 15.37898, 'val_loss': 15.163805, 'test_loss': 16.229591}}
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2024-10-14 15:57:10,241 (client:354) INFO: {'Role': 'Client #7', 'Round': 25, 'Results_raw': {'train_loss': 15.541108, 'val_loss': 15.661755, 'test_loss': 16.410454}}
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2024-10-14 15:57:10,253 (server:615) INFO: {'Role': 'Server #', 'Round': 24, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.983145), 'test_loss': np.float64(103592.623788), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.147341), 'val_loss': np.float64(104443.81539)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.983145), 'test_loss': np.float64(103592.623788), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.147341), 'val_loss': np.float64(104443.81539)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.313867), 'test_avg_loss_bottom_decile': np.float64(16.960402), 'test_avg_loss_top_decile': np.float64(24.118778), 'test_avg_loss_min': np.float64(16.553137), 'test_avg_loss_max': np.float64(24.118778), 'test_avg_loss_bottom10%': np.float64(16.553137), 'test_avg_loss_top10%': np.float64(24.118778), 'test_avg_loss_cos1': np.float64(0.993363), 'test_avg_loss_entropy': np.float64(2.295888), 'test_loss_std': np.float64(11995.08709), 'test_loss_bottom_decile': np.float64(87922.7258), 'test_loss_top_decile': np.float64(125031.742737), 'test_loss_min': np.float64(85811.463959), 'test_loss_max': np.float64(125031.742737), 'test_loss_bottom10%': np.float64(85811.463959), 'test_loss_top10%': np.float64(125031.742737), 'test_loss_cos1': np.float64(0.993363), 'test_loss_entropy': np.float64(2.295888), 'val_avg_loss_std': np.float64(2.582806), 'val_avg_loss_bottom_decile': np.float64(16.831149), 'val_avg_loss_top_decile': np.float64(24.090977), 'val_avg_loss_min': np.float64(16.140379), 'val_avg_loss_max': np.float64(24.090977), 'val_avg_loss_bottom10%': np.float64(16.140379), 'val_avg_loss_top10%': np.float64(24.090977), 'val_avg_loss_cos1': np.float64(0.991883), 'val_avg_loss_entropy': np.float64(2.294305), 'val_loss_std': np.float64(13389.265862), 'val_loss_bottom_decile': np.float64(87252.675659), 'val_loss_top_decile': np.float64(124887.623474), 'val_loss_min': np.float64(83671.723572), 'val_loss_max': np.float64(124887.623474), 'val_loss_bottom10%': np.float64(83671.723572), 'val_loss_top10%': np.float64(124887.623474), 'val_loss_cos1': np.float64(0.991883), 'val_loss_entropy': np.float64(2.294305)}}
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2024-10-14 15:57:10,300 (server:353) INFO: Server: Starting evaluation at the end of round 25.
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2024-10-14 15:57:10,301 (server:359) INFO: ----------- Starting a new training round (Round #26) -------------
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2024-10-14 15:59:41,805 (client:354) INFO: {'Role': 'Client #8', 'Round': 26, 'Results_raw': {'train_loss': 13.486087, 'val_loss': 13.223664, 'test_loss': 13.814334}}
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2024-10-14 16:00:36,114 (client:354) INFO: {'Role': 'Client #9', 'Round': 26, 'Results_raw': {'train_loss': 18.045404, 'val_loss': 17.964906, 'test_loss': 18.77908}}
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2024-10-14 16:01:40,156 (client:354) INFO: {'Role': 'Client #7', 'Round': 26, 'Results_raw': {'train_loss': 15.540902, 'val_loss': 15.606442, 'test_loss': 16.230793}}
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2024-10-14 16:02:39,713 (client:354) INFO: {'Role': 'Client #10', 'Round': 26, 'Results_raw': {'train_loss': 15.193852, 'val_loss': 15.346824, 'test_loss': 16.667226}}
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2024-10-14 16:03:39,310 (client:354) INFO: {'Role': 'Client #2', 'Round': 26, 'Results_raw': {'train_loss': 8.951409, 'val_loss': 8.561267, 'test_loss': 8.915676}}
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2024-10-14 16:04:38,132 (client:354) INFO: {'Role': 'Client #5', 'Round': 26, 'Results_raw': {'train_loss': 16.183042, 'val_loss': 16.826329, 'test_loss': 18.601823}}
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2024-10-14 16:05:38,087 (client:354) INFO: {'Role': 'Client #1', 'Round': 26, 'Results_raw': {'train_loss': 10.882176, 'val_loss': 10.570614, 'test_loss': 11.520585}}
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2024-10-14 16:06:39,054 (client:354) INFO: {'Role': 'Client #6', 'Round': 26, 'Results_raw': {'train_loss': 15.29583, 'val_loss': 15.018764, 'test_loss': 16.288301}}
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2024-10-14 16:07:40,169 (client:354) INFO: {'Role': 'Client #4', 'Round': 26, 'Results_raw': {'train_loss': 15.318749, 'val_loss': 15.261526, 'test_loss': 16.325525}}
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2024-10-14 16:08:39,184 (client:354) INFO: {'Role': 'Client #3', 'Round': 26, 'Results_raw': {'train_loss': 10.379385, 'val_loss': 10.775942, 'test_loss': 12.136744}}
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2024-10-14 16:08:39,189 (server:615) INFO: {'Role': 'Server #', 'Round': 25, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.9331), 'test_loss': np.float64(103333.191138), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.068349), 'val_loss': np.float64(104034.322342)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.9331), 'test_loss': np.float64(103333.191138), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.068349), 'val_loss': np.float64(104034.322342)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.274746), 'test_avg_loss_bottom_decile': np.float64(16.943619), 'test_avg_loss_top_decile': np.float64(23.988403), 'test_avg_loss_min': np.float64(16.57483), 'test_avg_loss_max': np.float64(23.988403), 'test_avg_loss_bottom10%': np.float64(16.57483), 'test_avg_loss_top10%': np.float64(23.988403), 'test_avg_loss_cos1': np.float64(0.993551), 'test_avg_loss_entropy': np.float64(2.29608), 'test_loss_std': np.float64(11792.281671), 'test_loss_bottom_decile': np.float64(87835.721558), 'test_loss_top_decile': np.float64(124355.87915), 'test_loss_min': np.float64(85923.917877), 'test_loss_max': np.float64(124355.87915), 'test_loss_bottom10%': np.float64(85923.917877), 'test_loss_top10%': np.float64(124355.87915), 'test_loss_cos1': np.float64(0.993551), 'test_loss_entropy': np.float64(2.29608), 'val_avg_loss_std': np.float64(2.52517), 'val_avg_loss_bottom_decile': np.float64(16.793805), 'val_avg_loss_top_decile': np.float64(23.916883), 'val_avg_loss_min': np.float64(16.158413), 'val_avg_loss_max': np.float64(23.916883), 'val_avg_loss_bottom10%': np.float64(16.158413), 'val_avg_loss_top10%': np.float64(23.916883), 'val_avg_loss_cos1': np.float64(0.992176), 'val_avg_loss_entropy': np.float64(2.294611), 'val_loss_std': np.float64(13090.483762), 'val_loss_bottom_decile': np.float64(87059.084076), 'val_loss_top_decile': np.float64(123985.120178), 'val_loss_min': np.float64(83765.212341), 'val_loss_max': np.float64(123985.120178), 'val_loss_bottom10%': np.float64(83765.212341), 'val_loss_top10%': np.float64(123985.120178), 'val_loss_cos1': np.float64(0.992176), 'val_loss_entropy': np.float64(2.294611)}}
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2024-10-14 16:08:39,257 (server:353) INFO: Server: Starting evaluation at the end of round 26.
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2024-10-14 16:08:39,257 (server:359) INFO: ----------- Starting a new training round (Round #27) -------------
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2024-10-14 16:11:06,428 (client:354) INFO: {'Role': 'Client #9', 'Round': 27, 'Results_raw': {'train_loss': 18.037843, 'val_loss': 18.059925, 'test_loss': 18.880242}}
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2024-10-14 16:12:02,488 (client:354) INFO: {'Role': 'Client #8', 'Round': 27, 'Results_raw': {'train_loss': 13.423238, 'val_loss': 13.256478, 'test_loss': 13.932408}}
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2024-10-14 16:13:00,225 (client:354) INFO: {'Role': 'Client #4', 'Round': 27, 'Results_raw': {'train_loss': 15.270321, 'val_loss': 15.149879, 'test_loss': 16.026017}}
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2024-10-14 16:14:01,346 (client:354) INFO: {'Role': 'Client #1', 'Round': 27, 'Results_raw': {'train_loss': 10.869506, 'val_loss': 10.756315, 'test_loss': 11.944274}}
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2024-10-14 16:15:04,679 (client:354) INFO: {'Role': 'Client #2', 'Round': 27, 'Results_raw': {'train_loss': 8.822468, 'val_loss': 8.639461, 'test_loss': 9.255811}}
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2024-10-14 16:16:05,105 (client:354) INFO: {'Role': 'Client #3', 'Round': 27, 'Results_raw': {'train_loss': 10.301654, 'val_loss': 10.646244, 'test_loss': 11.897263}}
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2024-10-14 16:17:05,059 (client:354) INFO: {'Role': 'Client #7', 'Round': 27, 'Results_raw': {'train_loss': 15.508453, 'val_loss': 15.500689, 'test_loss': 16.209506}}
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2024-10-14 16:18:05,910 (client:354) INFO: {'Role': 'Client #10', 'Round': 27, 'Results_raw': {'train_loss': 15.182488, 'val_loss': 15.393923, 'test_loss': 16.874624}}
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2024-10-14 16:19:07,555 (client:354) INFO: {'Role': 'Client #6', 'Round': 27, 'Results_raw': {'train_loss': 15.324903, 'val_loss': 15.113603, 'test_loss': 16.209085}}
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2024-10-14 16:20:07,593 (client:354) INFO: {'Role': 'Client #5', 'Round': 27, 'Results_raw': {'train_loss': 16.161849, 'val_loss': 16.634855, 'test_loss': 18.583144}}
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2024-10-14 16:20:07,599 (server:615) INFO: {'Role': 'Server #', 'Round': 26, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.797151), 'test_loss': np.float64(102628.431149), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.946568), 'val_loss': np.float64(103403.008395)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.797151), 'test_loss': np.float64(102628.431149), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.946568), 'val_loss': np.float64(103403.008395)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.366517), 'test_avg_loss_bottom_decile': np.float64(16.633541), 'test_avg_loss_top_decile': np.float64(24.016509), 'test_avg_loss_min': np.float64(16.368961), 'test_avg_loss_max': np.float64(24.016509), 'test_avg_loss_bottom10%': np.float64(16.368961), 'test_avg_loss_top10%': np.float64(24.016509), 'test_avg_loss_cos1': np.float64(0.992931), 'test_avg_loss_entropy': np.float64(2.295445), 'test_loss_std': np.float64(12268.024252), 'test_loss_bottom_decile': np.float64(86228.277985), 'test_loss_top_decile': np.float64(124501.58429), 'test_loss_min': np.float64(84856.691498), 'test_loss_max': np.float64(124501.58429), 'test_loss_bottom10%': np.float64(84856.691498), 'test_loss_top10%': np.float64(124501.58429), 'test_loss_cos1': np.float64(0.992931), 'test_loss_entropy': np.float64(2.295445), 'val_avg_loss_std': np.float64(2.618687), 'val_avg_loss_bottom_decile': np.float64(16.492042), 'val_avg_loss_top_decile': np.float64(23.941757), 'val_avg_loss_min': np.float64(15.967514), 'val_avg_loss_max': np.float64(23.941757), 'val_avg_loss_bottom10%': np.float64(15.967514), 'val_avg_loss_top10%': np.float64(23.941757), 'val_avg_loss_cos1': np.float64(0.991492), 'val_avg_loss_entropy': np.float64(2.293895), 'val_loss_std': np.float64(13575.272415), 'val_loss_bottom_decile': np.float64(85494.747467), 'val_loss_top_decile': np.float64(124114.068481), 'val_loss_min': np.float64(82775.590271), 'val_loss_max': np.float64(124114.068481), 'val_loss_bottom10%': np.float64(82775.590271), 'val_loss_top10%': np.float64(124114.068481), 'val_loss_cos1': np.float64(0.991492), 'val_loss_entropy': np.float64(2.293895)}}
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2024-10-14 16:20:07,652 (server:353) INFO: Server: Starting evaluation at the end of round 27.
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2024-10-14 16:20:07,652 (server:359) INFO: ----------- Starting a new training round (Round #28) -------------
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2024-10-14 16:22:39,948 (client:354) INFO: {'Role': 'Client #8', 'Round': 28, 'Results_raw': {'train_loss': 13.424971, 'val_loss': 13.478991, 'test_loss': 13.985976}}
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2024-10-14 16:23:45,607 (client:354) INFO: {'Role': 'Client #9', 'Round': 28, 'Results_raw': {'train_loss': 18.008497, 'val_loss': 17.84029, 'test_loss': 18.422786}}
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2024-10-14 16:24:45,571 (client:354) INFO: {'Role': 'Client #3', 'Round': 28, 'Results_raw': {'train_loss': 10.292549, 'val_loss': 10.807751, 'test_loss': 12.118247}}
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2024-10-14 16:25:43,010 (client:354) INFO: {'Role': 'Client #4', 'Round': 28, 'Results_raw': {'train_loss': 15.265038, 'val_loss': 15.142041, 'test_loss': 15.997681}}
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2024-10-14 16:26:43,514 (client:354) INFO: {'Role': 'Client #6', 'Round': 28, 'Results_raw': {'train_loss': 15.278659, 'val_loss': 14.962016, 'test_loss': 16.157472}}
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2024-10-14 16:27:47,756 (client:354) INFO: {'Role': 'Client #1', 'Round': 28, 'Results_raw': {'train_loss': 10.838724, 'val_loss': 10.557306, 'test_loss': 11.568768}}
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2024-10-14 16:28:47,685 (client:354) INFO: {'Role': 'Client #2', 'Round': 28, 'Results_raw': {'train_loss': 8.887462, 'val_loss': 8.614241, 'test_loss': 9.273393}}
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2024-10-14 16:29:49,491 (client:354) INFO: {'Role': 'Client #10', 'Round': 28, 'Results_raw': {'train_loss': 15.163386, 'val_loss': 15.424145, 'test_loss': 16.528112}}
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2024-10-14 16:30:46,216 (client:354) INFO: {'Role': 'Client #7', 'Round': 28, 'Results_raw': {'train_loss': 15.474837, 'val_loss': 15.478258, 'test_loss': 16.124551}}
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2024-10-14 16:31:51,098 (client:354) INFO: {'Role': 'Client #5', 'Round': 28, 'Results_raw': {'train_loss': 16.12319, 'val_loss': 16.59738, 'test_loss': 18.638935}}
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2024-10-14 16:31:51,103 (server:615) INFO: {'Role': 'Server #', 'Round': 27, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.795511), 'test_loss': np.float64(102619.926517), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.919255), 'val_loss': np.float64(103261.418048)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.795511), 'test_loss': np.float64(102619.926517), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.919255), 'val_loss': np.float64(103261.418048)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.271384), 'test_avg_loss_bottom_decile': np.float64(16.836543), 'test_avg_loss_top_decile': np.float64(23.852298), 'test_avg_loss_min': np.float64(16.397529), 'test_avg_loss_max': np.float64(23.852298), 'test_avg_loss_bottom10%': np.float64(16.397529), 'test_avg_loss_top10%': np.float64(23.852298), 'test_avg_loss_cos1': np.float64(0.993481), 'test_avg_loss_entropy': np.float64(2.296007), 'test_loss_std': np.float64(11774.855778), 'test_loss_bottom_decile': np.float64(87280.641022), 'test_loss_top_decile': np.float64(123650.313538), 'test_loss_min': np.float64(85004.7901), 'test_loss_max': np.float64(123650.313538), 'test_loss_bottom10%': np.float64(85004.7901), 'test_loss_top10%': np.float64(123650.313538), 'test_loss_cos1': np.float64(0.993481), 'test_loss_entropy': np.float64(2.296007), 'val_avg_loss_std': np.float64(2.513949), 'val_avg_loss_bottom_decile': np.float64(16.667266), 'val_avg_loss_top_decile': np.float64(23.732602), 'val_avg_loss_min': np.float64(15.980665), 'val_avg_loss_max': np.float64(23.732602), 'val_avg_loss_bottom10%': np.float64(15.980665), 'val_avg_loss_top10%': np.float64(23.732602), 'val_avg_loss_cos1': np.float64(0.99213), 'val_avg_loss_entropy': np.float64(2.294557), 'val_loss_std': np.float64(13032.313913), 'val_loss_bottom_decile': np.float64(86403.105774), 'val_loss_top_decile': np.float64(123029.810669), 'val_loss_min': np.float64(82843.767395), 'val_loss_max': np.float64(123029.810669), 'val_loss_bottom10%': np.float64(82843.767395), 'val_loss_top10%': np.float64(123029.810669), 'val_loss_cos1': np.float64(0.99213), 'val_loss_entropy': np.float64(2.294557)}}
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2024-10-14 16:31:51,152 (server:353) INFO: Server: Starting evaluation at the end of round 28.
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2024-10-14 16:31:51,153 (server:359) INFO: ----------- Starting a new training round (Round #29) -------------
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2024-10-14 16:34:21,326 (client:354) INFO: {'Role': 'Client #1', 'Round': 29, 'Results_raw': {'train_loss': 10.877397, 'val_loss': 10.670401, 'test_loss': 11.69679}}
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2024-10-14 16:35:20,152 (client:354) INFO: {'Role': 'Client #5', 'Round': 29, 'Results_raw': {'train_loss': 16.10272, 'val_loss': 16.79087, 'test_loss': 18.90692}}
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2024-10-14 16:36:15,651 (client:354) INFO: {'Role': 'Client #7', 'Round': 29, 'Results_raw': {'train_loss': 15.464315, 'val_loss': 15.627201, 'test_loss': 16.204309}}
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2024-10-14 16:37:08,750 (client:354) INFO: {'Role': 'Client #6', 'Round': 29, 'Results_raw': {'train_loss': 15.211031, 'val_loss': 15.035249, 'test_loss': 16.39951}}
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2024-10-14 16:38:03,736 (client:354) INFO: {'Role': 'Client #3', 'Round': 29, 'Results_raw': {'train_loss': 10.205536, 'val_loss': 10.739701, 'test_loss': 12.028948}}
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2024-10-14 16:39:02,370 (client:354) INFO: {'Role': 'Client #4', 'Round': 29, 'Results_raw': {'train_loss': 15.245341, 'val_loss': 15.187423, 'test_loss': 16.21854}}
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2024-10-14 16:40:04,017 (client:354) INFO: {'Role': 'Client #9', 'Round': 29, 'Results_raw': {'train_loss': 17.951137, 'val_loss': 17.935223, 'test_loss': 18.483128}}
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2024-10-14 16:41:02,336 (client:354) INFO: {'Role': 'Client #10', 'Round': 29, 'Results_raw': {'train_loss': 15.110629, 'val_loss': 15.449798, 'test_loss': 17.101342}}
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2024-10-14 16:42:02,434 (client:354) INFO: {'Role': 'Client #2', 'Round': 29, 'Results_raw': {'train_loss': 8.842626, 'val_loss': 8.499251, 'test_loss': 9.032047}}
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2024-10-14 16:43:02,036 (client:354) INFO: {'Role': 'Client #8', 'Round': 29, 'Results_raw': {'train_loss': 13.40759, 'val_loss': 13.344602, 'test_loss': 13.916338}}
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2024-10-14 16:43:02,041 (server:615) INFO: {'Role': 'Server #', 'Round': 28, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.897298), 'test_loss': np.float64(103147.593887), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.037936), 'val_loss': np.float64(103876.661304)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.897298), 'test_loss': np.float64(103147.593887), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(20.037936), 'val_loss': np.float64(103876.661304)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.344626), 'test_avg_loss_bottom_decile': np.float64(16.859135), 'test_avg_loss_top_decile': np.float64(24.004241), 'test_avg_loss_min': np.float64(16.344549), 'test_avg_loss_max': np.float64(24.004241), 'test_avg_loss_bottom10%': np.float64(16.344549), 'test_avg_loss_top10%': np.float64(24.004241), 'test_avg_loss_cos1': np.float64(0.993129), 'test_avg_loss_entropy': np.float64(2.295638), 'test_loss_std': np.float64(12154.54041), 'test_loss_bottom_decile': np.float64(87397.75824), 'test_loss_top_decile': np.float64(124437.986755), 'test_loss_min': np.float64(84730.139679), 'test_loss_max': np.float64(124437.986755), 'test_loss_bottom10%': np.float64(84730.139679), 'test_loss_top10%': np.float64(124437.986755), 'test_loss_cos1': np.float64(0.993129), 'test_loss_entropy': np.float64(2.295638), 'val_avg_loss_std': np.float64(2.598321), 'val_avg_loss_bottom_decile': np.float64(16.661765), 'val_avg_loss_top_decile': np.float64(23.881957), 'val_avg_loss_min': np.float64(15.94599), 'val_avg_loss_max': np.float64(23.881957), 'val_avg_loss_bottom10%': np.float64(15.94599), 'val_avg_loss_top10%': np.float64(23.881957), 'val_avg_loss_cos1': np.float64(0.991697), 'val_avg_loss_entropy': np.float64(2.294097), 'val_loss_std': np.float64(13469.695767), 'val_loss_bottom_decile': np.float64(86374.592224), 'val_loss_top_decile': np.float64(123804.065369), 'val_loss_min': np.float64(82664.010559), 'val_loss_max': np.float64(123804.065369), 'val_loss_bottom10%': np.float64(82664.010559), 'val_loss_top10%': np.float64(123804.065369), 'val_loss_cos1': np.float64(0.991697), 'val_loss_entropy': np.float64(2.294097)}}
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2024-10-14 16:43:02,081 (server:353) INFO: Server: Starting evaluation at the end of round 29.
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2024-10-14 16:43:02,081 (server:359) INFO: ----------- Starting a new training round (Round #30) -------------
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2024-10-14 16:45:33,967 (client:354) INFO: {'Role': 'Client #7', 'Round': 30, 'Results_raw': {'train_loss': 15.457126, 'val_loss': 15.613419, 'test_loss': 16.140769}}
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2024-10-14 16:46:31,274 (client:354) INFO: {'Role': 'Client #6', 'Round': 30, 'Results_raw': {'train_loss': 15.186945, 'val_loss': 15.022204, 'test_loss': 16.246524}}
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2024-10-14 16:47:27,138 (client:354) INFO: {'Role': 'Client #10', 'Round': 30, 'Results_raw': {'train_loss': 15.124121, 'val_loss': 15.388091, 'test_loss': 16.98767}}
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2024-10-14 16:48:28,087 (client:354) INFO: {'Role': 'Client #8', 'Round': 30, 'Results_raw': {'train_loss': 13.386906, 'val_loss': 13.227038, 'test_loss': 13.962221}}
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2024-10-14 16:49:23,606 (client:354) INFO: {'Role': 'Client #3', 'Round': 30, 'Results_raw': {'train_loss': 10.190454, 'val_loss': 10.761947, 'test_loss': 12.039427}}
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2024-10-14 16:50:23,625 (client:354) INFO: {'Role': 'Client #2', 'Round': 30, 'Results_raw': {'train_loss': 8.882368, 'val_loss': 8.803368, 'test_loss': 9.458357}}
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2024-10-14 16:51:23,816 (client:354) INFO: {'Role': 'Client #5', 'Round': 30, 'Results_raw': {'train_loss': 16.086939, 'val_loss': 16.728508, 'test_loss': 18.929038}}
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2024-10-14 16:52:26,827 (client:354) INFO: {'Role': 'Client #4', 'Round': 30, 'Results_raw': {'train_loss': 15.246969, 'val_loss': 15.134124, 'test_loss': 16.088478}}
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2024-10-14 16:53:25,612 (client:354) INFO: {'Role': 'Client #9', 'Round': 30, 'Results_raw': {'train_loss': 17.94396, 'val_loss': 17.781752, 'test_loss': 18.437075}}
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2024-10-14 16:54:24,335 (client:354) INFO: {'Role': 'Client #1', 'Round': 30, 'Results_raw': {'train_loss': 10.820121, 'val_loss': 10.54043, 'test_loss': 11.472243}}
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2024-10-14 16:54:24,338 (server:615) INFO: {'Role': 'Server #', 'Round': 29, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.884287), 'test_loss': np.float64(103080.144809), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.990073), 'val_loss': np.float64(103628.539706)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.884287), 'test_loss': np.float64(103080.144809), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.990073), 'val_loss': np.float64(103628.539706)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.213865), 'test_avg_loss_bottom_decile': np.float64(17.206106), 'test_avg_loss_top_decile': np.float64(23.896297), 'test_avg_loss_min': np.float64(16.446276), 'test_avg_loss_max': np.float64(23.896297), 'test_avg_loss_bottom10%': np.float64(16.446276), 'test_avg_loss_top10%': np.float64(23.896297), 'test_avg_loss_cos1': np.float64(0.993859), 'test_avg_loss_entropy': np.float64(2.296398), 'test_loss_std': np.float64(11476.677231), 'test_loss_bottom_decile': np.float64(89196.45285), 'test_loss_top_decile': np.float64(123878.40448), 'test_loss_min': np.float64(85257.494781), 'test_loss_max': np.float64(123878.40448), 'test_loss_bottom10%': np.float64(85257.494781), 'test_loss_top10%': np.float64(123878.40448), 'test_loss_cos1': np.float64(0.993859), 'test_loss_entropy': np.float64(2.296398), 'val_avg_loss_std': np.float64(2.452662), 'val_avg_loss_bottom_decile': np.float64(17.007844), 'val_avg_loss_top_decile': np.float64(23.731666), 'val_avg_loss_min': np.float64(16.012156), 'val_avg_loss_max': np.float64(23.731666), 'val_avg_loss_bottom10%': np.float64(16.012156), 'val_avg_loss_top10%': np.float64(23.731666), 'val_avg_loss_cos1': np.float64(0.992557), 'val_avg_loss_entropy': np.float64(2.295003), 'val_loss_std': np.float64(12714.597664), 'val_loss_bottom_decile': np.float64(88168.663605), 'val_loss_top_decile': np.float64(123024.956116), 'val_loss_min': np.float64(83007.016541), 'val_loss_max': np.float64(123024.956116), 'val_loss_bottom10%': np.float64(83007.016541), 'val_loss_top10%': np.float64(123024.956116), 'val_loss_cos1': np.float64(0.992557), 'val_loss_entropy': np.float64(2.295003)}}
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2024-10-14 16:54:24,375 (server:353) INFO: Server: Starting evaluation at the end of round 30.
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2024-10-14 16:54:24,375 (server:359) INFO: ----------- Starting a new training round (Round #31) -------------
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2024-10-14 16:56:59,807 (client:354) INFO: {'Role': 'Client #4', 'Round': 31, 'Results_raw': {'train_loss': 15.165786, 'val_loss': 15.192804, 'test_loss': 16.12161}}
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2024-10-14 16:57:58,707 (client:354) INFO: {'Role': 'Client #2', 'Round': 31, 'Results_raw': {'train_loss': 8.764713, 'val_loss': 8.553448, 'test_loss': 9.002677}}
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2024-10-14 16:58:56,367 (client:354) INFO: {'Role': 'Client #5', 'Round': 31, 'Results_raw': {'train_loss': 16.042964, 'val_loss': 16.74688, 'test_loss': 18.727039}}
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2024-10-14 16:59:55,584 (client:354) INFO: {'Role': 'Client #10', 'Round': 31, 'Results_raw': {'train_loss': 15.083029, 'val_loss': 15.436676, 'test_loss': 16.864608}}
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2024-10-14 17:00:54,567 (client:354) INFO: {'Role': 'Client #3', 'Round': 31, 'Results_raw': {'train_loss': 10.253459, 'val_loss': 10.80071, 'test_loss': 12.195661}}
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2024-10-14 17:01:55,349 (client:354) INFO: {'Role': 'Client #9', 'Round': 31, 'Results_raw': {'train_loss': 17.916546, 'val_loss': 17.8085, 'test_loss': 18.725226}}
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2024-10-14 17:02:54,196 (client:354) INFO: {'Role': 'Client #1', 'Round': 31, 'Results_raw': {'train_loss': 10.763293, 'val_loss': 10.540791, 'test_loss': 11.555338}}
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2024-10-14 17:03:51,586 (client:354) INFO: {'Role': 'Client #6', 'Round': 31, 'Results_raw': {'train_loss': 15.200335, 'val_loss': 15.017169, 'test_loss': 16.436448}}
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2024-10-14 17:05:08,652 (client:354) INFO: {'Role': 'Client #7', 'Round': 31, 'Results_raw': {'train_loss': 15.44506, 'val_loss': 15.682839, 'test_loss': 16.29895}}
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2024-10-14 17:06:09,376 (client:354) INFO: {'Role': 'Client #8', 'Round': 31, 'Results_raw': {'train_loss': 13.332756, 'val_loss': 13.31981, 'test_loss': 14.148364}}
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2024-10-14 17:06:09,381 (server:615) INFO: {'Role': 'Server #', 'Round': 30, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.771818), 'test_loss': np.float64(102497.102823), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.87419), 'val_loss': np.float64(103027.800687)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.771818), 'test_loss': np.float64(102497.102823), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.87419), 'val_loss': np.float64(103027.800687)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.197676), 'test_avg_loss_bottom_decile': np.float64(17.123713), 'test_avg_loss_top_decile': np.float64(23.777286), 'test_avg_loss_min': np.float64(16.412824), 'test_avg_loss_max': np.float64(23.777286), 'test_avg_loss_bottom10%': np.float64(16.412824), 'test_avg_loss_top10%': np.float64(23.777286), 'test_avg_loss_cos1': np.float64(0.993879), 'test_avg_loss_entropy': np.float64(2.296424), 'test_loss_std': np.float64(11392.750707), 'test_loss_bottom_decile': np.float64(88769.326019), 'test_loss_top_decile': np.float64(123261.452759), 'test_loss_min': np.float64(85084.078247), 'test_loss_max': np.float64(123261.452759), 'test_loss_bottom10%': np.float64(85084.078247), 'test_loss_top10%': np.float64(123261.452759), 'test_loss_cos1': np.float64(0.993879), 'test_loss_entropy': np.float64(2.296424), 'val_avg_loss_std': np.float64(2.42592), 'val_avg_loss_bottom_decile': np.float64(16.928409), 'val_avg_loss_top_decile': np.float64(23.619022), 'val_avg_loss_min': np.float64(15.990561), 'val_avg_loss_max': np.float64(23.619022), 'val_avg_loss_bottom10%': np.float64(15.990561), 'val_avg_loss_top10%': np.float64(23.619022), 'val_avg_loss_cos1': np.float64(0.992632), 'val_avg_loss_entropy': np.float64(2.295086), 'val_loss_std': np.float64(12575.970236), 'val_loss_bottom_decile': np.float64(87756.871185), 'val_loss_top_decile': np.float64(122441.012512), 'val_loss_min': np.float64(82895.070587), 'val_loss_max': np.float64(122441.012512), 'val_loss_bottom10%': np.float64(82895.070587), 'val_loss_top10%': np.float64(122441.012512), 'val_loss_cos1': np.float64(0.992632), 'val_loss_entropy': np.float64(2.295086)}}
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2024-10-14 17:06:09,428 (server:353) INFO: Server: Starting evaluation at the end of round 31.
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2024-10-14 17:06:09,429 (server:359) INFO: ----------- Starting a new training round (Round #32) -------------
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2024-10-14 17:08:39,879 (client:354) INFO: {'Role': 'Client #5', 'Round': 32, 'Results_raw': {'train_loss': 16.038697, 'val_loss': 16.625633, 'test_loss': 18.638969}}
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2024-10-14 17:09:39,182 (client:354) INFO: {'Role': 'Client #10', 'Round': 32, 'Results_raw': {'train_loss': 15.054562, 'val_loss': 15.448223, 'test_loss': 16.891653}}
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2024-10-14 17:10:38,642 (client:354) INFO: {'Role': 'Client #3', 'Round': 32, 'Results_raw': {'train_loss': 10.198281, 'val_loss': 10.568418, 'test_loss': 11.806325}}
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2024-10-14 17:11:37,577 (client:354) INFO: {'Role': 'Client #2', 'Round': 32, 'Results_raw': {'train_loss': 8.86433, 'val_loss': 8.607801, 'test_loss': 9.268638}}
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2024-10-14 17:12:39,767 (client:354) INFO: {'Role': 'Client #7', 'Round': 32, 'Results_raw': {'train_loss': 15.382114, 'val_loss': 15.478716, 'test_loss': 16.066027}}
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2024-10-14 17:13:42,865 (client:354) INFO: {'Role': 'Client #8', 'Round': 32, 'Results_raw': {'train_loss': 13.331895, 'val_loss': 13.290546, 'test_loss': 13.891631}}
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2024-10-14 17:14:43,585 (client:354) INFO: {'Role': 'Client #9', 'Round': 32, 'Results_raw': {'train_loss': 17.888033, 'val_loss': 17.875336, 'test_loss': 18.707528}}
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2024-10-14 17:15:44,678 (client:354) INFO: {'Role': 'Client #1', 'Round': 32, 'Results_raw': {'train_loss': 10.818098, 'val_loss': 10.424801, 'test_loss': 11.438929}}
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2024-10-14 17:16:41,164 (client:354) INFO: {'Role': 'Client #4', 'Round': 32, 'Results_raw': {'train_loss': 15.161361, 'val_loss': 15.147416, 'test_loss': 16.147984}}
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2024-10-14 17:17:41,727 (client:354) INFO: {'Role': 'Client #6', 'Round': 32, 'Results_raw': {'train_loss': 15.159243, 'val_loss': 14.942254, 'test_loss': 16.260663}}
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2024-10-14 17:17:41,731 (server:615) INFO: {'Role': 'Server #', 'Round': 31, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.833656), 'test_loss': np.float64(102817.670209), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.930402), 'val_loss': np.float64(103319.202591)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.833656), 'test_loss': np.float64(102817.670209), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.930402), 'val_loss': np.float64(103319.202591)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.189046), 'test_avg_loss_bottom_decile': np.float64(17.21582), 'test_avg_loss_top_decile': np.float64(23.902541), 'test_avg_loss_min': np.float64(16.521905), 'test_avg_loss_max': np.float64(23.902541), 'test_avg_loss_bottom10%': np.float64(16.521905), 'test_avg_loss_top10%': np.float64(23.902541), 'test_avg_loss_cos1': np.float64(0.993964), 'test_avg_loss_entropy': np.float64(2.296521), 'test_loss_std': np.float64(11348.015281), 'test_loss_bottom_decile': np.float64(89246.809479), 'test_loss_top_decile': np.float64(123910.772095), 'test_loss_min': np.float64(85649.554443), 'test_loss_max': np.float64(123910.772095), 'test_loss_bottom10%': np.float64(85649.554443), 'test_loss_top10%': np.float64(123910.772095), 'test_loss_cos1': np.float64(0.993964), 'test_loss_entropy': np.float64(2.296521), 'val_avg_loss_std': np.float64(2.422912), 'val_avg_loss_bottom_decile': np.float64(17.024808), 'val_avg_loss_top_decile': np.float64(23.747377), 'val_avg_loss_min': np.float64(16.08683), 'val_avg_loss_max': np.float64(23.747377), 'val_avg_loss_bottom10%': np.float64(16.08683), 'val_avg_loss_top10%': np.float64(23.747377), 'val_avg_loss_cos1': np.float64(0.992691), 'val_avg_loss_entropy': np.float64(2.295159), 'val_loss_std': np.float64(12560.377258), 'val_loss_bottom_decile': np.float64(88256.605133), 'val_loss_top_decile': np.float64(123106.40033), 'val_loss_min': np.float64(83394.128479), 'val_loss_max': np.float64(123106.40033), 'val_loss_bottom10%': np.float64(83394.128479), 'val_loss_top10%': np.float64(123106.40033), 'val_loss_cos1': np.float64(0.992691), 'val_loss_entropy': np.float64(2.295159)}}
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2024-10-14 17:17:41,771 (server:353) INFO: Server: Starting evaluation at the end of round 32.
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2024-10-14 17:17:41,772 (server:359) INFO: ----------- Starting a new training round (Round #33) -------------
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2024-10-14 17:20:20,410 (client:354) INFO: {'Role': 'Client #9', 'Round': 33, 'Results_raw': {'train_loss': 17.874272, 'val_loss': 17.774344, 'test_loss': 18.493573}}
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2024-10-14 17:21:19,366 (client:354) INFO: {'Role': 'Client #8', 'Round': 33, 'Results_raw': {'train_loss': 13.307457, 'val_loss': 13.482528, 'test_loss': 14.196116}}
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2024-10-14 17:22:16,667 (client:354) INFO: {'Role': 'Client #7', 'Round': 33, 'Results_raw': {'train_loss': 15.383376, 'val_loss': 15.486311, 'test_loss': 16.073459}}
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2024-10-14 17:23:17,301 (client:354) INFO: {'Role': 'Client #10', 'Round': 33, 'Results_raw': {'train_loss': 15.077187, 'val_loss': 15.383056, 'test_loss': 16.995529}}
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2024-10-14 17:24:16,986 (client:354) INFO: {'Role': 'Client #1', 'Round': 33, 'Results_raw': {'train_loss': 10.740029, 'val_loss': 10.608805, 'test_loss': 11.637087}}
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2024-10-14 17:25:17,080 (client:354) INFO: {'Role': 'Client #5', 'Round': 33, 'Results_raw': {'train_loss': 16.024737, 'val_loss': 16.77862, 'test_loss': 18.997677}}
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2024-10-14 17:26:17,779 (client:354) INFO: {'Role': 'Client #3', 'Round': 33, 'Results_raw': {'train_loss': 10.163984, 'val_loss': 10.657193, 'test_loss': 12.022817}}
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2024-10-14 17:27:15,518 (client:354) INFO: {'Role': 'Client #6', 'Round': 33, 'Results_raw': {'train_loss': 15.100715, 'val_loss': 14.996072, 'test_loss': 16.588474}}
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2024-10-14 17:28:14,594 (client:354) INFO: {'Role': 'Client #2', 'Round': 33, 'Results_raw': {'train_loss': 8.778187, 'val_loss': 8.66353, 'test_loss': 9.221747}}
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2024-10-14 17:29:19,542 (client:354) INFO: {'Role': 'Client #4', 'Round': 33, 'Results_raw': {'train_loss': 15.213651, 'val_loss': 15.183323, 'test_loss': 16.293427}}
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2024-10-14 17:29:19,546 (server:615) INFO: {'Role': 'Server #', 'Round': 32, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.734482), 'test_loss': np.float64(102303.554929), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.851343), 'val_loss': np.float64(102909.360098)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.734482), 'test_loss': np.float64(102303.554929), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.851343), 'val_loss': np.float64(102909.360098)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.284125), 'test_avg_loss_bottom_decile': np.float64(16.756292), 'test_avg_loss_top_decile': np.float64(23.821216), 'test_avg_loss_min': np.float64(16.324171), 'test_avg_loss_max': np.float64(23.821216), 'test_avg_loss_bottom10%': np.float64(16.324171), 'test_avg_loss_top10%': np.float64(23.821216), 'test_avg_loss_cos1': np.float64(0.993368), 'test_avg_loss_entropy': np.float64(2.295891), 'test_loss_std': np.float64(11840.905974), 'test_loss_bottom_decile': np.float64(86864.615173), 'test_loss_top_decile': np.float64(123489.181763), 'test_loss_min': np.float64(84624.504211), 'test_loss_max': np.float64(123489.181763), 'test_loss_bottom10%': np.float64(84624.504211), 'test_loss_top10%': np.float64(123489.181763), 'test_loss_cos1': np.float64(0.993368), 'test_loss_entropy': np.float64(2.295891), 'val_avg_loss_std': np.float64(2.532236), 'val_avg_loss_bottom_decile': np.float64(16.56397), 'val_avg_loss_top_decile': np.float64(23.694578), 'val_avg_loss_min': np.float64(15.899769), 'val_avg_loss_max': np.float64(23.694578), 'val_avg_loss_bottom10%': np.float64(15.899769), 'val_avg_loss_top10%': np.float64(23.694578), 'val_avg_loss_cos1': np.float64(0.991962), 'val_avg_loss_entropy': np.float64(2.294381), 'val_loss_std': np.float64(13127.113585), 'val_loss_bottom_decile': np.float64(85867.621307), 'val_loss_top_decile': np.float64(122832.690491), 'val_loss_min': np.float64(82424.401154), 'val_loss_max': np.float64(122832.690491), 'val_loss_bottom10%': np.float64(82424.401154), 'val_loss_top10%': np.float64(122832.690491), 'val_loss_cos1': np.float64(0.991962), 'val_loss_entropy': np.float64(2.294381)}}
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2024-10-14 17:29:19,586 (server:353) INFO: Server: Starting evaluation at the end of round 33.
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2024-10-14 17:29:19,586 (server:359) INFO: ----------- Starting a new training round (Round #34) -------------
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2024-10-14 17:31:53,333 (client:354) INFO: {'Role': 'Client #4', 'Round': 34, 'Results_raw': {'train_loss': 15.127682, 'val_loss': 15.15303, 'test_loss': 15.879948}}
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2024-10-14 17:32:51,229 (client:354) INFO: {'Role': 'Client #7', 'Round': 34, 'Results_raw': {'train_loss': 15.365766, 'val_loss': 15.443545, 'test_loss': 16.176966}}
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2024-10-14 17:33:50,572 (client:354) INFO: {'Role': 'Client #8', 'Round': 34, 'Results_raw': {'train_loss': 13.270568, 'val_loss': 13.292762, 'test_loss': 14.045531}}
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2024-10-14 17:34:49,402 (client:354) INFO: {'Role': 'Client #10', 'Round': 34, 'Results_raw': {'train_loss': 15.058841, 'val_loss': 15.590524, 'test_loss': 17.329449}}
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2024-10-14 17:35:45,096 (client:354) INFO: {'Role': 'Client #5', 'Round': 34, 'Results_raw': {'train_loss': 15.981169, 'val_loss': 16.631875, 'test_loss': 18.932258}}
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2024-10-14 17:36:44,679 (client:354) INFO: {'Role': 'Client #2', 'Round': 34, 'Results_raw': {'train_loss': 8.76174, 'val_loss': 8.515986, 'test_loss': 9.067213}}
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2024-10-14 17:37:46,479 (client:354) INFO: {'Role': 'Client #9', 'Round': 34, 'Results_raw': {'train_loss': 17.863276, 'val_loss': 17.711024, 'test_loss': 18.635155}}
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2024-10-14 17:38:43,357 (client:354) INFO: {'Role': 'Client #3', 'Round': 34, 'Results_raw': {'train_loss': 10.157618, 'val_loss': 10.586132, 'test_loss': 11.988958}}
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2024-10-14 17:39:41,933 (client:354) INFO: {'Role': 'Client #1', 'Round': 34, 'Results_raw': {'train_loss': 10.73819, 'val_loss': 10.592155, 'test_loss': 11.592138}}
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2024-10-14 17:40:41,812 (client:354) INFO: {'Role': 'Client #6', 'Round': 34, 'Results_raw': {'train_loss': 15.096398, 'val_loss': 14.943238, 'test_loss': 16.337111}}
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2024-10-14 17:40:41,816 (server:615) INFO: {'Role': 'Server #', 'Round': 33, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.678608), 'test_loss': np.float64(102013.905151), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.758452), 'val_loss': np.float64(102427.816467)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.678608), 'test_loss': np.float64(102013.905151), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.758452), 'val_loss': np.float64(102427.816467)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.21194), 'test_avg_loss_bottom_decile': np.float64(16.874033), 'test_avg_loss_top_decile': np.float64(23.729311), 'test_avg_loss_min': np.float64(16.401113), 'test_avg_loss_max': np.float64(23.729311), 'test_avg_loss_bottom10%': np.float64(16.401113), 'test_avg_loss_top10%': np.float64(23.729311), 'test_avg_loss_cos1': np.float64(0.993742), 'test_avg_loss_entropy': np.float64(2.296285), 'test_loss_std': np.float64(11466.698096), 'test_loss_bottom_decile': np.float64(87474.985168), 'test_loss_top_decile': np.float64(123012.750366), 'test_loss_min': np.float64(85023.368439), 'test_loss_max': np.float64(123012.750366), 'test_loss_bottom10%': np.float64(85023.368439), 'test_loss_top10%': np.float64(123012.750366), 'test_loss_cos1': np.float64(0.993742), 'test_loss_entropy': np.float64(2.296285), 'val_avg_loss_std': np.float64(2.429741), 'val_avg_loss_bottom_decile': np.float64(16.693741), 'val_avg_loss_top_decile': np.float64(23.535083), 'val_avg_loss_min': np.float64(15.977005), 'val_avg_loss_max': np.float64(23.535083), 'val_avg_loss_bottom10%': np.float64(15.977005), 'val_avg_loss_top10%': np.float64(23.535083), 'val_avg_loss_cos1': np.float64(0.992524), 'val_avg_loss_entropy': np.float64(2.294976), 'val_loss_std': np.float64(12595.779534), 'val_loss_bottom_decile': np.float64(86540.351501), 'val_loss_top_decile': np.float64(122005.872437), 'val_loss_min': np.float64(82824.796509), 'val_loss_max': np.float64(122005.872437), 'val_loss_bottom10%': np.float64(82824.796509), 'val_loss_top10%': np.float64(122005.872437), 'val_loss_cos1': np.float64(0.992524), 'val_loss_entropy': np.float64(2.294976)}}
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2024-10-14 17:40:41,853 (server:353) INFO: Server: Starting evaluation at the end of round 34.
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2024-10-14 17:40:41,853 (server:359) INFO: ----------- Starting a new training round (Round #35) -------------
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2024-10-14 17:43:21,441 (client:354) INFO: {'Role': 'Client #3', 'Round': 35, 'Results_raw': {'train_loss': 10.158775, 'val_loss': 10.815194, 'test_loss': 11.99071}}
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2024-10-14 17:44:20,421 (client:354) INFO: {'Role': 'Client #9', 'Round': 35, 'Results_raw': {'train_loss': 17.815499, 'val_loss': 18.015673, 'test_loss': 19.055736}}
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2024-10-14 17:45:18,936 (client:354) INFO: {'Role': 'Client #2', 'Round': 35, 'Results_raw': {'train_loss': 8.744266, 'val_loss': 8.547665, 'test_loss': 9.048374}}
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2024-10-14 17:46:24,174 (client:354) INFO: {'Role': 'Client #8', 'Round': 35, 'Results_raw': {'train_loss': 13.341101, 'val_loss': 13.44571, 'test_loss': 13.916117}}
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2024-10-14 17:47:22,846 (client:354) INFO: {'Role': 'Client #5', 'Round': 35, 'Results_raw': {'train_loss': 15.987469, 'val_loss': 16.759375, 'test_loss': 18.863743}}
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2024-10-14 17:48:21,195 (client:354) INFO: {'Role': 'Client #4', 'Round': 35, 'Results_raw': {'train_loss': 15.135645, 'val_loss': 15.070233, 'test_loss': 16.15496}}
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2024-10-14 17:49:17,997 (client:354) INFO: {'Role': 'Client #6', 'Round': 35, 'Results_raw': {'train_loss': 15.107976, 'val_loss': 14.977102, 'test_loss': 16.20406}}
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2024-10-14 17:50:14,498 (client:354) INFO: {'Role': 'Client #7', 'Round': 35, 'Results_raw': {'train_loss': 15.337558, 'val_loss': 15.452313, 'test_loss': 16.091523}}
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2024-10-14 17:51:13,219 (client:354) INFO: {'Role': 'Client #10', 'Round': 35, 'Results_raw': {'train_loss': 15.024002, 'val_loss': 15.355509, 'test_loss': 16.766755}}
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2024-10-14 17:52:12,029 (client:354) INFO: {'Role': 'Client #1', 'Round': 35, 'Results_raw': {'train_loss': 10.709409, 'val_loss': 10.449653, 'test_loss': 11.519549}}
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2024-10-14 17:52:12,034 (server:615) INFO: {'Role': 'Server #', 'Round': 34, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.696119), 'test_loss': np.float64(102104.682635), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.773635), 'val_loss': np.float64(102506.523883)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.696119), 'test_loss': np.float64(102104.682635), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.773635), 'val_loss': np.float64(102506.523883)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.236208), 'test_avg_loss_bottom_decile': np.float64(16.977675), 'test_avg_loss_top_decile': np.float64(23.828052), 'test_avg_loss_min': np.float64(16.320701), 'test_avg_loss_max': np.float64(23.828052), 'test_avg_loss_bottom10%': np.float64(16.320701), 'test_avg_loss_top10%': np.float64(23.828052), 'test_avg_loss_cos1': np.float64(0.993616), 'test_avg_loss_entropy': np.float64(2.296162), 'test_loss_std': np.float64(11592.504702), 'test_loss_bottom_decile': np.float64(88012.269409), 'test_loss_top_decile': np.float64(123524.622559), 'test_loss_min': np.float64(84606.512665), 'test_loss_max': np.float64(123524.622559), 'test_loss_bottom10%': np.float64(84606.512665), 'test_loss_top10%': np.float64(123524.622559), 'test_loss_cos1': np.float64(0.993616), 'test_loss_entropy': np.float64(2.296162), 'val_avg_loss_std': np.float64(2.462699), 'val_avg_loss_bottom_decile': np.float64(16.767332), 'val_avg_loss_top_decile': np.float64(23.648713), 'val_avg_loss_min': np.float64(15.885745), 'val_avg_loss_max': np.float64(23.648713), 'val_avg_loss_bottom10%': np.float64(15.885745), 'val_avg_loss_top10%': np.float64(23.648713), 'val_avg_loss_cos1': np.float64(0.992333), 'val_avg_loss_entropy': np.float64(2.294784), 'val_loss_std': np.float64(12766.633116), 'val_loss_bottom_decile': np.float64(86921.85144), 'val_loss_top_decile': np.float64(122594.92981), 'val_loss_min': np.float64(82351.702789), 'val_loss_max': np.float64(122594.92981), 'val_loss_bottom10%': np.float64(82351.702789), 'val_loss_top10%': np.float64(122594.92981), 'val_loss_cos1': np.float64(0.992333), 'val_loss_entropy': np.float64(2.294784)}}
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2024-10-14 17:52:12,086 (server:353) INFO: Server: Starting evaluation at the end of round 35.
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2024-10-14 17:52:12,086 (server:359) INFO: ----------- Starting a new training round (Round #36) -------------
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2024-10-14 17:54:46,429 (client:354) INFO: {'Role': 'Client #9', 'Round': 36, 'Results_raw': {'train_loss': 17.824656, 'val_loss': 17.738084, 'test_loss': 18.433854}}
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2024-10-14 17:55:55,398 (client:354) INFO: {'Role': 'Client #6', 'Round': 36, 'Results_raw': {'train_loss': 15.082295, 'val_loss': 15.03804, 'test_loss': 16.455678}}
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2024-10-14 17:56:53,533 (client:354) INFO: {'Role': 'Client #8', 'Round': 36, 'Results_raw': {'train_loss': 13.254431, 'val_loss': 13.284607, 'test_loss': 14.039811}}
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2024-10-14 17:57:51,650 (client:354) INFO: {'Role': 'Client #5', 'Round': 36, 'Results_raw': {'train_loss': 15.928894, 'val_loss': 16.724686, 'test_loss': 19.12519}}
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2024-10-14 17:58:50,642 (client:354) INFO: {'Role': 'Client #7', 'Round': 36, 'Results_raw': {'train_loss': 15.313691, 'val_loss': 15.467679, 'test_loss': 16.159001}}
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2024-10-14 17:59:49,629 (client:354) INFO: {'Role': 'Client #4', 'Round': 36, 'Results_raw': {'train_loss': 15.133778, 'val_loss': 15.101506, 'test_loss': 16.107829}}
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2024-10-14 18:00:49,607 (client:354) INFO: {'Role': 'Client #3', 'Round': 36, 'Results_raw': {'train_loss': 10.157637, 'val_loss': 10.758752, 'test_loss': 12.189218}}
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2024-10-14 18:01:48,031 (client:354) INFO: {'Role': 'Client #2', 'Round': 36, 'Results_raw': {'train_loss': 8.746937, 'val_loss': 8.561651, 'test_loss': 9.103157}}
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2024-10-14 18:02:51,426 (client:354) INFO: {'Role': 'Client #10', 'Round': 36, 'Results_raw': {'train_loss': 14.955715, 'val_loss': 15.239892, 'test_loss': 16.733176}}
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2024-10-14 18:03:50,075 (client:354) INFO: {'Role': 'Client #1', 'Round': 36, 'Results_raw': {'train_loss': 10.685229, 'val_loss': 10.583329, 'test_loss': 11.551267}}
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2024-10-14 18:03:50,079 (server:615) INFO: {'Role': 'Server #', 'Round': 35, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.516379), 'test_loss': np.float64(101172.907333), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.604924), 'val_loss': np.float64(101631.925055)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.516379), 'test_loss': np.float64(101172.907333), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.604924), 'val_loss': np.float64(101631.925055)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.317652), 'test_avg_loss_bottom_decile': np.float64(16.478216), 'test_avg_loss_top_decile': np.float64(23.648785), 'test_avg_loss_min': np.float64(16.099821), 'test_avg_loss_max': np.float64(23.648785), 'test_avg_loss_bottom10%': np.float64(16.099821), 'test_avg_loss_top10%': np.float64(23.648785), 'test_avg_loss_cos1': np.float64(0.993022), 'test_avg_loss_entropy': np.float64(2.295537), 'test_loss_std': np.float64(12014.706645), 'test_loss_bottom_decile': np.float64(85423.069489), 'test_loss_top_decile': np.float64(122595.299133), 'test_loss_min': np.float64(83461.469635), 'test_loss_max': np.float64(122595.299133), 'test_loss_bottom10%': np.float64(83461.469635), 'test_loss_top10%': np.float64(122595.299133), 'test_loss_cos1': np.float64(0.993022), 'test_loss_entropy': np.float64(2.295537), 'val_avg_loss_std': np.float64(2.551755), 'val_avg_loss_bottom_decile': np.float64(16.285422), 'val_avg_loss_top_decile': np.float64(23.487197), 'val_avg_loss_min': np.float64(15.672367), 'val_avg_loss_max': np.float64(23.487197), 'val_avg_loss_bottom10%': np.float64(15.672367), 'val_avg_loss_top10%': np.float64(23.487197), 'val_avg_loss_cos1': np.float64(0.991635), 'val_avg_loss_entropy': np.float64(2.294043), 'val_loss_std': np.float64(13228.299107), 'val_loss_bottom_decile': np.float64(84423.625244), 'val_loss_top_decile': np.float64(121757.631592), 'val_loss_min': np.float64(81245.552795), 'val_loss_max': np.float64(121757.631592), 'val_loss_bottom10%': np.float64(81245.552795), 'val_loss_top10%': np.float64(121757.631592), 'val_loss_cos1': np.float64(0.991635), 'val_loss_entropy': np.float64(2.294043)}}
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2024-10-14 18:03:50,127 (server:353) INFO: Server: Starting evaluation at the end of round 36.
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2024-10-14 18:03:50,128 (server:359) INFO: ----------- Starting a new training round (Round #37) -------------
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2024-10-14 18:06:23,863 (client:354) INFO: {'Role': 'Client #9', 'Round': 37, 'Results_raw': {'train_loss': 17.787192, 'val_loss': 17.640755, 'test_loss': 18.337661}}
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2024-10-14 18:07:23,132 (client:354) INFO: {'Role': 'Client #8', 'Round': 37, 'Results_raw': {'train_loss': 13.250642, 'val_loss': 13.260769, 'test_loss': 14.03173}}
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2024-10-14 18:08:19,698 (client:354) INFO: {'Role': 'Client #1', 'Round': 37, 'Results_raw': {'train_loss': 10.727401, 'val_loss': 10.534466, 'test_loss': 11.700413}}
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2024-10-14 18:09:16,177 (client:354) INFO: {'Role': 'Client #2', 'Round': 37, 'Results_raw': {'train_loss': 8.707715, 'val_loss': 8.568552, 'test_loss': 9.08954}}
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2024-10-14 18:10:15,333 (client:354) INFO: {'Role': 'Client #4', 'Round': 37, 'Results_raw': {'train_loss': 15.084672, 'val_loss': 15.119313, 'test_loss': 16.020203}}
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2024-10-14 18:11:10,193 (client:354) INFO: {'Role': 'Client #5', 'Round': 37, 'Results_raw': {'train_loss': 15.926385, 'val_loss': 16.568124, 'test_loss': 18.599954}}
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2024-10-14 18:12:09,565 (client:354) INFO: {'Role': 'Client #3', 'Round': 37, 'Results_raw': {'train_loss': 10.10852, 'val_loss': 10.621097, 'test_loss': 12.030102}}
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2024-10-14 18:13:06,677 (client:354) INFO: {'Role': 'Client #6', 'Round': 37, 'Results_raw': {'train_loss': 15.045, 'val_loss': 14.918882, 'test_loss': 16.376979}}
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2024-10-14 18:14:05,367 (client:354) INFO: {'Role': 'Client #10', 'Round': 37, 'Results_raw': {'train_loss': 14.964446, 'val_loss': 15.392942, 'test_loss': 16.796833}}
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2024-10-14 18:15:03,746 (client:354) INFO: {'Role': 'Client #7', 'Round': 37, 'Results_raw': {'train_loss': 15.301724, 'val_loss': 15.500584, 'test_loss': 16.055823}}
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2024-10-14 18:15:03,751 (server:615) INFO: {'Role': 'Server #', 'Round': 36, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.676568), 'test_loss': np.float64(102003.326636), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.757049), 'val_loss': np.float64(102420.544058)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.676568), 'test_loss': np.float64(102003.326636), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.757049), 'val_loss': np.float64(102420.544058)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.294598), 'test_avg_loss_bottom_decile': np.float64(16.730839), 'test_avg_loss_top_decile': np.float64(23.862906), 'test_avg_loss_min': np.float64(16.250078), 'test_avg_loss_max': np.float64(23.862906), 'test_avg_loss_bottom10%': np.float64(16.250078), 'test_avg_loss_top10%': np.float64(23.862906), 'test_avg_loss_cos1': np.float64(0.993269), 'test_avg_loss_entropy': np.float64(2.2958), 'test_loss_std': np.float64(11895.195305), 'test_loss_bottom_decile': np.float64(86732.670563), 'test_loss_top_decile': np.float64(123705.30542), 'test_loss_min': np.float64(84240.402222), 'test_loss_max': np.float64(123705.30542), 'test_loss_bottom10%': np.float64(84240.402222), 'test_loss_top10%': np.float64(123705.30542), 'test_loss_cos1': np.float64(0.993269), 'test_loss_entropy': np.float64(2.2958), 'val_avg_loss_std': np.float64(2.534185), 'val_avg_loss_bottom_decile': np.float64(16.504279), 'val_avg_loss_top_decile': np.float64(23.685681), 'val_avg_loss_min': np.float64(15.808644), 'val_avg_loss_max': np.float64(23.685681), 'val_avg_loss_bottom10%': np.float64(15.808644), 'val_avg_loss_top10%': np.float64(23.685681), 'val_avg_loss_cos1': np.float64(0.991874), 'val_avg_loss_entropy': np.float64(2.294301), 'val_loss_std': np.float64(13137.217271), 'val_loss_bottom_decile': np.float64(85558.182831), 'val_loss_top_decile': np.float64(122786.572266), 'val_loss_min': np.float64(81952.009277), 'val_loss_max': np.float64(122786.572266), 'val_loss_bottom10%': np.float64(81952.009277), 'val_loss_top10%': np.float64(122786.572266), 'val_loss_cos1': np.float64(0.991874), 'val_loss_entropy': np.float64(2.294301)}}
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2024-10-14 18:15:03,797 (server:353) INFO: Server: Starting evaluation at the end of round 37.
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2024-10-14 18:15:03,797 (server:359) INFO: ----------- Starting a new training round (Round #38) -------------
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2024-10-14 18:17:35,896 (client:354) INFO: {'Role': 'Client #4', 'Round': 38, 'Results_raw': {'train_loss': 15.082245, 'val_loss': 15.135688, 'test_loss': 16.229044}}
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2024-10-14 18:18:38,953 (client:354) INFO: {'Role': 'Client #2', 'Round': 38, 'Results_raw': {'train_loss': 8.707729, 'val_loss': 8.390411, 'test_loss': 8.900081}}
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2024-10-14 18:19:39,427 (client:354) INFO: {'Role': 'Client #9', 'Round': 38, 'Results_raw': {'train_loss': 17.813758, 'val_loss': 17.631822, 'test_loss': 18.37244}}
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2024-10-14 18:20:45,938 (client:354) INFO: {'Role': 'Client #6', 'Round': 38, 'Results_raw': {'train_loss': 15.090375, 'val_loss': 15.064171, 'test_loss': 16.01164}}
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2024-10-14 18:21:43,781 (client:354) INFO: {'Role': 'Client #5', 'Round': 38, 'Results_raw': {'train_loss': 15.925303, 'val_loss': 16.629, 'test_loss': 18.719828}}
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2024-10-14 18:22:43,550 (client:354) INFO: {'Role': 'Client #7', 'Round': 38, 'Results_raw': {'train_loss': 15.298282, 'val_loss': 15.415583, 'test_loss': 16.05156}}
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2024-10-14 18:23:43,048 (client:354) INFO: {'Role': 'Client #10', 'Round': 38, 'Results_raw': {'train_loss': 14.970604, 'val_loss': 15.273375, 'test_loss': 16.679602}}
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2024-10-14 18:24:41,057 (client:354) INFO: {'Role': 'Client #3', 'Round': 38, 'Results_raw': {'train_loss': 10.160219, 'val_loss': 10.703597, 'test_loss': 12.0467}}
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2024-10-14 18:25:40,250 (client:354) INFO: {'Role': 'Client #1', 'Round': 38, 'Results_raw': {'train_loss': 10.852351, 'val_loss': 10.540495, 'test_loss': 11.613442}}
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2024-10-14 18:26:38,859 (client:354) INFO: {'Role': 'Client #8', 'Round': 38, 'Results_raw': {'train_loss': 13.244261, 'val_loss': 13.271537, 'test_loss': 13.752825}}
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2024-10-14 18:26:38,863 (server:615) INFO: {'Role': 'Server #', 'Round': 37, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.505371), 'test_loss': np.float64(101115.844125), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.564019), 'val_loss': np.float64(101419.875613)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.505371), 'test_loss': np.float64(101115.844125), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.564019), 'val_loss': np.float64(101419.875613)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.278643), 'test_avg_loss_bottom_decile': np.float64(16.608599), 'test_avg_loss_top_decile': np.float64(23.677332), 'test_avg_loss_min': np.float64(16.130848), 'test_avg_loss_max': np.float64(23.677332), 'test_avg_loss_bottom10%': np.float64(16.130848), 'test_avg_loss_top10%': np.float64(23.677332), 'test_avg_loss_cos1': np.float64(0.993245), 'test_avg_loss_entropy': np.float64(2.295779), 'test_loss_std': np.float64(11812.486865), 'test_loss_bottom_decile': np.float64(86098.978821), 'test_loss_top_decile': np.float64(122743.286682), 'test_loss_min': np.float64(83622.314941), 'test_loss_max': np.float64(122743.286682), 'test_loss_bottom10%': np.float64(83622.314941), 'test_loss_top10%': np.float64(122743.286682), 'test_loss_cos1': np.float64(0.993245), 'test_loss_entropy': np.float64(2.295779), 'val_avg_loss_std': np.float64(2.502999), 'val_avg_loss_bottom_decile': np.float64(16.3866), 'val_avg_loss_top_decile': np.float64(23.463974), 'val_avg_loss_min': np.float64(15.682836), 'val_avg_loss_max': np.float64(23.463974), 'val_avg_loss_bottom10%': np.float64(15.682836), 'val_avg_loss_top10%': np.float64(23.463974), 'val_avg_loss_cos1': np.float64(0.991915), 'val_avg_loss_entropy': np.float64(2.294347), 'val_loss_std': np.float64(12975.548176), 'val_loss_bottom_decile': np.float64(84948.134369), 'val_loss_top_decile': np.float64(121637.242859), 'val_loss_min': np.float64(81299.822479), 'val_loss_max': np.float64(121637.242859), 'val_loss_bottom10%': np.float64(81299.822479), 'val_loss_top10%': np.float64(121637.242859), 'val_loss_cos1': np.float64(0.991915), 'val_loss_entropy': np.float64(2.294347)}}
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2024-10-14 18:26:38,908 (server:353) INFO: Server: Starting evaluation at the end of round 38.
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2024-10-14 18:26:38,908 (server:359) INFO: ----------- Starting a new training round (Round #39) -------------
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2024-10-14 18:29:12,904 (client:354) INFO: {'Role': 'Client #5', 'Round': 39, 'Results_raw': {'train_loss': 15.879953, 'val_loss': 16.612321, 'test_loss': 18.786543}}
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2024-10-14 18:30:05,844 (client:354) INFO: {'Role': 'Client #10', 'Round': 39, 'Results_raw': {'train_loss': 14.931407, 'val_loss': 15.351307, 'test_loss': 16.856134}}
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2024-10-14 18:31:01,801 (client:354) INFO: {'Role': 'Client #7', 'Round': 39, 'Results_raw': {'train_loss': 15.271758, 'val_loss': 15.515656, 'test_loss': 16.080648}}
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2024-10-14 18:31:58,922 (client:354) INFO: {'Role': 'Client #3', 'Round': 39, 'Results_raw': {'train_loss': 10.118101, 'val_loss': 10.738536, 'test_loss': 12.100165}}
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2024-10-14 18:33:05,290 (client:354) INFO: {'Role': 'Client #8', 'Round': 39, 'Results_raw': {'train_loss': 13.21715, 'val_loss': 13.412031, 'test_loss': 14.157294}}
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2024-10-14 18:34:05,947 (client:354) INFO: {'Role': 'Client #4', 'Round': 39, 'Results_raw': {'train_loss': 15.043658, 'val_loss': 15.359749, 'test_loss': 16.300507}}
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2024-10-14 18:35:05,034 (client:354) INFO: {'Role': 'Client #2', 'Round': 39, 'Results_raw': {'train_loss': 8.72316, 'val_loss': 8.389617, 'test_loss': 8.936337}}
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2024-10-14 18:36:07,369 (client:354) INFO: {'Role': 'Client #6', 'Round': 39, 'Results_raw': {'train_loss': 15.039677, 'val_loss': 15.136336, 'test_loss': 16.368325}}
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2024-10-14 18:37:03,629 (client:354) INFO: {'Role': 'Client #1', 'Round': 39, 'Results_raw': {'train_loss': 10.698151, 'val_loss': 10.573993, 'test_loss': 11.7}}
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2024-10-14 18:38:01,220 (client:354) INFO: {'Role': 'Client #9', 'Round': 39, 'Results_raw': {'train_loss': 17.750494, 'val_loss': 17.72945, 'test_loss': 18.537333}}
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2024-10-14 18:38:01,226 (server:615) INFO: {'Role': 'Server #', 'Round': 38, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.482602), 'test_loss': np.float64(100997.808234), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.567557), 'val_loss': np.float64(101438.215735)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.482602), 'test_loss': np.float64(100997.808234), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.567557), 'val_loss': np.float64(101438.215735)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.318459), 'test_avg_loss_bottom_decile': np.float64(16.511818), 'test_avg_loss_top_decile': np.float64(23.667066), 'test_avg_loss_min': np.float64(16.083679), 'test_avg_loss_max': np.float64(23.667066), 'test_avg_loss_bottom10%': np.float64(16.083679), 'test_avg_loss_top10%': np.float64(23.667066), 'test_avg_loss_cos1': np.float64(0.992994), 'test_avg_loss_entropy': np.float64(2.295519), 'test_loss_std': np.float64(12018.893842), 'test_loss_bottom_decile': np.float64(85597.265839), 'test_loss_top_decile': np.float64(122690.067566), 'test_loss_min': np.float64(83377.794373), 'test_loss_max': np.float64(122690.067566), 'test_loss_bottom10%': np.float64(83377.794373), 'test_loss_top10%': np.float64(122690.067566), 'test_loss_cos1': np.float64(0.992994), 'test_loss_entropy': np.float64(2.295519), 'val_avg_loss_std': np.float64(2.555057), 'val_avg_loss_bottom_decile': np.float64(16.299482), 'val_avg_loss_top_decile': np.float64(23.457979), 'val_avg_loss_min': np.float64(15.649948), 'val_avg_loss_max': np.float64(23.457979), 'val_avg_loss_bottom10%': np.float64(15.649948), 'val_avg_loss_top10%': np.float64(23.457979), 'val_avg_loss_cos1': np.float64(0.991582), 'val_avg_loss_entropy': np.float64(2.293995), 'val_loss_std': np.float64(13245.414604), 'val_loss_bottom_decile': np.float64(84496.515106), 'val_loss_top_decile': np.float64(121606.165344), 'val_loss_min': np.float64(81129.332245), 'val_loss_max': np.float64(121606.165344), 'val_loss_bottom10%': np.float64(81129.332245), 'val_loss_top10%': np.float64(121606.165344), 'val_loss_cos1': np.float64(0.991582), 'val_loss_entropy': np.float64(2.293995)}}
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2024-10-14 18:38:01,289 (server:353) INFO: Server: Starting evaluation at the end of round 39.
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2024-10-14 18:38:01,289 (server:359) INFO: ----------- Starting a new training round (Round #40) -------------
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2024-10-14 18:40:30,313 (client:354) INFO: {'Role': 'Client #3', 'Round': 40, 'Results_raw': {'train_loss': 10.039364, 'val_loss': 10.619796, 'test_loss': 12.067475}}
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2024-10-14 18:41:26,963 (client:354) INFO: {'Role': 'Client #5', 'Round': 40, 'Results_raw': {'train_loss': 15.874092, 'val_loss': 16.657162, 'test_loss': 18.802135}}
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2024-10-14 18:42:26,797 (client:354) INFO: {'Role': 'Client #4', 'Round': 40, 'Results_raw': {'train_loss': 15.015746, 'val_loss': 15.138658, 'test_loss': 16.433494}}
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2024-10-14 18:43:29,742 (client:354) INFO: {'Role': 'Client #6', 'Round': 40, 'Results_raw': {'train_loss': 15.010987, 'val_loss': 14.963691, 'test_loss': 16.286681}}
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2024-10-14 18:44:25,081 (client:354) INFO: {'Role': 'Client #7', 'Round': 40, 'Results_raw': {'train_loss': 15.275798, 'val_loss': 15.462046, 'test_loss': 16.113385}}
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2024-10-14 18:45:25,430 (client:354) INFO: {'Role': 'Client #2', 'Round': 40, 'Results_raw': {'train_loss': 8.739454, 'val_loss': 8.462993, 'test_loss': 8.98131}}
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2024-10-14 18:46:22,288 (client:354) INFO: {'Role': 'Client #9', 'Round': 40, 'Results_raw': {'train_loss': 17.746043, 'val_loss': 17.832087, 'test_loss': 18.618674}}
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2024-10-14 18:47:21,907 (client:354) INFO: {'Role': 'Client #1', 'Round': 40, 'Results_raw': {'train_loss': 10.641197, 'val_loss': 10.540771, 'test_loss': 11.639476}}
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2024-10-14 18:48:23,175 (client:354) INFO: {'Role': 'Client #8', 'Round': 40, 'Results_raw': {'train_loss': 13.1847, 'val_loss': 13.302446, 'test_loss': 14.228953}}
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2024-10-14 18:49:21,440 (client:354) INFO: {'Role': 'Client #10', 'Round': 40, 'Results_raw': {'train_loss': 14.923847, 'val_loss': 15.408604, 'test_loss': 16.969966}}
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2024-10-14 18:49:21,446 (server:615) INFO: {'Role': 'Server #', 'Round': 39, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.509089), 'test_loss': np.float64(101135.115414), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.574695), 'val_loss': np.float64(101475.217227)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.509089), 'test_loss': np.float64(101135.115414), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.574695), 'val_loss': np.float64(101475.217227)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.341959), 'test_avg_loss_bottom_decile': np.float64(16.50724), 'test_avg_loss_top_decile': np.float64(23.780166), 'test_avg_loss_min': np.float64(15.994294), 'test_avg_loss_max': np.float64(23.780166), 'test_avg_loss_bottom10%': np.float64(15.994294), 'test_avg_loss_top10%': np.float64(23.780166), 'test_avg_loss_cos1': np.float64(0.992872), 'test_avg_loss_entropy': np.float64(2.295389), 'test_loss_std': np.float64(12140.714001), 'test_loss_bottom_decile': np.float64(85573.531555), 'test_loss_top_decile': np.float64(123276.381104), 'test_loss_min': np.float64(82914.417999), 'test_loss_max': np.float64(123276.381104), 'test_loss_bottom10%': np.float64(82914.417999), 'test_loss_top10%': np.float64(123276.381104), 'test_loss_cos1': np.float64(0.992872), 'test_loss_entropy': np.float64(2.295389), 'val_avg_loss_std': np.float64(2.577078), 'val_avg_loss_bottom_decile': np.float64(16.264906), 'val_avg_loss_top_decile': np.float64(23.56517), 'val_avg_loss_min': np.float64(15.545323), 'val_avg_loss_max': np.float64(23.56517), 'val_avg_loss_bottom10%': np.float64(15.545323), 'val_avg_loss_top10%': np.float64(23.56517), 'val_avg_loss_cos1': np.float64(0.991445), 'val_avg_loss_entropy': np.float64(2.293846), 'val_loss_std': np.float64(13359.570453), 'val_loss_bottom_decile': np.float64(84317.270844), 'val_loss_top_decile': np.float64(122161.843628), 'val_loss_min': np.float64(80586.953278), 'val_loss_max': np.float64(122161.843628), 'val_loss_bottom10%': np.float64(80586.953278), 'val_loss_top10%': np.float64(122161.843628), 'val_loss_cos1': np.float64(0.991445), 'val_loss_entropy': np.float64(2.293846)}}
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2024-10-14 18:49:21,501 (server:353) INFO: Server: Starting evaluation at the end of round 40.
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2024-10-14 18:49:21,502 (server:359) INFO: ----------- Starting a new training round (Round #41) -------------
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2024-10-14 18:51:54,617 (client:354) INFO: {'Role': 'Client #6', 'Round': 41, 'Results_raw': {'train_loss': 15.015939, 'val_loss': 14.917164, 'test_loss': 16.461689}}
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2024-10-14 18:52:51,001 (client:354) INFO: {'Role': 'Client #4', 'Round': 41, 'Results_raw': {'train_loss': 15.043853, 'val_loss': 15.136049, 'test_loss': 16.113238}}
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2024-10-14 18:53:50,153 (client:354) INFO: {'Role': 'Client #9', 'Round': 41, 'Results_raw': {'train_loss': 17.731603, 'val_loss': 17.871791, 'test_loss': 18.858781}}
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2024-10-14 18:54:45,143 (client:354) INFO: {'Role': 'Client #8', 'Round': 41, 'Results_raw': {'train_loss': 13.203045, 'val_loss': 13.21683, 'test_loss': 13.84278}}
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2024-10-14 18:55:44,605 (client:354) INFO: {'Role': 'Client #7', 'Round': 41, 'Results_raw': {'train_loss': 15.264939, 'val_loss': 15.419844, 'test_loss': 16.118572}}
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2024-10-14 18:56:44,705 (client:354) INFO: {'Role': 'Client #1', 'Round': 41, 'Results_raw': {'train_loss': 10.646626, 'val_loss': 10.679148, 'test_loss': 11.763639}}
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2024-10-14 18:57:42,173 (client:354) INFO: {'Role': 'Client #10', 'Round': 41, 'Results_raw': {'train_loss': 14.916173, 'val_loss': 15.352584, 'test_loss': 16.661566}}
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2024-10-14 18:58:50,266 (client:354) INFO: {'Role': 'Client #3', 'Round': 41, 'Results_raw': {'train_loss': 10.047694, 'val_loss': 10.557891, 'test_loss': 11.904868}}
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2024-10-14 18:59:47,288 (client:354) INFO: {'Role': 'Client #5', 'Round': 41, 'Results_raw': {'train_loss': 15.888216, 'val_loss': 16.729059, 'test_loss': 18.6676}}
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2024-10-14 19:00:50,298 (client:354) INFO: {'Role': 'Client #2', 'Round': 41, 'Results_raw': {'train_loss': 8.753991, 'val_loss': 8.501451, 'test_loss': 8.902841}}
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2024-10-14 19:00:50,302 (server:615) INFO: {'Role': 'Server #', 'Round': 40, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.505192), 'test_loss': np.float64(101114.916336), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.54283), 'val_loss': np.float64(101310.030902)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.505192), 'test_loss': np.float64(101114.916336), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.54283), 'val_loss': np.float64(101310.030902)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.238924), 'test_avg_loss_bottom_decile': np.float64(16.751875), 'test_avg_loss_top_decile': np.float64(23.636836), 'test_avg_loss_min': np.float64(16.096945), 'test_avg_loss_max': np.float64(23.636836), 'test_avg_loss_bottom10%': np.float64(16.096945), 'test_avg_loss_top10%': np.float64(23.636836), 'test_avg_loss_cos1': np.float64(0.993476), 'test_avg_loss_entropy': np.float64(2.296015), 'test_loss_std': np.float64(11606.583194), 'test_loss_bottom_decile': np.float64(86841.717529), 'test_loss_top_decile': np.float64(122533.356018), 'test_loss_min': np.float64(83446.56427), 'test_loss_max': np.float64(122533.356018), 'test_loss_bottom10%': np.float64(83446.56427), 'test_loss_top10%': np.float64(122533.356018), 'test_loss_cos1': np.float64(0.993476), 'test_loss_entropy': np.float64(2.296015), 'val_avg_loss_std': np.float64(2.459217), 'val_avg_loss_bottom_decile': np.float64(16.500124), 'val_avg_loss_top_decile': np.float64(23.400398), 'val_avg_loss_min': np.float64(15.631315), 'val_avg_loss_max': np.float64(23.400398), 'val_avg_loss_bottom10%': np.float64(15.631315), 'val_avg_loss_top10%': np.float64(23.400398), 'val_avg_loss_cos1': np.float64(0.992175), 'val_avg_loss_entropy': np.float64(2.294615), 'val_loss_std': np.float64(12748.578856), 'val_loss_bottom_decile': np.float64(85536.642914), 'val_loss_top_decile': np.float64(121307.662476), 'val_loss_min': np.float64(81032.734833), 'val_loss_max': np.float64(121307.662476), 'val_loss_bottom10%': np.float64(81032.734833), 'val_loss_top10%': np.float64(121307.662476), 'val_loss_cos1': np.float64(0.992175), 'val_loss_entropy': np.float64(2.294615)}}
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2024-10-14 19:00:50,346 (server:353) INFO: Server: Starting evaluation at the end of round 41.
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2024-10-14 19:00:50,347 (server:359) INFO: ----------- Starting a new training round (Round #42) -------------
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2024-10-14 19:03:21,352 (client:354) INFO: {'Role': 'Client #3', 'Round': 42, 'Results_raw': {'train_loss': 10.032534, 'val_loss': 10.567902, 'test_loss': 12.019137}}
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2024-10-14 19:04:22,356 (client:354) INFO: {'Role': 'Client #6', 'Round': 42, 'Results_raw': {'train_loss': 14.995583, 'val_loss': 14.898195, 'test_loss': 16.09844}}
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2024-10-14 19:05:21,193 (client:354) INFO: {'Role': 'Client #10', 'Round': 42, 'Results_raw': {'train_loss': 14.906884, 'val_loss': 15.318938, 'test_loss': 16.994119}}
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2024-10-14 19:06:24,199 (client:354) INFO: {'Role': 'Client #8', 'Round': 42, 'Results_raw': {'train_loss': 13.207337, 'val_loss': 13.199311, 'test_loss': 13.770458}}
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2024-10-14 19:07:22,430 (client:354) INFO: {'Role': 'Client #7', 'Round': 42, 'Results_raw': {'train_loss': 15.231154, 'val_loss': 15.453368, 'test_loss': 16.108207}}
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2024-10-14 19:08:23,078 (client:354) INFO: {'Role': 'Client #4', 'Round': 42, 'Results_raw': {'train_loss': 15.034992, 'val_loss': 15.158817, 'test_loss': 16.175502}}
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2024-10-14 19:09:15,674 (client:354) INFO: {'Role': 'Client #5', 'Round': 42, 'Results_raw': {'train_loss': 15.880987, 'val_loss': 16.590857, 'test_loss': 18.712526}}
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2024-10-14 19:10:13,908 (client:354) INFO: {'Role': 'Client #2', 'Round': 42, 'Results_raw': {'train_loss': 8.696503, 'val_loss': 8.489146, 'test_loss': 9.10727}}
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2024-10-14 19:11:17,346 (client:354) INFO: {'Role': 'Client #9', 'Round': 42, 'Results_raw': {'train_loss': 17.717493, 'val_loss': 17.685701, 'test_loss': 18.45664}}
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2024-10-14 19:12:14,967 (client:354) INFO: {'Role': 'Client #1', 'Round': 42, 'Results_raw': {'train_loss': 10.638408, 'val_loss': 10.467496, 'test_loss': 11.460855}}
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2024-10-14 19:12:14,972 (server:615) INFO: {'Role': 'Server #', 'Round': 41, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.345199), 'test_loss': np.float64(100285.512024), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.400144), 'val_loss': np.float64(100570.346811)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.345199), 'test_loss': np.float64(100285.512024), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.400144), 'val_loss': np.float64(100570.346811)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.29117), 'test_avg_loss_bottom_decile': np.float64(16.441111), 'test_avg_loss_top_decile': np.float64(23.48068), 'test_avg_loss_min': np.float64(15.957975), 'test_avg_loss_max': np.float64(23.48068), 'test_avg_loss_bottom10%': np.float64(15.957975), 'test_avg_loss_top10%': np.float64(23.48068), 'test_avg_loss_cos1': np.float64(0.993059), 'test_avg_loss_entropy': np.float64(2.295586), 'test_loss_std': np.float64(11877.427115), 'test_loss_bottom_decile': np.float64(85230.71875), 'test_loss_top_decile': np.float64(121723.845459), 'test_loss_min': np.float64(82726.140717), 'test_loss_max': np.float64(121723.845459), 'test_loss_bottom10%': np.float64(82726.140717), 'test_loss_top10%': np.float64(121723.845459), 'test_loss_cos1': np.float64(0.993059), 'test_loss_entropy': np.float64(2.295586), 'val_avg_loss_std': np.float64(2.511565), 'val_avg_loss_bottom_decile': np.float64(16.219008), 'val_avg_loss_top_decile': np.float64(23.227644), 'val_avg_loss_min': np.float64(15.512062), 'val_avg_loss_max': np.float64(23.227644), 'val_avg_loss_bottom10%': np.float64(15.512062), 'val_avg_loss_top10%': np.float64(23.227644), 'val_avg_loss_cos1': np.float64(0.991724), 'val_avg_loss_entropy': np.float64(2.294142), 'val_loss_std': np.float64(13019.952771), 'val_loss_bottom_decile': np.float64(84079.336823), 'val_loss_top_decile': np.float64(120412.105103), 'val_loss_min': np.float64(80414.528259), 'val_loss_max': np.float64(120412.105103), 'val_loss_bottom10%': np.float64(80414.528259), 'val_loss_top10%': np.float64(120412.105103), 'val_loss_cos1': np.float64(0.991724), 'val_loss_entropy': np.float64(2.294142)}}
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2024-10-14 19:12:15,013 (server:353) INFO: Server: Starting evaluation at the end of round 42.
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2024-10-14 19:12:15,014 (server:359) INFO: ----------- Starting a new training round (Round #43) -------------
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2024-10-14 19:14:45,802 (client:354) INFO: {'Role': 'Client #9', 'Round': 43, 'Results_raw': {'train_loss': 17.729144, 'val_loss': 17.894055, 'test_loss': 18.791586}}
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2024-10-14 19:15:45,860 (client:354) INFO: {'Role': 'Client #3', 'Round': 43, 'Results_raw': {'train_loss': 10.064648, 'val_loss': 10.630411, 'test_loss': 12.0477}}
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2024-10-14 19:16:45,249 (client:354) INFO: {'Role': 'Client #2', 'Round': 43, 'Results_raw': {'train_loss': 8.731757, 'val_loss': 8.579462, 'test_loss': 9.142491}}
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2024-10-14 19:17:39,868 (client:354) INFO: {'Role': 'Client #1', 'Round': 43, 'Results_raw': {'train_loss': 10.694803, 'val_loss': 10.514782, 'test_loss': 11.539764}}
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2024-10-14 19:18:38,606 (client:354) INFO: {'Role': 'Client #10', 'Round': 43, 'Results_raw': {'train_loss': 14.895453, 'val_loss': 15.346375, 'test_loss': 17.052038}}
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2024-10-14 19:19:38,160 (client:354) INFO: {'Role': 'Client #6', 'Round': 43, 'Results_raw': {'train_loss': 14.969563, 'val_loss': 15.024953, 'test_loss': 16.380617}}
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2024-10-14 19:20:38,770 (client:354) INFO: {'Role': 'Client #8', 'Round': 43, 'Results_raw': {'train_loss': 13.165807, 'val_loss': 13.187566, 'test_loss': 13.869792}}
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2024-10-14 19:21:35,824 (client:354) INFO: {'Role': 'Client #7', 'Round': 43, 'Results_raw': {'train_loss': 15.222076, 'val_loss': 15.4395, 'test_loss': 16.287271}}
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2024-10-14 19:22:35,480 (client:354) INFO: {'Role': 'Client #4', 'Round': 43, 'Results_raw': {'train_loss': 15.012077, 'val_loss': 15.013041, 'test_loss': 16.05417}}
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2024-10-14 19:23:39,253 (client:354) INFO: {'Role': 'Client #5', 'Round': 43, 'Results_raw': {'train_loss': 15.810116, 'val_loss': 16.640127, 'test_loss': 18.831369}}
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2024-10-14 19:23:39,257 (server:615) INFO: {'Role': 'Server #', 'Round': 42, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.53779), 'test_loss': np.float64(101283.905255), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.601608), 'val_loss': np.float64(101614.736047)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.53779), 'test_loss': np.float64(101283.905255), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.601608), 'val_loss': np.float64(101614.736047)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.323618), 'test_avg_loss_bottom_decile': np.float64(16.58912), 'test_avg_loss_top_decile': np.float64(23.789076), 'test_avg_loss_min': np.float64(16.041429), 'test_avg_loss_max': np.float64(23.789076), 'test_avg_loss_bottom10%': np.float64(16.041429), 'test_avg_loss_top10%': np.float64(23.789076), 'test_avg_loss_cos1': np.float64(0.993002), 'test_avg_loss_entropy': np.float64(2.29553), 'test_loss_std': np.float64(12045.637735), 'test_loss_bottom_decile': np.float64(85997.998566), 'test_loss_top_decile': np.float64(123322.571045), 'test_loss_min': np.float64(83158.769867), 'test_loss_max': np.float64(123322.571045), 'test_loss_bottom10%': np.float64(83158.769867), 'test_loss_top10%': np.float64(123322.571045), 'test_loss_cos1': np.float64(0.993002), 'test_loss_entropy': np.float64(2.29553), 'val_avg_loss_std': np.float64(2.554851), 'val_avg_loss_bottom_decile': np.float64(16.340516), 'val_avg_loss_top_decile': np.float64(23.562585), 'val_avg_loss_min': np.float64(15.594478), 'val_avg_loss_max': np.float64(23.562585), 'val_avg_loss_bottom10%': np.float64(15.594478), 'val_avg_loss_top10%': np.float64(23.562585), 'val_avg_loss_cos1': np.float64(0.991613), 'val_avg_loss_entropy': np.float64(2.294029), 'val_loss_std': np.float64(13244.345858), 'val_loss_bottom_decile': np.float64(84709.234833), 'val_loss_top_decile': np.float64(122148.440552), 'val_loss_min': np.float64(80841.772797), 'val_loss_max': np.float64(122148.440552), 'val_loss_bottom10%': np.float64(80841.772797), 'val_loss_top10%': np.float64(122148.440552), 'val_loss_cos1': np.float64(0.991613), 'val_loss_entropy': np.float64(2.294029)}}
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2024-10-14 19:23:39,297 (server:353) INFO: Server: Starting evaluation at the end of round 43.
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2024-10-14 19:23:39,298 (server:359) INFO: ----------- Starting a new training round (Round #44) -------------
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2024-10-14 19:26:09,215 (client:354) INFO: {'Role': 'Client #1', 'Round': 44, 'Results_raw': {'train_loss': 10.646028, 'val_loss': 10.521278, 'test_loss': 11.646683}}
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2024-10-14 19:27:08,457 (client:354) INFO: {'Role': 'Client #8', 'Round': 44, 'Results_raw': {'train_loss': 13.17192, 'val_loss': 13.17729, 'test_loss': 13.815443}}
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2024-10-14 19:28:04,628 (client:354) INFO: {'Role': 'Client #4', 'Round': 44, 'Results_raw': {'train_loss': 15.005387, 'val_loss': 15.217709, 'test_loss': 16.289788}}
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2024-10-14 19:29:03,279 (client:354) INFO: {'Role': 'Client #7', 'Round': 44, 'Results_raw': {'train_loss': 15.208332, 'val_loss': 15.497501, 'test_loss': 16.06759}}
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2024-10-14 19:30:01,054 (client:354) INFO: {'Role': 'Client #6', 'Round': 44, 'Results_raw': {'train_loss': 14.973515, 'val_loss': 14.944644, 'test_loss': 16.266856}}
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2024-10-14 19:30:59,026 (client:354) INFO: {'Role': 'Client #5', 'Round': 44, 'Results_raw': {'train_loss': 15.818693, 'val_loss': 16.640548, 'test_loss': 18.863273}}
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2024-10-14 19:31:57,637 (client:354) INFO: {'Role': 'Client #10', 'Round': 44, 'Results_raw': {'train_loss': 14.870141, 'val_loss': 15.225014, 'test_loss': 16.652374}}
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2024-10-14 19:32:59,072 (client:354) INFO: {'Role': 'Client #9', 'Round': 44, 'Results_raw': {'train_loss': 17.681752, 'val_loss': 17.764382, 'test_loss': 18.569957}}
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2024-10-14 19:33:58,972 (client:354) INFO: {'Role': 'Client #2', 'Round': 44, 'Results_raw': {'train_loss': 8.620345, 'val_loss': 8.439047, 'test_loss': 9.110108}}
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2024-10-14 19:34:56,344 (client:354) INFO: {'Role': 'Client #3', 'Round': 44, 'Results_raw': {'train_loss': 10.055371, 'val_loss': 10.59036, 'test_loss': 11.934526}}
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2024-10-14 19:34:56,348 (server:615) INFO: {'Role': 'Server #', 'Round': 43, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.460138), 'test_loss': np.float64(100881.357401), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.498865), 'val_loss': np.float64(101082.113623)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.460138), 'test_loss': np.float64(100881.357401), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.498865), 'val_loss': np.float64(101082.113623)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.297981), 'test_avg_loss_bottom_decile': np.float64(16.597063), 'test_avg_loss_top_decile': np.float64(23.653529), 'test_avg_loss_min': np.float64(16.005075), 'test_avg_loss_max': np.float64(23.653529), 'test_avg_loss_bottom10%': np.float64(16.005075), 'test_avg_loss_top10%': np.float64(23.653529), 'test_avg_loss_cos1': np.float64(0.9931), 'test_avg_loss_entropy': np.float64(2.29563), 'test_loss_std': np.float64(11912.73331), 'test_loss_bottom_decile': np.float64(86039.173645), 'test_loss_top_decile': np.float64(122619.893494), 'test_loss_min': np.float64(82970.310028), 'test_loss_max': np.float64(122619.893494), 'test_loss_bottom10%': np.float64(82970.310028), 'test_loss_top10%': np.float64(122619.893494), 'test_loss_cos1': np.float64(0.9931), 'test_loss_entropy': np.float64(2.29563), 'val_avg_loss_std': np.float64(2.523188), 'val_avg_loss_bottom_decile': np.float64(16.320682), 'val_avg_loss_top_decile': np.float64(23.391876), 'val_avg_loss_min': np.float64(15.534957), 'val_avg_loss_max': np.float64(23.391876), 'val_avg_loss_bottom10%': np.float64(15.534957), 'val_avg_loss_top10%': np.float64(23.391876), 'val_avg_loss_cos1': np.float64(0.991731), 'val_avg_loss_entropy': np.float64(2.294152), 'val_loss_std': np.float64(13080.206759), 'val_loss_bottom_decile': np.float64(84606.413818), 'val_loss_top_decile': np.float64(121263.483765), 'val_loss_min': np.float64(80533.219269), 'val_loss_max': np.float64(121263.483765), 'val_loss_bottom10%': np.float64(80533.219269), 'val_loss_top10%': np.float64(121263.483765), 'val_loss_cos1': np.float64(0.991731), 'val_loss_entropy': np.float64(2.294152)}}
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2024-10-14 19:34:56,385 (server:353) INFO: Server: Starting evaluation at the end of round 44.
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2024-10-14 19:34:56,385 (server:359) INFO: ----------- Starting a new training round (Round #45) -------------
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2024-10-14 19:37:30,760 (client:354) INFO: {'Role': 'Client #4', 'Round': 45, 'Results_raw': {'train_loss': 14.948797, 'val_loss': 15.047792, 'test_loss': 16.152441}}
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2024-10-14 19:38:28,621 (client:354) INFO: {'Role': 'Client #7', 'Round': 45, 'Results_raw': {'train_loss': 15.186201, 'val_loss': 15.327846, 'test_loss': 16.183431}}
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2024-10-14 19:39:26,946 (client:354) INFO: {'Role': 'Client #5', 'Round': 45, 'Results_raw': {'train_loss': 15.813922, 'val_loss': 16.55863, 'test_loss': 18.650435}}
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2024-10-14 19:40:26,840 (client:354) INFO: {'Role': 'Client #10', 'Round': 45, 'Results_raw': {'train_loss': 14.865779, 'val_loss': 15.307414, 'test_loss': 16.815309}}
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2024-10-14 19:41:27,385 (client:354) INFO: {'Role': 'Client #9', 'Round': 45, 'Results_raw': {'train_loss': 17.678258, 'val_loss': 17.665782, 'test_loss': 18.582553}}
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2024-10-14 19:42:27,725 (client:354) INFO: {'Role': 'Client #2', 'Round': 45, 'Results_raw': {'train_loss': 8.67801, 'val_loss': 8.467089, 'test_loss': 8.951637}}
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2024-10-14 19:43:22,312 (client:354) INFO: {'Role': 'Client #1', 'Round': 45, 'Results_raw': {'train_loss': 10.569289, 'val_loss': 10.342794, 'test_loss': 11.428102}}
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2024-10-14 19:44:22,034 (client:354) INFO: {'Role': 'Client #8', 'Round': 45, 'Results_raw': {'train_loss': 13.150165, 'val_loss': 13.158644, 'test_loss': 13.700577}}
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2024-10-14 19:45:20,110 (client:354) INFO: {'Role': 'Client #6', 'Round': 45, 'Results_raw': {'train_loss': 14.94277, 'val_loss': 14.883988, 'test_loss': 16.17609}}
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2024-10-14 19:46:17,283 (client:354) INFO: {'Role': 'Client #3', 'Round': 45, 'Results_raw': {'train_loss': 9.992235, 'val_loss': 10.583677, 'test_loss': 12.014879}}
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2024-10-14 19:46:17,288 (server:615) INFO: {'Role': 'Server #', 'Round': 44, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.441429), 'test_loss': np.float64(100784.369632), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.498809), 'val_loss': np.float64(101081.825586)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.441429), 'test_loss': np.float64(100784.369632), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.498809), 'val_loss': np.float64(101081.825586)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.336871), 'test_avg_loss_bottom_decile': np.float64(16.391413), 'test_avg_loss_top_decile': np.float64(23.683129), 'test_avg_loss_min': np.float64(15.948683), 'test_avg_loss_max': np.float64(23.683129), 'test_avg_loss_bottom10%': np.float64(15.948683), 'test_avg_loss_top10%': np.float64(23.683129), 'test_avg_loss_cos1': np.float64(0.992853), 'test_avg_loss_entropy': np.float64(2.29537), 'test_loss_std': np.float64(12114.337555), 'test_loss_bottom_decile': np.float64(84973.085449), 'test_loss_top_decile': np.float64(122773.339172), 'test_loss_min': np.float64(82677.974182), 'test_loss_max': np.float64(122773.339172), 'test_loss_bottom10%': np.float64(82677.974182), 'test_loss_top10%': np.float64(122773.339172), 'test_loss_cos1': np.float64(0.992853), 'test_loss_entropy': np.float64(2.29537), 'val_avg_loss_std': np.float64(2.569492), 'val_avg_loss_bottom_decile': np.float64(16.150906), 'val_avg_loss_top_decile': np.float64(23.458977), 'val_avg_loss_min': np.float64(15.492311), 'val_avg_loss_max': np.float64(23.458977), 'val_avg_loss_bottom10%': np.float64(15.492311), 'val_avg_loss_top10%': np.float64(23.458977), 'val_avg_loss_cos1': np.float64(0.991429), 'val_avg_loss_entropy': np.float64(2.293833), 'val_loss_std': np.float64(13320.244797), 'val_loss_bottom_decile': np.float64(83726.295441), 'val_loss_top_decile': np.float64(121611.337524), 'val_loss_min': np.float64(80312.141663), 'val_loss_max': np.float64(121611.337524), 'val_loss_bottom10%': np.float64(80312.141663), 'val_loss_top10%': np.float64(121611.337524), 'val_loss_cos1': np.float64(0.991429), 'val_loss_entropy': np.float64(2.293833)}}
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2024-10-14 19:46:17,338 (server:353) INFO: Server: Starting evaluation at the end of round 45.
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2024-10-14 19:46:17,339 (server:359) INFO: ----------- Starting a new training round (Round #46) -------------
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2024-10-14 19:48:52,647 (client:354) INFO: {'Role': 'Client #10', 'Round': 46, 'Results_raw': {'train_loss': 14.8451, 'val_loss': 15.370318, 'test_loss': 16.972927}}
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2024-10-14 19:49:51,066 (client:354) INFO: {'Role': 'Client #8', 'Round': 46, 'Results_raw': {'train_loss': 13.147069, 'val_loss': 13.263598, 'test_loss': 13.954139}}
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2024-10-14 19:50:49,280 (client:354) INFO: {'Role': 'Client #4', 'Round': 46, 'Results_raw': {'train_loss': 14.954693, 'val_loss': 15.052363, 'test_loss': 16.034811}}
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2024-10-14 19:51:48,102 (client:354) INFO: {'Role': 'Client #5', 'Round': 46, 'Results_raw': {'train_loss': 15.793649, 'val_loss': 16.582954, 'test_loss': 18.707286}}
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2024-10-14 19:52:47,809 (client:354) INFO: {'Role': 'Client #3', 'Round': 46, 'Results_raw': {'train_loss': 9.986798, 'val_loss': 10.630508, 'test_loss': 12.041126}}
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2024-10-14 19:53:45,423 (client:354) INFO: {'Role': 'Client #2', 'Round': 46, 'Results_raw': {'train_loss': 8.649639, 'val_loss': 8.385497, 'test_loss': 8.973842}}
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2024-10-14 19:54:43,773 (client:354) INFO: {'Role': 'Client #6', 'Round': 46, 'Results_raw': {'train_loss': 14.943425, 'val_loss': 14.941326, 'test_loss': 16.256223}}
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2024-10-14 19:55:42,336 (client:354) INFO: {'Role': 'Client #7', 'Round': 46, 'Results_raw': {'train_loss': 15.191927, 'val_loss': 15.472335, 'test_loss': 16.238023}}
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2024-10-14 19:56:39,623 (client:354) INFO: {'Role': 'Client #1', 'Round': 46, 'Results_raw': {'train_loss': 10.582666, 'val_loss': 10.5537, 'test_loss': 11.744321}}
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2024-10-14 19:57:40,942 (client:354) INFO: {'Role': 'Client #9', 'Round': 46, 'Results_raw': {'train_loss': 17.671695, 'val_loss': 17.788784, 'test_loss': 18.746088}}
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2024-10-14 19:57:40,947 (server:615) INFO: {'Role': 'Server #', 'Round': 45, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.473046), 'test_loss': np.float64(100948.272037), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.516441), 'val_loss': np.float64(101173.228018)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.473046), 'test_loss': np.float64(100948.272037), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.516441), 'val_loss': np.float64(101173.228018)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.314452), 'test_avg_loss_bottom_decile': np.float64(16.535218), 'test_avg_loss_top_decile': np.float64(23.718576), 'test_avg_loss_min': np.float64(15.995709), 'test_avg_loss_max': np.float64(23.718576), 'test_avg_loss_bottom10%': np.float64(15.995709), 'test_avg_loss_top10%': np.float64(23.718576), 'test_avg_loss_cos1': np.float64(0.993011), 'test_avg_loss_entropy': np.float64(2.295536), 'test_loss_std': np.float64(11998.120417), 'test_loss_bottom_decile': np.float64(85718.569977), 'test_loss_top_decile': np.float64(122957.095764), 'test_loss_min': np.float64(82921.755341), 'test_loss_max': np.float64(122957.095764), 'test_loss_bottom10%': np.float64(82921.755341), 'test_loss_top10%': np.float64(122957.095764), 'test_loss_cos1': np.float64(0.993011), 'test_loss_entropy': np.float64(2.295536), 'val_avg_loss_std': np.float64(2.53432), 'val_avg_loss_bottom_decile': np.float64(16.29658), 'val_avg_loss_top_decile': np.float64(23.457412), 'val_avg_loss_min': np.float64(15.540562), 'val_avg_loss_max': np.float64(23.457412), 'val_avg_loss_bottom10%': np.float64(15.540562), 'val_avg_loss_top10%': np.float64(23.457412), 'val_avg_loss_cos1': np.float64(0.991674), 'val_avg_loss_entropy': np.float64(2.294091), 'val_loss_std': np.float64(13137.91356), 'val_loss_bottom_decile': np.float64(84481.472015), 'val_loss_top_decile': np.float64(121603.223877), 'val_loss_min': np.float64(80562.274841), 'val_loss_max': np.float64(121603.223877), 'val_loss_bottom10%': np.float64(80562.274841), 'val_loss_top10%': np.float64(121603.223877), 'val_loss_cos1': np.float64(0.991674), 'val_loss_entropy': np.float64(2.294091)}}
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2024-10-14 19:57:40,994 (server:353) INFO: Server: Starting evaluation at the end of round 46.
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2024-10-14 19:57:40,995 (server:359) INFO: ----------- Starting a new training round (Round #47) -------------
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2024-10-14 20:00:13,561 (client:354) INFO: {'Role': 'Client #7', 'Round': 47, 'Results_raw': {'train_loss': 15.198374, 'val_loss': 15.41821, 'test_loss': 16.107516}}
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2024-10-14 20:01:13,393 (client:354) INFO: {'Role': 'Client #5', 'Round': 47, 'Results_raw': {'train_loss': 15.77955, 'val_loss': 16.687328, 'test_loss': 18.813687}}
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2024-10-14 20:02:18,647 (client:354) INFO: {'Role': 'Client #3', 'Round': 47, 'Results_raw': {'train_loss': 9.980101, 'val_loss': 10.683389, 'test_loss': 12.052074}}
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2024-10-14 20:03:18,828 (client:354) INFO: {'Role': 'Client #8', 'Round': 47, 'Results_raw': {'train_loss': 13.173564, 'val_loss': 13.302613, 'test_loss': 13.774641}}
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2024-10-14 20:04:16,853 (client:354) INFO: {'Role': 'Client #2', 'Round': 47, 'Results_raw': {'train_loss': 8.584805, 'val_loss': 8.528186, 'test_loss': 9.34118}}
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2024-10-14 20:05:17,523 (client:354) INFO: {'Role': 'Client #4', 'Round': 47, 'Results_raw': {'train_loss': 15.001856, 'val_loss': 15.04604, 'test_loss': 16.122485}}
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2024-10-14 20:06:19,709 (client:354) INFO: {'Role': 'Client #1', 'Round': 47, 'Results_raw': {'train_loss': 10.593298, 'val_loss': 10.536645, 'test_loss': 11.6091}}
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2024-10-14 20:07:18,070 (client:354) INFO: {'Role': 'Client #10', 'Round': 47, 'Results_raw': {'train_loss': 14.874312, 'val_loss': 15.435278, 'test_loss': 16.906457}}
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2024-10-14 20:08:17,090 (client:354) INFO: {'Role': 'Client #9', 'Round': 47, 'Results_raw': {'train_loss': 17.638129, 'val_loss': 17.742291, 'test_loss': 18.738839}}
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2024-10-14 20:09:18,295 (client:354) INFO: {'Role': 'Client #6', 'Round': 47, 'Results_raw': {'train_loss': 14.928906, 'val_loss': 14.867234, 'test_loss': 16.150085}}
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2024-10-14 20:09:18,302 (server:615) INFO: {'Role': 'Server #', 'Round': 46, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.327612), 'test_loss': np.float64(100194.342508), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.358731), 'val_loss': np.float64(100355.662634)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.327612), 'test_loss': np.float64(100194.342508), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.358731), 'val_loss': np.float64(100355.662634)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.301592), 'test_avg_loss_bottom_decile': np.float64(16.386344), 'test_avg_loss_top_decile': np.float64(23.520719), 'test_avg_loss_min': np.float64(15.9166), 'test_avg_loss_max': np.float64(23.520719), 'test_avg_loss_bottom10%': np.float64(15.9166), 'test_avg_loss_top10%': np.float64(23.520719), 'test_avg_loss_cos1': np.float64(0.992984), 'test_avg_loss_entropy': np.float64(2.295511), 'test_loss_std': np.float64(11931.454059), 'test_loss_bottom_decile': np.float64(84946.809143), 'test_loss_top_decile': np.float64(121931.404785), 'test_loss_min': np.float64(82511.652069), 'test_loss_max': np.float64(121931.404785), 'test_loss_bottom10%': np.float64(82511.652069), 'test_loss_top10%': np.float64(121931.404785), 'test_loss_cos1': np.float64(0.992984), 'test_loss_entropy': np.float64(2.295511), 'val_avg_loss_std': np.float64(2.52367), 'val_avg_loss_bottom_decile': np.float64(16.117544), 'val_avg_loss_top_decile': np.float64(23.291276), 'val_avg_loss_min': np.float64(15.445585), 'val_avg_loss_max': np.float64(23.291276), 'val_avg_loss_bottom10%': np.float64(15.445585), 'val_avg_loss_top10%': np.float64(23.291276), 'val_avg_loss_cos1': np.float64(0.991609), 'val_avg_loss_entropy': np.float64(2.29403), 'val_loss_std': np.float64(13082.703072), 'val_loss_bottom_decile': np.float64(83553.345673), 'val_loss_top_decile': np.float64(120741.972412), 'val_loss_min': np.float64(80069.911774), 'val_loss_max': np.float64(120741.972412), 'val_loss_bottom10%': np.float64(80069.911774), 'val_loss_top10%': np.float64(120741.972412), 'val_loss_cos1': np.float64(0.991609), 'val_loss_entropy': np.float64(2.29403)}}
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2024-10-14 20:09:18,364 (server:353) INFO: Server: Starting evaluation at the end of round 47.
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2024-10-14 20:09:18,364 (server:359) INFO: ----------- Starting a new training round (Round #48) -------------
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2024-10-14 20:12:01,899 (client:354) INFO: {'Role': 'Client #7', 'Round': 48, 'Results_raw': {'train_loss': 15.160983, 'val_loss': 15.401891, 'test_loss': 16.12569}}
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2024-10-14 20:13:03,969 (client:354) INFO: {'Role': 'Client #4', 'Round': 48, 'Results_raw': {'train_loss': 14.908169, 'val_loss': 15.101577, 'test_loss': 16.234043}}
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2024-10-14 20:14:06,399 (client:354) INFO: {'Role': 'Client #8', 'Round': 48, 'Results_raw': {'train_loss': 13.12065, 'val_loss': 13.134932, 'test_loss': 13.730302}}
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2024-10-14 20:15:00,022 (client:354) INFO: {'Role': 'Client #5', 'Round': 48, 'Results_raw': {'train_loss': 15.742063, 'val_loss': 16.604574, 'test_loss': 18.727511}}
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2024-10-14 20:16:00,646 (client:354) INFO: {'Role': 'Client #6', 'Round': 48, 'Results_raw': {'train_loss': 14.918521, 'val_loss': 15.010798, 'test_loss': 16.592644}}
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2024-10-14 20:16:58,882 (client:354) INFO: {'Role': 'Client #9', 'Round': 48, 'Results_raw': {'train_loss': 17.639385, 'val_loss': 17.690567, 'test_loss': 18.474245}}
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2024-10-14 20:17:57,676 (client:354) INFO: {'Role': 'Client #2', 'Round': 48, 'Results_raw': {'train_loss': 8.69279, 'val_loss': 8.356448, 'test_loss': 8.927832}}
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2024-10-14 20:18:55,892 (client:354) INFO: {'Role': 'Client #10', 'Round': 48, 'Results_raw': {'train_loss': 14.817565, 'val_loss': 15.252165, 'test_loss': 16.960251}}
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2024-10-14 20:19:55,287 (client:354) INFO: {'Role': 'Client #3', 'Round': 48, 'Results_raw': {'train_loss': 9.996504, 'val_loss': 10.696132, 'test_loss': 12.151041}}
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2024-10-14 20:20:54,815 (client:354) INFO: {'Role': 'Client #1', 'Round': 48, 'Results_raw': {'train_loss': 10.550226, 'val_loss': 10.505933, 'test_loss': 11.546818}}
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2024-10-14 20:20:54,820 (server:615) INFO: {'Role': 'Server #', 'Round': 47, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.438435), 'test_loss': np.float64(100768.845673), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.470431), 'val_loss': np.float64(100934.71424)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.438435), 'test_loss': np.float64(100768.845673), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.470431), 'val_loss': np.float64(100934.71424)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.242144), 'test_avg_loss_bottom_decile': np.float64(16.660862), 'test_avg_loss_top_decile': np.float64(23.60525), 'test_avg_loss_min': np.float64(16.092263), 'test_avg_loss_max': np.float64(23.60525), 'test_avg_loss_bottom10%': np.float64(16.092263), 'test_avg_loss_top10%': np.float64(23.60525), 'test_avg_loss_cos1': np.float64(0.993413), 'test_avg_loss_entropy': np.float64(2.295958), 'test_loss_std': np.float64(11623.276553), 'test_loss_bottom_decile': np.float64(86369.907288), 'test_loss_top_decile': np.float64(122369.617065), 'test_loss_min': np.float64(83422.292206), 'test_loss_max': np.float64(122369.617065), 'test_loss_bottom10%': np.float64(83422.292206), 'test_loss_top10%': np.float64(122369.617065), 'test_loss_cos1': np.float64(0.993413), 'test_loss_entropy': np.float64(2.295958), 'val_avg_loss_std': np.float64(2.459834), 'val_avg_loss_bottom_decile': np.float64(16.399224), 'val_avg_loss_top_decile': np.float64(23.3577), 'val_avg_loss_min': np.float64(15.624224), 'val_avg_loss_max': np.float64(23.3577), 'val_avg_loss_bottom10%': np.float64(15.624224), 'val_avg_loss_top10%': np.float64(23.3577), 'val_avg_loss_cos1': np.float64(0.992114), 'val_avg_loss_entropy': np.float64(2.294559), 'val_loss_std': np.float64(12751.778884), 'val_loss_bottom_decile': np.float64(85013.575867), 'val_loss_top_decile': np.float64(121086.316162), 'val_loss_min': np.float64(80995.975861), 'val_loss_max': np.float64(121086.316162), 'val_loss_bottom10%': np.float64(80995.975861), 'val_loss_top10%': np.float64(121086.316162), 'val_loss_cos1': np.float64(0.992114), 'val_loss_entropy': np.float64(2.294559)}}
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2024-10-14 20:20:54,863 (server:353) INFO: Server: Starting evaluation at the end of round 48.
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2024-10-14 20:20:54,863 (server:359) INFO: ----------- Starting a new training round (Round #49) -------------
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2024-10-14 20:23:30,962 (client:354) INFO: {'Role': 'Client #10', 'Round': 49, 'Results_raw': {'train_loss': 14.802738, 'val_loss': 15.404297, 'test_loss': 16.744588}}
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2024-10-14 20:24:29,897 (client:354) INFO: {'Role': 'Client #3', 'Round': 49, 'Results_raw': {'train_loss': 10.051556, 'val_loss': 10.569611, 'test_loss': 12.060979}}
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2024-10-14 20:25:28,781 (client:354) INFO: {'Role': 'Client #2', 'Round': 49, 'Results_raw': {'train_loss': 8.610073, 'val_loss': 8.631755, 'test_loss': 9.318622}}
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2024-10-14 20:26:24,758 (client:354) INFO: {'Role': 'Client #6', 'Round': 49, 'Results_raw': {'train_loss': 14.929371, 'val_loss': 14.834402, 'test_loss': 16.531995}}
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2024-10-14 20:27:24,086 (client:354) INFO: {'Role': 'Client #7', 'Round': 49, 'Results_raw': {'train_loss': 15.166666, 'val_loss': 15.566624, 'test_loss': 16.303851}}
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2024-10-14 20:28:22,462 (client:354) INFO: {'Role': 'Client #4', 'Round': 49, 'Results_raw': {'train_loss': 14.955013, 'val_loss': 15.07409, 'test_loss': 16.221208}}
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2024-10-14 20:29:21,261 (client:354) INFO: {'Role': 'Client #5', 'Round': 49, 'Results_raw': {'train_loss': 15.786972, 'val_loss': 16.470591, 'test_loss': 18.570113}}
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2024-10-14 20:30:19,089 (client:354) INFO: {'Role': 'Client #9', 'Round': 49, 'Results_raw': {'train_loss': 17.644231, 'val_loss': 17.669698, 'test_loss': 18.504186}}
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2024-10-14 20:31:20,280 (client:354) INFO: {'Role': 'Client #8', 'Round': 49, 'Results_raw': {'train_loss': 13.127454, 'val_loss': 13.126085, 'test_loss': 13.762903}}
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2024-10-14 20:32:20,722 (client:354) INFO: {'Role': 'Client #1', 'Round': 49, 'Results_raw': {'train_loss': 10.599355, 'val_loss': 10.417089, 'test_loss': 11.607423}}
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2024-10-14 20:32:20,726 (server:615) INFO: {'Role': 'Server #', 'Round': 48, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.296317), 'test_loss': np.float64(100032.109125), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.329323), 'val_loss': np.float64(100203.211209)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.296317), 'test_loss': np.float64(100032.109125), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.329323), 'val_loss': np.float64(100203.211209)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.339086), 'test_avg_loss_bottom_decile': np.float64(16.321342), 'test_avg_loss_top_decile': np.float64(23.540695), 'test_avg_loss_min': np.float64(15.845278), 'test_avg_loss_max': np.float64(23.540695), 'test_avg_loss_bottom10%': np.float64(15.845278), 'test_avg_loss_top10%': np.float64(23.540695), 'test_avg_loss_cos1': np.float64(0.992733), 'test_avg_loss_entropy': np.float64(2.295255), 'test_loss_std': np.float64(12125.822756), 'test_loss_bottom_decile': np.float64(84609.839264), 'test_loss_top_decile': np.float64(122034.962708), 'test_loss_min': np.float64(82141.921204), 'test_loss_max': np.float64(122034.962708), 'test_loss_bottom10%': np.float64(82141.921204), 'test_loss_top10%': np.float64(122034.962708), 'test_loss_cos1': np.float64(0.992733), 'test_loss_entropy': np.float64(2.295255), 'val_avg_loss_std': np.float64(2.572595), 'val_avg_loss_bottom_decile': np.float64(16.038962), 'val_avg_loss_top_decile': np.float64(23.302995), 'val_avg_loss_min': np.float64(15.364304), 'val_avg_loss_max': np.float64(23.302995), 'val_avg_loss_bottom10%': np.float64(15.364304), 'val_avg_loss_top10%': np.float64(23.302995), 'val_avg_loss_cos1': np.float64(0.991259), 'val_avg_loss_entropy': np.float64(2.293666), 'val_loss_std': np.float64(13336.33439), 'val_loss_bottom_decile': np.float64(83145.980957), 'val_loss_top_decile': np.float64(120802.728027), 'val_loss_min': np.float64(79648.550568), 'val_loss_max': np.float64(120802.728027), 'val_loss_bottom10%': np.float64(79648.550568), 'val_loss_top10%': np.float64(120802.728027), 'val_loss_cos1': np.float64(0.991259), 'val_loss_entropy': np.float64(2.293666)}}
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2024-10-14 20:32:20,771 (server:353) INFO: Server: Starting evaluation at the end of round 49.
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2024-10-14 20:32:20,771 (server:359) INFO: ----------- Starting a new training round (Round #50) -------------
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2024-10-14 20:34:51,836 (client:354) INFO: {'Role': 'Client #8', 'Round': 50, 'Results_raw': {'train_loss': 13.134785, 'val_loss': 13.097726, 'test_loss': 13.75258}}
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2024-10-14 20:35:50,219 (client:354) INFO: {'Role': 'Client #1', 'Round': 50, 'Results_raw': {'train_loss': 10.534406, 'val_loss': 10.408821, 'test_loss': 11.505532}}
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2024-10-14 20:36:49,202 (client:354) INFO: {'Role': 'Client #9', 'Round': 50, 'Results_raw': {'train_loss': 17.595494, 'val_loss': 17.669204, 'test_loss': 18.533025}}
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2024-10-14 20:37:57,490 (client:354) INFO: {'Role': 'Client #10', 'Round': 50, 'Results_raw': {'train_loss': 14.826113, 'val_loss': 15.330896, 'test_loss': 17.111811}}
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2024-10-14 20:38:57,789 (client:354) INFO: {'Role': 'Client #3', 'Round': 50, 'Results_raw': {'train_loss': 9.985065, 'val_loss': 10.568209, 'test_loss': 11.940598}}
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2024-10-14 20:39:57,484 (client:354) INFO: {'Role': 'Client #5', 'Round': 50, 'Results_raw': {'train_loss': 15.760231, 'val_loss': 16.707438, 'test_loss': 18.930356}}
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2024-10-14 20:40:57,160 (client:354) INFO: {'Role': 'Client #7', 'Round': 50, 'Results_raw': {'train_loss': 15.174548, 'val_loss': 15.497058, 'test_loss': 15.952789}}
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2024-10-14 20:41:55,538 (client:354) INFO: {'Role': 'Client #2', 'Round': 50, 'Results_raw': {'train_loss': 8.644412, 'val_loss': 8.372702, 'test_loss': 8.97355}}
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2024-10-14 20:42:55,032 (client:354) INFO: {'Role': 'Client #6', 'Round': 50, 'Results_raw': {'train_loss': 14.889444, 'val_loss': 14.860817, 'test_loss': 16.148998}}
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2024-10-14 20:43:52,527 (client:354) INFO: {'Role': 'Client #4', 'Round': 50, 'Results_raw': {'train_loss': 14.92304, 'val_loss': 15.023459, 'test_loss': 16.167269}}
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2024-10-14 20:43:52,532 (server:615) INFO: {'Role': 'Server #', 'Round': 49, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.387677), 'test_loss': np.float64(100505.715784), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.405542), 'val_loss': np.float64(100598.331586)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.387677), 'test_loss': np.float64(100505.715784), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.405542), 'val_loss': np.float64(100598.331586)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.214102), 'test_avg_loss_bottom_decile': np.float64(16.686554), 'test_avg_loss_top_decile': np.float64(23.473802), 'test_avg_loss_min': np.float64(16.041367), 'test_avg_loss_max': np.float64(23.473802), 'test_avg_loss_bottom10%': np.float64(16.041367), 'test_avg_loss_top10%': np.float64(23.473802), 'test_avg_loss_cos1': np.float64(0.993542), 'test_avg_loss_entropy': np.float64(2.296087), 'test_loss_std': np.float64(11477.906277), 'test_loss_bottom_decile': np.float64(86503.094879), 'test_loss_top_decile': np.float64(121688.189575), 'test_loss_min': np.float64(83158.448059), 'test_loss_max': np.float64(121688.189575), 'test_loss_bottom10%': np.float64(83158.448059), 'test_loss_top10%': np.float64(121688.189575), 'test_loss_cos1': np.float64(0.993542), 'test_loss_entropy': np.float64(2.296087), 'val_avg_loss_std': np.float64(2.430888), 'val_avg_loss_bottom_decile': np.float64(16.405255), 'val_avg_loss_top_decile': np.float64(23.206557), 'val_avg_loss_min': np.float64(15.565813), 'val_avg_loss_max': np.float64(23.206557), 'val_avg_loss_bottom10%': np.float64(15.565813), 'val_avg_loss_top10%': np.float64(23.206557), 'val_avg_loss_cos1': np.float64(0.992245), 'val_avg_loss_entropy': np.float64(2.294695), 'val_loss_std': np.float64(12601.725065), 'val_loss_bottom_decile': np.float64(85044.842773), 'val_loss_top_decile': np.float64(120302.792175), 'val_loss_min': np.float64(80693.17691), 'val_loss_max': np.float64(120302.792175), 'val_loss_bottom10%': np.float64(80693.17691), 'val_loss_top10%': np.float64(120302.792175), 'val_loss_cos1': np.float64(0.992245), 'val_loss_entropy': np.float64(2.294695)}}
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2024-10-14 20:43:52,575 (server:353) INFO: Server: Starting evaluation at the end of round 50.
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2024-10-14 20:43:52,576 (server:359) INFO: ----------- Starting a new training round (Round #51) -------------
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2024-10-14 20:46:26,013 (client:354) INFO: {'Role': 'Client #8', 'Round': 51, 'Results_raw': {'train_loss': 13.120302, 'val_loss': 13.156173, 'test_loss': 13.760854}}
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2024-10-14 20:47:27,508 (client:354) INFO: {'Role': 'Client #1', 'Round': 51, 'Results_raw': {'train_loss': 10.550744, 'val_loss': 10.464614, 'test_loss': 11.641581}}
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2024-10-14 20:48:22,218 (client:354) INFO: {'Role': 'Client #4', 'Round': 51, 'Results_raw': {'train_loss': 14.9362, 'val_loss': 15.288669, 'test_loss': 16.334524}}
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2024-10-14 20:49:24,277 (client:354) INFO: {'Role': 'Client #10', 'Round': 51, 'Results_raw': {'train_loss': 14.79278, 'val_loss': 15.256102, 'test_loss': 16.93319}}
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2024-10-14 20:50:23,882 (client:354) INFO: {'Role': 'Client #3', 'Round': 51, 'Results_raw': {'train_loss': 9.936761, 'val_loss': 10.694357, 'test_loss': 12.117802}}
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2024-10-14 20:51:24,006 (client:354) INFO: {'Role': 'Client #2', 'Round': 51, 'Results_raw': {'train_loss': 8.607658, 'val_loss': 8.496445, 'test_loss': 9.092176}}
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2024-10-14 20:52:22,687 (client:354) INFO: {'Role': 'Client #7', 'Round': 51, 'Results_raw': {'train_loss': 15.158547, 'val_loss': 15.574722, 'test_loss': 16.121732}}
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2024-10-14 20:53:21,588 (client:354) INFO: {'Role': 'Client #5', 'Round': 51, 'Results_raw': {'train_loss': 15.738698, 'val_loss': 16.769372, 'test_loss': 18.815134}}
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2024-10-14 20:54:20,518 (client:354) INFO: {'Role': 'Client #9', 'Round': 51, 'Results_raw': {'train_loss': 17.597877, 'val_loss': 17.861579, 'test_loss': 18.956764}}
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2024-10-14 20:55:24,117 (client:354) INFO: {'Role': 'Client #6', 'Round': 51, 'Results_raw': {'train_loss': 14.902988, 'val_loss': 15.000093, 'test_loss': 16.165256}}
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2024-10-14 20:55:24,121 (server:615) INFO: {'Role': 'Server #', 'Round': 50, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.217519), 'test_loss': np.float64(99623.618463), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.245184), 'val_loss': np.float64(99767.035187)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.217519), 'test_loss': np.float64(99623.618463), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.245184), 'val_loss': np.float64(99767.035187)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.276524), 'test_avg_loss_bottom_decile': np.float64(16.337485), 'test_avg_loss_top_decile': np.float64(23.340278), 'test_avg_loss_min': np.float64(15.851117), 'test_avg_loss_max': np.float64(23.340278), 'test_avg_loss_bottom10%': np.float64(15.851117), 'test_avg_loss_top10%': np.float64(23.340278), 'test_avg_loss_cos1': np.float64(0.993057), 'test_avg_loss_entropy': np.float64(2.295586), 'test_loss_std': np.float64(11801.502984), 'test_loss_bottom_decile': np.float64(84693.523438), 'test_loss_top_decile': np.float64(120996.000671), 'test_loss_min': np.float64(82172.189056), 'test_loss_max': np.float64(120996.000671), 'test_loss_bottom10%': np.float64(82172.189056), 'test_loss_top10%': np.float64(120996.000671), 'test_loss_cos1': np.float64(0.993057), 'test_loss_entropy': np.float64(2.295586), 'val_avg_loss_std': np.float64(2.499686), 'val_avg_loss_bottom_decile': np.float64(16.060768), 'val_avg_loss_top_decile': np.float64(23.096227), 'val_avg_loss_min': np.float64(15.389959), 'val_avg_loss_max': np.float64(23.096227), 'val_avg_loss_bottom10%': np.float64(15.389959), 'val_avg_loss_top10%': np.float64(23.096227), 'val_avg_loss_cos1': np.float64(0.99167), 'val_avg_loss_entropy': np.float64(2.294094), 'val_loss_std': np.float64(12958.371759), 'val_loss_bottom_decile': np.float64(83259.020874), 'val_loss_top_decile': np.float64(119730.839478), 'val_loss_min': np.float64(79781.545471), 'val_loss_max': np.float64(119730.839478), 'val_loss_bottom10%': np.float64(79781.545471), 'val_loss_top10%': np.float64(119730.839478), 'val_loss_cos1': np.float64(0.99167), 'val_loss_entropy': np.float64(2.294094)}}
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2024-10-14 20:55:24,166 (server:353) INFO: Server: Starting evaluation at the end of round 51.
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2024-10-14 20:55:24,166 (server:359) INFO: ----------- Starting a new training round (Round #52) -------------
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2024-10-14 20:57:58,133 (client:354) INFO: {'Role': 'Client #10', 'Round': 52, 'Results_raw': {'train_loss': 14.800601, 'val_loss': 15.298189, 'test_loss': 17.015837}}
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2024-10-14 20:58:57,883 (client:354) INFO: {'Role': 'Client #1', 'Round': 52, 'Results_raw': {'train_loss': 10.508329, 'val_loss': 10.436263, 'test_loss': 11.556203}}
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2024-10-14 20:59:55,582 (client:354) INFO: {'Role': 'Client #5', 'Round': 52, 'Results_raw': {'train_loss': 15.724974, 'val_loss': 16.602546, 'test_loss': 18.808758}}
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2024-10-14 21:00:52,557 (client:354) INFO: {'Role': 'Client #4', 'Round': 52, 'Results_raw': {'train_loss': 14.891501, 'val_loss': 15.032917, 'test_loss': 16.04216}}
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2024-10-14 21:01:55,194 (client:354) INFO: {'Role': 'Client #7', 'Round': 52, 'Results_raw': {'train_loss': 15.124112, 'val_loss': 15.499701, 'test_loss': 16.077129}}
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2024-10-14 21:02:54,456 (client:354) INFO: {'Role': 'Client #9', 'Round': 52, 'Results_raw': {'train_loss': 17.567909, 'val_loss': 18.015763, 'test_loss': 19.078977}}
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2024-10-14 21:03:55,630 (client:354) INFO: {'Role': 'Client #3', 'Round': 52, 'Results_raw': {'train_loss': 9.934894, 'val_loss': 10.560639, 'test_loss': 12.022134}}
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2024-10-14 21:04:54,146 (client:354) INFO: {'Role': 'Client #6', 'Round': 52, 'Results_raw': {'train_loss': 14.871041, 'val_loss': 14.918706, 'test_loss': 16.297112}}
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2024-10-14 21:05:52,562 (client:354) INFO: {'Role': 'Client #2', 'Round': 52, 'Results_raw': {'train_loss': 8.632462, 'val_loss': 8.383642, 'test_loss': 9.042136}}
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2024-10-14 21:06:50,932 (client:354) INFO: {'Role': 'Client #8', 'Round': 52, 'Results_raw': {'train_loss': 13.093019, 'val_loss': 13.15005, 'test_loss': 13.741248}}
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2024-10-14 21:06:50,935 (server:615) INFO: {'Role': 'Server #', 'Round': 51, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.418488), 'test_loss': np.float64(100665.440973), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.434439), 'val_loss': np.float64(100748.131622)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.418488), 'test_loss': np.float64(100665.440973), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.434439), 'val_loss': np.float64(100748.131622)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.207833), 'test_avg_loss_bottom_decile': np.float64(16.763187), 'test_avg_loss_top_decile': np.float64(23.535171), 'test_avg_loss_min': np.float64(16.066621), 'test_avg_loss_max': np.float64(23.535171), 'test_avg_loss_bottom10%': np.float64(16.066621), 'test_avg_loss_top10%': np.float64(23.535171), 'test_avg_loss_cos1': np.float64(0.993598), 'test_avg_loss_entropy': np.float64(2.296148), 'test_loss_std': np.float64(11445.407966), 'test_loss_bottom_decile': np.float64(86900.361816), 'test_loss_top_decile': np.float64(122006.326965), 'test_loss_min': np.float64(83289.365082), 'test_loss_max': np.float64(122006.326965), 'test_loss_bottom10%': np.float64(83289.365082), 'test_loss_top10%': np.float64(122006.326965), 'test_loss_cos1': np.float64(0.993598), 'test_loss_entropy': np.float64(2.296148), 'val_avg_loss_std': np.float64(2.425048), 'val_avg_loss_bottom_decile': np.float64(16.463764), 'val_avg_loss_top_decile': np.float64(23.272788), 'val_avg_loss_min': np.float64(15.593926), 'val_avg_loss_max': np.float64(23.272788), 'val_avg_loss_bottom10%': np.float64(15.593926), 'val_avg_loss_top10%': np.float64(23.272788), 'val_avg_loss_cos1': np.float64(0.992305), 'val_avg_loss_entropy': np.float64(2.29476), 'val_loss_std': np.float64(12571.446909), 'val_loss_bottom_decile': np.float64(85348.150177), 'val_loss_top_decile': np.float64(120646.135071), 'val_loss_min': np.float64(80838.910736), 'val_loss_max': np.float64(120646.135071), 'val_loss_bottom10%': np.float64(80838.910736), 'val_loss_top10%': np.float64(120646.135071), 'val_loss_cos1': np.float64(0.992305), 'val_loss_entropy': np.float64(2.29476)}}
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2024-10-14 21:06:50,970 (server:353) INFO: Server: Starting evaluation at the end of round 52.
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2024-10-14 21:06:50,971 (server:359) INFO: ----------- Starting a new training round (Round #53) -------------
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2024-10-14 21:09:36,260 (client:354) INFO: {'Role': 'Client #9', 'Round': 53, 'Results_raw': {'train_loss': 17.563528, 'val_loss': 17.765159, 'test_loss': 18.624946}}
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2024-10-14 21:10:36,779 (client:354) INFO: {'Role': 'Client #1', 'Round': 53, 'Results_raw': {'train_loss': 10.521239, 'val_loss': 10.421138, 'test_loss': 11.393361}}
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2024-10-14 21:11:39,981 (client:354) INFO: {'Role': 'Client #2', 'Round': 53, 'Results_raw': {'train_loss': 8.572651, 'val_loss': 8.462444, 'test_loss': 9.036271}}
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2024-10-14 21:12:38,869 (client:354) INFO: {'Role': 'Client #6', 'Round': 53, 'Results_raw': {'train_loss': 14.857828, 'val_loss': 14.94479, 'test_loss': 16.217686}}
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2024-10-14 21:13:37,168 (client:354) INFO: {'Role': 'Client #3', 'Round': 53, 'Results_raw': {'train_loss': 9.969498, 'val_loss': 10.731975, 'test_loss': 12.17368}}
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2024-10-14 21:14:38,770 (client:354) INFO: {'Role': 'Client #4', 'Round': 53, 'Results_raw': {'train_loss': 14.917901, 'val_loss': 15.010603, 'test_loss': 16.060458}}
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2024-10-14 21:15:37,708 (client:354) INFO: {'Role': 'Client #10', 'Round': 53, 'Results_raw': {'train_loss': 14.748326, 'val_loss': 15.334953, 'test_loss': 16.810821}}
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2024-10-14 21:16:38,445 (client:354) INFO: {'Role': 'Client #8', 'Round': 53, 'Results_raw': {'train_loss': 13.092628, 'val_loss': 13.232468, 'test_loss': 13.908923}}
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2024-10-14 21:17:36,582 (client:354) INFO: {'Role': 'Client #7', 'Round': 53, 'Results_raw': {'train_loss': 15.145301, 'val_loss': 15.582629, 'test_loss': 16.565256}}
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2024-10-14 21:18:35,826 (client:354) INFO: {'Role': 'Client #5', 'Round': 53, 'Results_raw': {'train_loss': 15.693854, 'val_loss': 16.63594, 'test_loss': 18.901501}}
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2024-10-14 21:18:35,829 (server:615) INFO: {'Role': 'Server #', 'Round': 52, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.191489), 'test_loss': np.float64(99488.676501), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.219654), 'val_loss': np.float64(99634.688708)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.191489), 'test_loss': np.float64(99488.676501), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.219654), 'val_loss': np.float64(99634.688708)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.353363), 'test_avg_loss_bottom_decile': np.float64(16.04472), 'test_avg_loss_top_decile': np.float64(23.382976), 'test_avg_loss_min': np.float64(15.723686), 'test_avg_loss_max': np.float64(23.382976), 'test_avg_loss_bottom10%': np.float64(15.723686), 'test_avg_loss_top10%': np.float64(23.382976), 'test_avg_loss_cos1': np.float64(0.992565), 'test_avg_loss_entropy': np.float64(2.295065), 'test_loss_std': np.float64(12199.835915), 'test_loss_bottom_decile': np.float64(83175.826233), 'test_loss_top_decile': np.float64(121217.349792), 'test_loss_min': np.float64(81511.586151), 'test_loss_max': np.float64(121217.349792), 'test_loss_bottom10%': np.float64(81511.586151), 'test_loss_top10%': np.float64(121217.349792), 'test_loss_cos1': np.float64(0.992565), 'test_loss_entropy': np.float64(2.295065), 'val_avg_loss_std': np.float64(2.578769), 'val_avg_loss_bottom_decile': np.float64(15.79294), 'val_avg_loss_top_decile': np.float64(23.137142), 'val_avg_loss_min': np.float64(15.258265), 'val_avg_loss_max': np.float64(23.137142), 'val_avg_loss_bottom10%': np.float64(15.258265), 'val_avg_loss_top10%': np.float64(23.137142), 'val_avg_loss_cos1': np.float64(0.991118), 'val_avg_loss_entropy': np.float64(2.293503), 'val_loss_std': np.float64(13368.336652), 'val_loss_bottom_decile': np.float64(81870.602692), 'val_loss_top_decile': np.float64(119942.941956), 'val_loss_min': np.float64(79098.848053), 'val_loss_max': np.float64(119942.941956), 'val_loss_bottom10%': np.float64(79098.848053), 'val_loss_top10%': np.float64(119942.941956), 'val_loss_cos1': np.float64(0.991118), 'val_loss_entropy': np.float64(2.293503)}}
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2024-10-14 21:18:35,867 (server:353) INFO: Server: Starting evaluation at the end of round 53.
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2024-10-14 21:18:35,867 (server:359) INFO: ----------- Starting a new training round (Round #54) -------------
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2024-10-14 21:21:10,908 (client:354) INFO: {'Role': 'Client #5', 'Round': 54, 'Results_raw': {'train_loss': 15.737867, 'val_loss': 16.723875, 'test_loss': 19.092215}}
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2024-10-14 21:22:11,394 (client:354) INFO: {'Role': 'Client #4', 'Round': 54, 'Results_raw': {'train_loss': 14.839307, 'val_loss': 15.004652, 'test_loss': 16.092816}}
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2024-10-14 21:23:08,484 (client:354) INFO: {'Role': 'Client #2', 'Round': 54, 'Results_raw': {'train_loss': 8.571606, 'val_loss': 8.386206, 'test_loss': 8.956247}}
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2024-10-14 21:24:09,377 (client:354) INFO: {'Role': 'Client #9', 'Round': 54, 'Results_raw': {'train_loss': 17.614545, 'val_loss': 17.793047, 'test_loss': 18.623131}}
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2024-10-14 21:25:08,411 (client:354) INFO: {'Role': 'Client #7', 'Round': 54, 'Results_raw': {'train_loss': 15.114876, 'val_loss': 15.579616, 'test_loss': 16.370667}}
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2024-10-14 21:26:09,880 (client:354) INFO: {'Role': 'Client #10', 'Round': 54, 'Results_raw': {'train_loss': 14.783765, 'val_loss': 15.228723, 'test_loss': 16.724768}}
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2024-10-14 21:27:08,911 (client:354) INFO: {'Role': 'Client #3', 'Round': 54, 'Results_raw': {'train_loss': 9.983356, 'val_loss': 10.52925, 'test_loss': 11.863029}}
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2024-10-14 21:28:10,875 (client:354) INFO: {'Role': 'Client #8', 'Round': 54, 'Results_raw': {'train_loss': 13.070056, 'val_loss': 13.232617, 'test_loss': 14.015524}}
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2024-10-14 21:29:08,890 (client:354) INFO: {'Role': 'Client #1', 'Round': 54, 'Results_raw': {'train_loss': 10.524868, 'val_loss': 10.446979, 'test_loss': 11.542817}}
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2024-10-14 21:30:06,588 (client:354) INFO: {'Role': 'Client #6', 'Round': 54, 'Results_raw': {'train_loss': 14.87438, 'val_loss': 14.839033, 'test_loss': 16.20119}}
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2024-10-14 21:30:06,592 (server:615) INFO: {'Role': 'Server #', 'Round': 53, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.18745), 'test_loss': np.float64(99467.742093), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.210891), 'val_loss': np.float64(99589.25726)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.18745), 'test_loss': np.float64(99467.742093), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.210891), 'val_loss': np.float64(99589.25726)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.299242), 'test_avg_loss_bottom_decile': np.float64(16.258243), 'test_avg_loss_top_decile': np.float64(23.419489), 'test_avg_loss_min': np.float64(15.783505), 'test_avg_loss_max': np.float64(23.419489), 'test_avg_loss_bottom10%': np.float64(15.783505), 'test_avg_loss_top10%': np.float64(23.419489), 'test_avg_loss_cos1': np.float64(0.992897), 'test_avg_loss_entropy': np.float64(2.295425), 'test_loss_std': np.float64(11919.269789), 'test_loss_bottom_decile': np.float64(84282.729919), 'test_loss_top_decile': np.float64(121406.631897), 'test_loss_min': np.float64(81821.689484), 'test_loss_max': np.float64(121406.631897), 'test_loss_bottom10%': np.float64(81821.689484), 'test_loss_top10%': np.float64(121406.631897), 'test_loss_cos1': np.float64(0.992897), 'test_loss_entropy': np.float64(2.295425), 'val_avg_loss_std': np.float64(2.519044), 'val_avg_loss_bottom_decile': np.float64(15.99224), 'val_avg_loss_top_decile': np.float64(23.1865), 'val_avg_loss_min': np.float64(15.330195), 'val_avg_loss_max': np.float64(23.1865), 'val_avg_loss_bottom10%': np.float64(15.330195), 'val_avg_loss_top10%': np.float64(23.1865), 'val_avg_loss_cos1': np.float64(0.991512), 'val_avg_loss_entropy': np.float64(2.293934), 'val_loss_std': np.float64(13058.724324), 'val_loss_bottom_decile': np.float64(82903.770569), 'val_loss_top_decile': np.float64(120198.817505), 'val_loss_min': np.float64(79471.731628), 'val_loss_max': np.float64(120198.817505), 'val_loss_bottom10%': np.float64(79471.731628), 'val_loss_top10%': np.float64(120198.817505), 'val_loss_cos1': np.float64(0.991512), 'val_loss_entropy': np.float64(2.293934)}}
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2024-10-14 21:30:06,633 (server:353) INFO: Server: Starting evaluation at the end of round 54.
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2024-10-14 21:30:06,634 (server:359) INFO: ----------- Starting a new training round (Round #55) -------------
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2024-10-14 21:32:42,765 (client:354) INFO: {'Role': 'Client #5', 'Round': 55, 'Results_raw': {'train_loss': 15.715836, 'val_loss': 16.693352, 'test_loss': 18.914218}}
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2024-10-14 21:33:44,471 (client:354) INFO: {'Role': 'Client #2', 'Round': 55, 'Results_raw': {'train_loss': 8.580938, 'val_loss': 8.605305, 'test_loss': 9.181384}}
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2024-10-14 21:34:49,328 (client:354) INFO: {'Role': 'Client #3', 'Round': 55, 'Results_raw': {'train_loss': 9.915373, 'val_loss': 10.714741, 'test_loss': 12.236844}}
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2024-10-14 21:35:53,370 (client:354) INFO: {'Role': 'Client #7', 'Round': 55, 'Results_raw': {'train_loss': 15.100436, 'val_loss': 15.39338, 'test_loss': 15.901054}}
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2024-10-14 21:36:50,291 (client:354) INFO: {'Role': 'Client #6', 'Round': 55, 'Results_raw': {'train_loss': 14.840015, 'val_loss': 14.841769, 'test_loss': 16.30151}}
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2024-10-14 21:37:49,194 (client:354) INFO: {'Role': 'Client #9', 'Round': 55, 'Results_raw': {'train_loss': 17.591935, 'val_loss': 17.989101, 'test_loss': 18.985882}}
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2024-10-14 21:38:50,484 (client:354) INFO: {'Role': 'Client #1', 'Round': 55, 'Results_raw': {'train_loss': 10.465589, 'val_loss': 10.453541, 'test_loss': 11.439862}}
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2024-10-14 21:39:51,393 (client:354) INFO: {'Role': 'Client #10', 'Round': 55, 'Results_raw': {'train_loss': 14.751355, 'val_loss': 15.223343, 'test_loss': 16.664099}}
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2024-10-14 21:40:48,397 (client:354) INFO: {'Role': 'Client #4', 'Round': 55, 'Results_raw': {'train_loss': 14.853328, 'val_loss': 15.056428, 'test_loss': 16.323924}}
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2024-10-14 21:41:45,711 (client:354) INFO: {'Role': 'Client #8', 'Round': 55, 'Results_raw': {'train_loss': 13.094334, 'val_loss': 13.168189, 'test_loss': 13.772302}}
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2024-10-14 21:41:45,717 (server:615) INFO: {'Role': 'Server #', 'Round': 54, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.303246), 'test_loss': np.float64(100068.027734), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.315462), 'val_loss': np.float64(100131.355557)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.303246), 'test_loss': np.float64(100068.027734), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.315462), 'val_loss': np.float64(100131.355557)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.257572), 'test_avg_loss_bottom_decile': np.float64(16.56503), 'test_avg_loss_top_decile': np.float64(23.527547), 'test_avg_loss_min': np.float64(15.908329), 'test_avg_loss_max': np.float64(23.527547), 'test_avg_loss_bottom10%': np.float64(15.908329), 'test_avg_loss_top10%': np.float64(23.527547), 'test_avg_loss_cos1': np.float64(0.99323), 'test_avg_loss_entropy': np.float64(2.295776), 'test_loss_std': np.float64(11703.251221), 'test_loss_bottom_decile': np.float64(85873.114136), 'test_loss_top_decile': np.float64(121966.804626), 'test_loss_min': np.float64(82468.775818), 'test_loss_max': np.float64(121966.804626), 'test_loss_bottom10%': np.float64(82468.775818), 'test_loss_top10%': np.float64(121966.804626), 'test_loss_cos1': np.float64(0.99323), 'test_loss_entropy': np.float64(2.295776), 'val_avg_loss_std': np.float64(2.476933), 'val_avg_loss_bottom_decile': np.float64(16.268805), 'val_avg_loss_top_decile': np.float64(23.276829), 'val_avg_loss_min': np.float64(15.431111), 'val_avg_loss_max': np.float64(23.276829), 'val_avg_loss_bottom10%': np.float64(15.431111), 'val_avg_loss_top10%': np.float64(23.276829), 'val_avg_loss_cos1': np.float64(0.991878), 'val_avg_loss_entropy': np.float64(2.294322), 'val_loss_std': np.float64(12840.419722), 'val_loss_bottom_decile': np.float64(84337.487366), 'val_loss_top_decile': np.float64(120667.081848), 'val_loss_min': np.float64(79994.878021), 'val_loss_max': np.float64(120667.081848), 'val_loss_bottom10%': np.float64(79994.878021), 'val_loss_top10%': np.float64(120667.081848), 'val_loss_cos1': np.float64(0.991878), 'val_loss_entropy': np.float64(2.294322)}}
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2024-10-14 21:41:45,772 (server:353) INFO: Server: Starting evaluation at the end of round 55.
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2024-10-14 21:41:45,773 (server:359) INFO: ----------- Starting a new training round (Round #56) -------------
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2024-10-14 21:44:19,793 (client:354) INFO: {'Role': 'Client #5', 'Round': 56, 'Results_raw': {'train_loss': 15.669873, 'val_loss': 16.657878, 'test_loss': 18.855705}}
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2024-10-14 21:45:21,286 (client:354) INFO: {'Role': 'Client #10', 'Round': 56, 'Results_raw': {'train_loss': 14.761171, 'val_loss': 15.350031, 'test_loss': 17.239441}}
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2024-10-14 21:46:23,483 (client:354) INFO: {'Role': 'Client #7', 'Round': 56, 'Results_raw': {'train_loss': 15.090265, 'val_loss': 15.433392, 'test_loss': 16.123124}}
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2024-10-14 21:47:28,580 (client:354) INFO: {'Role': 'Client #8', 'Round': 56, 'Results_raw': {'train_loss': 13.068458, 'val_loss': 13.205624, 'test_loss': 13.814461}}
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2024-10-14 21:48:28,888 (client:354) INFO: {'Role': 'Client #9', 'Round': 56, 'Results_raw': {'train_loss': 17.592934, 'val_loss': 17.769842, 'test_loss': 18.699267}}
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2024-10-14 21:49:25,237 (client:354) INFO: {'Role': 'Client #4', 'Round': 56, 'Results_raw': {'train_loss': 14.871257, 'val_loss': 15.000405, 'test_loss': 16.099756}}
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2024-10-14 21:50:23,726 (client:354) INFO: {'Role': 'Client #3', 'Round': 56, 'Results_raw': {'train_loss': 9.921665, 'val_loss': 10.658927, 'test_loss': 12.13185}}
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2024-10-14 21:51:22,079 (client:354) INFO: {'Role': 'Client #1', 'Round': 56, 'Results_raw': {'train_loss': 10.518756, 'val_loss': 10.473801, 'test_loss': 11.472522}}
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2024-10-14 21:52:23,516 (client:354) INFO: {'Role': 'Client #2', 'Round': 56, 'Results_raw': {'train_loss': 8.553338, 'val_loss': 8.532901, 'test_loss': 9.080181}}
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2024-10-14 21:53:23,263 (client:354) INFO: {'Role': 'Client #6', 'Round': 56, 'Results_raw': {'train_loss': 14.847559, 'val_loss': 14.819071, 'test_loss': 16.2852}}
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2024-10-14 21:53:23,267 (server:615) INFO: {'Role': 'Server #', 'Round': 55, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.197719), 'test_loss': np.float64(99520.97388), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.194399), 'val_loss': np.float64(99503.764523)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.197719), 'test_loss': np.float64(99520.97388), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.194399), 'val_loss': np.float64(99503.764523)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.208876), 'test_avg_loss_bottom_decile': np.float64(16.517616), 'test_avg_loss_top_decile': np.float64(23.281576), 'test_avg_loss_min': np.float64(15.89796), 'test_avg_loss_max': np.float64(23.281576), 'test_avg_loss_bottom10%': np.float64(15.89796), 'test_avg_loss_top10%': np.float64(23.281576), 'test_avg_loss_cos1': np.float64(0.993446), 'test_avg_loss_entropy': np.float64(2.295995), 'test_loss_std': np.float64(11450.810623), 'test_loss_bottom_decile': np.float64(85627.321838), 'test_loss_top_decile': np.float64(120691.691223), 'test_loss_min': np.float64(82415.025146), 'test_loss_max': np.float64(120691.691223), 'test_loss_bottom10%': np.float64(82415.025146), 'test_loss_top10%': np.float64(120691.691223), 'test_loss_cos1': np.float64(0.993446), 'test_loss_entropy': np.float64(2.295995), 'val_avg_loss_std': np.float64(2.421005), 'val_avg_loss_bottom_decile': np.float64(16.221831), 'val_avg_loss_top_decile': np.float64(23.004384), 'val_avg_loss_min': np.float64(15.419789), 'val_avg_loss_max': np.float64(23.004384), 'val_avg_loss_bottom10%': np.float64(15.419789), 'val_avg_loss_top10%': np.float64(23.004384), 'val_avg_loss_cos1': np.float64(0.992139), 'val_avg_loss_entropy': np.float64(2.294595), 'val_loss_std': np.float64(12550.487616), 'val_loss_bottom_decile': np.float64(84093.973297), 'val_loss_top_decile': np.float64(119254.724792), 'val_loss_min': np.float64(79936.183899), 'val_loss_max': np.float64(119254.724792), 'val_loss_bottom10%': np.float64(79936.183899), 'val_loss_top10%': np.float64(119254.724792), 'val_loss_cos1': np.float64(0.992139), 'val_loss_entropy': np.float64(2.294595)}}
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2024-10-14 21:53:23,311 (server:353) INFO: Server: Starting evaluation at the end of round 56.
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2024-10-14 21:53:23,311 (server:359) INFO: ----------- Starting a new training round (Round #57) -------------
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2024-10-14 21:55:54,772 (client:354) INFO: {'Role': 'Client #2', 'Round': 57, 'Results_raw': {'train_loss': 8.642256, 'val_loss': 8.4986, 'test_loss': 9.08648}}
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2024-10-14 21:56:55,933 (client:354) INFO: {'Role': 'Client #10', 'Round': 57, 'Results_raw': {'train_loss': 14.758561, 'val_loss': 15.245126, 'test_loss': 16.670405}}
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2024-10-14 21:57:56,290 (client:354) INFO: {'Role': 'Client #5', 'Round': 57, 'Results_raw': {'train_loss': 15.669392, 'val_loss': 16.599379, 'test_loss': 18.681395}}
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2024-10-14 21:58:55,873 (client:354) INFO: {'Role': 'Client #7', 'Round': 57, 'Results_raw': {'train_loss': 15.094129, 'val_loss': 15.471816, 'test_loss': 16.157297}}
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2024-10-14 21:59:53,092 (client:354) INFO: {'Role': 'Client #4', 'Round': 57, 'Results_raw': {'train_loss': 14.854029, 'val_loss': 15.101813, 'test_loss': 16.281598}}
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2024-10-14 22:00:53,886 (client:354) INFO: {'Role': 'Client #6', 'Round': 57, 'Results_raw': {'train_loss': 14.832007, 'val_loss': 14.837972, 'test_loss': 16.347555}}
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2024-10-14 22:01:54,579 (client:354) INFO: {'Role': 'Client #8', 'Round': 57, 'Results_raw': {'train_loss': 13.071986, 'val_loss': 13.196559, 'test_loss': 13.92265}}
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2024-10-14 22:02:55,526 (client:354) INFO: {'Role': 'Client #1', 'Round': 57, 'Results_raw': {'train_loss': 10.558279, 'val_loss': 10.396194, 'test_loss': 11.434379}}
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2024-10-14 22:03:55,496 (client:354) INFO: {'Role': 'Client #9', 'Round': 57, 'Results_raw': {'train_loss': 17.547761, 'val_loss': 17.561778, 'test_loss': 18.49001}}
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2024-10-14 22:04:55,462 (client:354) INFO: {'Role': 'Client #3', 'Round': 57, 'Results_raw': {'train_loss': 9.913392, 'val_loss': 10.577444, 'test_loss': 11.985949}}
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2024-10-14 22:04:55,467 (server:615) INFO: {'Role': 'Server #', 'Round': 56, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.179074), 'test_loss': np.float64(99424.321115), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.2134), 'val_loss': np.float64(99602.267593)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.179074), 'test_loss': np.float64(99424.321115), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.2134), 'val_loss': np.float64(99602.267593)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.27109), 'test_avg_loss_bottom_decile': np.float64(16.393377), 'test_avg_loss_top_decile': np.float64(23.388232), 'test_avg_loss_min': np.float64(15.854279), 'test_avg_loss_max': np.float64(23.388232), 'test_avg_loss_bottom10%': np.float64(15.854279), 'test_avg_loss_top10%': np.float64(23.388232), 'test_avg_loss_cos1': np.float64(0.993062), 'test_avg_loss_entropy': np.float64(2.295607), 'test_loss_std': np.float64(11773.330591), 'test_loss_bottom_decile': np.float64(84983.267853), 'test_loss_top_decile': np.float64(121244.595886), 'test_loss_min': np.float64(82188.579773), 'test_loss_max': np.float64(121244.595886), 'test_loss_bottom10%': np.float64(82188.579773), 'test_loss_top10%': np.float64(121244.595886), 'test_loss_cos1': np.float64(0.993062), 'test_loss_entropy': np.float64(2.295607), 'val_avg_loss_std': np.float64(2.490031), 'val_avg_loss_bottom_decile': np.float64(16.127529), 'val_avg_loss_top_decile': np.float64(23.136212), 'val_avg_loss_min': np.float64(15.389607), 'val_avg_loss_max': np.float64(23.136212), 'val_avg_loss_bottom10%': np.float64(15.389607), 'val_avg_loss_top10%': np.float64(23.136212), 'val_avg_loss_cos1': np.float64(0.991706), 'val_avg_loss_entropy': np.float64(2.294147), 'val_loss_std': np.float64(12908.319317), 'val_loss_bottom_decile': np.float64(83605.107971), 'val_loss_top_decile': np.float64(119938.121765), 'val_loss_min': np.float64(79779.725067), 'val_loss_max': np.float64(119938.121765), 'val_loss_bottom10%': np.float64(79779.725067), 'val_loss_top10%': np.float64(119938.121765), 'val_loss_cos1': np.float64(0.991706), 'val_loss_entropy': np.float64(2.294147)}}
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2024-10-14 22:04:55,508 (server:353) INFO: Server: Starting evaluation at the end of round 57.
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2024-10-14 22:04:55,508 (server:359) INFO: ----------- Starting a new training round (Round #58) -------------
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2024-10-14 22:07:32,630 (client:354) INFO: {'Role': 'Client #6', 'Round': 58, 'Results_raw': {'train_loss': 14.829892, 'val_loss': 14.911489, 'test_loss': 16.568181}}
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2024-10-14 22:08:31,749 (client:354) INFO: {'Role': 'Client #8', 'Round': 58, 'Results_raw': {'train_loss': 13.05447, 'val_loss': 13.181492, 'test_loss': 13.826232}}
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2024-10-14 22:09:28,526 (client:354) INFO: {'Role': 'Client #10', 'Round': 58, 'Results_raw': {'train_loss': 14.768803, 'val_loss': 15.210966, 'test_loss': 16.958606}}
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2024-10-14 22:10:29,548 (client:354) INFO: {'Role': 'Client #9', 'Round': 58, 'Results_raw': {'train_loss': 17.542666, 'val_loss': 17.661835, 'test_loss': 18.561635}}
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2024-10-14 22:11:30,141 (client:354) INFO: {'Role': 'Client #4', 'Round': 58, 'Results_raw': {'train_loss': 14.837009, 'val_loss': 15.05156, 'test_loss': 16.035495}}
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2024-10-14 22:12:31,065 (client:354) INFO: {'Role': 'Client #2', 'Round': 58, 'Results_raw': {'train_loss': 8.611473, 'val_loss': 8.422013, 'test_loss': 9.008274}}
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2024-10-14 22:13:28,848 (client:354) INFO: {'Role': 'Client #3', 'Round': 58, 'Results_raw': {'train_loss': 9.903505, 'val_loss': 10.769087, 'test_loss': 12.208523}}
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2024-10-14 22:14:29,499 (client:354) INFO: {'Role': 'Client #5', 'Round': 58, 'Results_raw': {'train_loss': 15.660138, 'val_loss': 16.656005, 'test_loss': 18.819454}}
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2024-10-14 22:15:27,750 (client:354) INFO: {'Role': 'Client #7', 'Round': 58, 'Results_raw': {'train_loss': 15.060933, 'val_loss': 15.441633, 'test_loss': 16.155697}}
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2024-10-14 22:16:29,278 (client:354) INFO: {'Role': 'Client #1', 'Round': 58, 'Results_raw': {'train_loss': 10.481996, 'val_loss': 10.411091, 'test_loss': 11.439083}}
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2024-10-14 22:16:29,282 (server:615) INFO: {'Role': 'Server #', 'Round': 57, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.133822), 'test_loss': np.float64(99189.734457), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.161832), 'val_loss': np.float64(99334.93945)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.133822), 'test_loss': np.float64(99189.734457), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.161832), 'val_loss': np.float64(99334.93945)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.301853), 'test_avg_loss_bottom_decile': np.float64(16.29834), 'test_avg_loss_top_decile': np.float64(23.371874), 'test_avg_loss_min': np.float64(15.733175), 'test_avg_loss_max': np.float64(23.371874), 'test_avg_loss_bottom10%': np.float64(15.733175), 'test_avg_loss_top10%': np.float64(23.371874), 'test_avg_loss_cos1': np.float64(0.992841), 'test_avg_loss_entropy': np.float64(2.295376), 'test_loss_std': np.float64(11932.803599), 'test_loss_bottom_decile': np.float64(84490.597107), 'test_loss_top_decile': np.float64(121159.795898), 'test_loss_min': np.float64(81560.777985), 'test_loss_max': np.float64(121159.795898), 'test_loss_bottom10%': np.float64(81560.777985), 'test_loss_top10%': np.float64(121159.795898), 'test_loss_cos1': np.float64(0.992841), 'test_loss_entropy': np.float64(2.295376), 'val_avg_loss_std': np.float64(2.530073), 'val_avg_loss_bottom_decile': np.float64(16.002971), 'val_avg_loss_top_decile': np.float64(23.114869), 'val_avg_loss_min': np.float64(15.259041), 'val_avg_loss_max': np.float64(23.114869), 'val_avg_loss_bottom10%': np.float64(15.259041), 'val_avg_loss_top10%': np.float64(23.114869), 'val_avg_loss_cos1': np.float64(0.991395), 'val_avg_loss_entropy': np.float64(2.293819), 'val_loss_std': np.float64(13115.896196), 'val_loss_bottom_decile': np.float64(82959.400177), 'val_loss_top_decile': np.float64(119827.482117), 'val_loss_min': np.float64(79102.867737), 'val_loss_max': np.float64(119827.482117), 'val_loss_bottom10%': np.float64(79102.867737), 'val_loss_top10%': np.float64(119827.482117), 'val_loss_cos1': np.float64(0.991395), 'val_loss_entropy': np.float64(2.293819)}}
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2024-10-14 22:16:29,322 (server:353) INFO: Server: Starting evaluation at the end of round 58.
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2024-10-14 22:16:29,322 (server:359) INFO: ----------- Starting a new training round (Round #59) -------------
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2024-10-14 22:19:02,259 (client:354) INFO: {'Role': 'Client #1', 'Round': 59, 'Results_raw': {'train_loss': 10.464982, 'val_loss': 10.345803, 'test_loss': 11.514412}}
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2024-10-14 22:20:00,734 (client:354) INFO: {'Role': 'Client #10', 'Round': 59, 'Results_raw': {'train_loss': 14.718004, 'val_loss': 15.331012, 'test_loss': 16.947984}}
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2024-10-14 22:21:01,558 (client:354) INFO: {'Role': 'Client #5', 'Round': 59, 'Results_raw': {'train_loss': 15.657947, 'val_loss': 16.72644, 'test_loss': 18.921171}}
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2024-10-14 22:22:00,836 (client:354) INFO: {'Role': 'Client #3', 'Round': 59, 'Results_raw': {'train_loss': 9.906707, 'val_loss': 10.54695, 'test_loss': 12.025182}}
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2024-10-14 22:22:58,871 (client:354) INFO: {'Role': 'Client #8', 'Round': 59, 'Results_raw': {'train_loss': 13.031968, 'val_loss': 13.203048, 'test_loss': 14.091525}}
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2024-10-14 22:24:02,062 (client:354) INFO: {'Role': 'Client #2', 'Round': 59, 'Results_raw': {'train_loss': 8.630273, 'val_loss': 8.559597, 'test_loss': 9.156531}}
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2024-10-14 22:25:06,302 (client:354) INFO: {'Role': 'Client #6', 'Round': 59, 'Results_raw': {'train_loss': 14.820853, 'val_loss': 14.887676, 'test_loss': 16.449847}}
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2024-10-14 22:26:05,251 (client:354) INFO: {'Role': 'Client #7', 'Round': 59, 'Results_raw': {'train_loss': 15.072776, 'val_loss': 15.58553, 'test_loss': 16.244213}}
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2024-10-14 22:27:04,096 (client:354) INFO: {'Role': 'Client #9', 'Round': 59, 'Results_raw': {'train_loss': 17.50088, 'val_loss': 17.700579, 'test_loss': 18.418143}}
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2024-10-14 22:28:03,883 (client:354) INFO: {'Role': 'Client #4', 'Round': 59, 'Results_raw': {'train_loss': 14.820709, 'val_loss': 15.075985, 'test_loss': 16.182104}}
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2024-10-14 22:28:03,886 (server:615) INFO: {'Role': 'Server #', 'Round': 58, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.171143), 'test_loss': np.float64(99383.203311), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.181191), 'val_loss': np.float64(99435.292065)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.171143), 'test_loss': np.float64(99383.203311), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.181191), 'val_loss': np.float64(99435.292065)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.324769), 'test_avg_loss_bottom_decile': np.float64(16.201369), 'test_avg_loss_top_decile': np.float64(23.456034), 'test_avg_loss_min': np.float64(15.802192), 'test_avg_loss_max': np.float64(23.456034), 'test_avg_loss_bottom10%': np.float64(15.802192), 'test_avg_loss_top10%': np.float64(23.456034), 'test_avg_loss_cos1': np.float64(0.992728), 'test_avg_loss_entropy': np.float64(2.295259), 'test_loss_std': np.float64(12051.600653), 'test_loss_bottom_decile': np.float64(83987.898743), 'test_loss_top_decile': np.float64(121596.082397), 'test_loss_min': np.float64(81918.565582), 'test_loss_max': np.float64(121596.082397), 'test_loss_bottom10%': np.float64(81918.565582), 'test_loss_top10%': np.float64(121596.082397), 'test_loss_cos1': np.float64(0.992728), 'test_loss_entropy': np.float64(2.295259), 'val_avg_loss_std': np.float64(2.549074), 'val_avg_loss_bottom_decile': np.float64(15.900865), 'val_avg_loss_top_decile': np.float64(23.199098), 'val_avg_loss_min': np.float64(15.315428), 'val_avg_loss_max': np.float64(23.199098), 'val_avg_loss_bottom10%': np.float64(15.315428), 'val_avg_loss_top10%': np.float64(23.199098), 'val_avg_loss_cos1': np.float64(0.991285), 'val_avg_loss_entropy': np.float64(2.293703), 'val_loss_std': np.float64(13214.400462), 'val_loss_bottom_decile': np.float64(82430.082703), 'val_loss_top_decile': np.float64(120264.125488), 'val_loss_min': np.float64(79395.17746), 'val_loss_max': np.float64(120264.125488), 'val_loss_bottom10%': np.float64(79395.17746), 'val_loss_top10%': np.float64(120264.125488), 'val_loss_cos1': np.float64(0.991285), 'val_loss_entropy': np.float64(2.293703)}}
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2024-10-14 22:28:03,925 (server:353) INFO: Server: Starting evaluation at the end of round 59.
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2024-10-14 22:28:03,926 (server:359) INFO: ----------- Starting a new training round (Round #60) -------------
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2024-10-14 22:30:39,094 (client:354) INFO: {'Role': 'Client #4', 'Round': 60, 'Results_raw': {'train_loss': 14.846347, 'val_loss': 14.961397, 'test_loss': 15.988743}}
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2024-10-14 22:31:34,136 (client:354) INFO: {'Role': 'Client #10', 'Round': 60, 'Results_raw': {'train_loss': 14.704979, 'val_loss': 15.350204, 'test_loss': 16.991962}}
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2024-10-14 22:32:32,801 (client:354) INFO: {'Role': 'Client #1', 'Round': 60, 'Results_raw': {'train_loss': 10.489843, 'val_loss': 10.363749, 'test_loss': 11.528708}}
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2024-10-14 22:33:31,564 (client:354) INFO: {'Role': 'Client #9', 'Round': 60, 'Results_raw': {'train_loss': 17.626347, 'val_loss': 17.716772, 'test_loss': 18.44163}}
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2024-10-14 22:34:26,949 (client:354) INFO: {'Role': 'Client #5', 'Round': 60, 'Results_raw': {'train_loss': 15.68173, 'val_loss': 16.536086, 'test_loss': 18.837502}}
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2024-10-14 22:35:26,772 (client:354) INFO: {'Role': 'Client #3', 'Round': 60, 'Results_raw': {'train_loss': 9.894033, 'val_loss': 10.615717, 'test_loss': 11.995935}}
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2024-10-14 22:36:27,563 (client:354) INFO: {'Role': 'Client #7', 'Round': 60, 'Results_raw': {'train_loss': 15.045266, 'val_loss': 15.327373, 'test_loss': 16.151106}}
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2024-10-14 22:37:28,673 (client:354) INFO: {'Role': 'Client #2', 'Round': 60, 'Results_raw': {'train_loss': 8.602929, 'val_loss': 8.542189, 'test_loss': 9.267934}}
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2024-10-14 22:38:28,926 (client:354) INFO: {'Role': 'Client #6', 'Round': 60, 'Results_raw': {'train_loss': 14.836278, 'val_loss': 14.881167, 'test_loss': 16.310026}}
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2024-10-14 22:39:26,387 (client:354) INFO: {'Role': 'Client #8', 'Round': 60, 'Results_raw': {'train_loss': 13.047482, 'val_loss': 13.179569, 'test_loss': 14.02388}}
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2024-10-14 22:39:26,391 (server:615) INFO: {'Role': 'Server #', 'Round': 59, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.107899), 'test_loss': np.float64(99055.346283), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.098036), 'val_loss': np.float64(99004.216525)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.107899), 'test_loss': np.float64(99055.346283), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.098036), 'val_loss': np.float64(99004.216525)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.231717), 'test_avg_loss_bottom_decile': np.float64(16.284832), 'test_avg_loss_top_decile': np.float64(23.188386), 'test_avg_loss_min': np.float64(15.810879), 'test_avg_loss_max': np.float64(23.188386), 'test_avg_loss_bottom10%': np.float64(15.810879), 'test_avg_loss_top10%': np.float64(23.188386), 'test_avg_loss_cos1': np.float64(0.993248), 'test_avg_loss_entropy': np.float64(2.295786), 'test_loss_std': np.float64(11569.218801), 'test_loss_bottom_decile': np.float64(84420.57132), 'test_loss_top_decile': np.float64(120208.590637), 'test_loss_min': np.float64(81963.597412), 'test_loss_max': np.float64(120208.590637), 'test_loss_bottom10%': np.float64(81963.597412), 'test_loss_top10%': np.float64(120208.590637), 'test_loss_cos1': np.float64(0.993248), 'test_loss_entropy': np.float64(2.295786), 'val_avg_loss_std': np.float64(2.448752), 'val_avg_loss_bottom_decile': np.float64(15.966322), 'val_avg_loss_top_decile': np.float64(22.942435), 'val_avg_loss_min': np.float64(15.324373), 'val_avg_loss_max': np.float64(22.942435), 'val_avg_loss_bottom10%': np.float64(15.324373), 'val_avg_loss_top10%': np.float64(22.942435), 'val_avg_loss_cos1': np.float64(0.99188), 'val_avg_loss_entropy': np.float64(2.29432), 'val_loss_std': np.float64(12694.332901), 'val_loss_bottom_decile': np.float64(82769.41272), 'val_loss_top_decile': np.float64(118933.582825), 'val_loss_min': np.float64(79441.547913), 'val_loss_max': np.float64(118933.582825), 'val_loss_bottom10%': np.float64(79441.547913), 'val_loss_top10%': np.float64(118933.582825), 'val_loss_cos1': np.float64(0.99188), 'val_loss_entropy': np.float64(2.29432)}}
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2024-10-14 22:39:26,433 (server:353) INFO: Server: Starting evaluation at the end of round 60.
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2024-10-14 22:39:26,434 (server:359) INFO: ----------- Starting a new training round (Round #61) -------------
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2024-10-14 22:42:02,487 (client:354) INFO: {'Role': 'Client #6', 'Round': 61, 'Results_raw': {'train_loss': 14.791277, 'val_loss': 14.810272, 'test_loss': 16.222685}}
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2024-10-14 22:43:00,462 (client:354) INFO: {'Role': 'Client #2', 'Round': 61, 'Results_raw': {'train_loss': 8.531774, 'val_loss': 8.545745, 'test_loss': 9.072469}}
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2024-10-14 22:43:59,241 (client:354) INFO: {'Role': 'Client #8', 'Round': 61, 'Results_raw': {'train_loss': 13.041513, 'val_loss': 13.117636, 'test_loss': 13.802978}}
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2024-10-14 22:45:00,939 (client:354) INFO: {'Role': 'Client #4', 'Round': 61, 'Results_raw': {'train_loss': 14.823834, 'val_loss': 15.102756, 'test_loss': 16.241976}}
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2024-10-14 22:46:00,505 (client:354) INFO: {'Role': 'Client #5', 'Round': 61, 'Results_raw': {'train_loss': 15.629491, 'val_loss': 16.661415, 'test_loss': 18.865286}}
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2024-10-14 22:47:00,373 (client:354) INFO: {'Role': 'Client #7', 'Round': 61, 'Results_raw': {'train_loss': 15.055006, 'val_loss': 15.355773, 'test_loss': 16.21755}}
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2024-10-14 22:47:59,633 (client:354) INFO: {'Role': 'Client #9', 'Round': 61, 'Results_raw': {'train_loss': 17.55225, 'val_loss': 17.695366, 'test_loss': 18.498303}}
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2024-10-14 22:49:02,825 (client:354) INFO: {'Role': 'Client #10', 'Round': 61, 'Results_raw': {'train_loss': 14.710555, 'val_loss': 15.334828, 'test_loss': 16.940873}}
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2024-10-14 22:50:03,273 (client:354) INFO: {'Role': 'Client #1', 'Round': 61, 'Results_raw': {'train_loss': 10.454369, 'val_loss': 10.417338, 'test_loss': 11.574658}}
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2024-10-14 22:51:03,633 (client:354) INFO: {'Role': 'Client #3', 'Round': 61, 'Results_raw': {'train_loss': 9.867984, 'val_loss': 10.588351, 'test_loss': 11.99143}}
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2024-10-14 22:51:03,637 (server:615) INFO: {'Role': 'Server #', 'Round': 60, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.247699), 'test_loss': np.float64(99780.069083), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.246134), 'val_loss': np.float64(99771.960944)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.247699), 'test_loss': np.float64(99780.069083), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.246134), 'val_loss': np.float64(99771.960944)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.231911), 'test_avg_loss_bottom_decile': np.float64(16.491442), 'test_avg_loss_top_decile': np.float64(23.377865), 'test_avg_loss_min': np.float64(15.926406), 'test_avg_loss_max': np.float64(23.377865), 'test_avg_loss_bottom10%': np.float64(15.926406), 'test_avg_loss_top10%': np.float64(23.377865), 'test_avg_loss_cos1': np.float64(0.993344), 'test_avg_loss_entropy': np.float64(2.29589), 'test_loss_std': np.float64(11570.229195), 'test_loss_bottom_decile': np.float64(85491.636932), 'test_loss_top_decile': np.float64(121190.850159), 'test_loss_min': np.float64(82562.48877), 'test_loss_max': np.float64(121190.850159), 'test_loss_bottom10%': np.float64(82562.48877), 'test_loss_top10%': np.float64(121190.850159), 'test_loss_cos1': np.float64(0.993344), 'test_loss_entropy': np.float64(2.29589), 'val_avg_loss_std': np.float64(2.45225), 'val_avg_loss_bottom_decile': np.float64(16.186323), 'val_avg_loss_top_decile': np.float64(23.13345), 'val_avg_loss_min': np.float64(15.446269), 'val_avg_loss_max': np.float64(23.13345), 'val_avg_loss_bottom10%': np.float64(15.446269), 'val_avg_loss_top10%': np.float64(23.13345), 'val_avg_loss_cos1': np.float64(0.99198), 'val_avg_loss_entropy': np.float64(2.294433), 'val_loss_std': np.float64(12712.462976), 'val_loss_bottom_decile': np.float64(83909.899933), 'val_loss_top_decile': np.float64(119923.807007), 'val_loss_min': np.float64(80073.458435), 'val_loss_max': np.float64(119923.807007), 'val_loss_bottom10%': np.float64(80073.458435), 'val_loss_top10%': np.float64(119923.807007), 'val_loss_cos1': np.float64(0.99198), 'val_loss_entropy': np.float64(2.294433)}}
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2024-10-14 22:51:03,675 (server:353) INFO: Server: Starting evaluation at the end of round 61.
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2024-10-14 22:51:03,676 (server:359) INFO: ----------- Starting a new training round (Round #62) -------------
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2024-10-14 22:53:35,929 (client:354) INFO: {'Role': 'Client #4', 'Round': 62, 'Results_raw': {'train_loss': 14.81598, 'val_loss': 15.053875, 'test_loss': 16.072207}}
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2024-10-14 22:54:36,082 (client:354) INFO: {'Role': 'Client #8', 'Round': 62, 'Results_raw': {'train_loss': 13.033834, 'val_loss': 13.177655, 'test_loss': 13.829756}}
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2024-10-14 22:55:34,986 (client:354) INFO: {'Role': 'Client #3', 'Round': 62, 'Results_raw': {'train_loss': 9.913972, 'val_loss': 10.585957, 'test_loss': 12.071486}}
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2024-10-14 22:56:31,909 (client:354) INFO: {'Role': 'Client #10', 'Round': 62, 'Results_raw': {'train_loss': 14.716828, 'val_loss': 15.311941, 'test_loss': 17.063726}}
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2024-10-14 22:57:35,679 (client:354) INFO: {'Role': 'Client #2', 'Round': 62, 'Results_raw': {'train_loss': 8.543927, 'val_loss': 8.510033, 'test_loss': 9.098034}}
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2024-10-14 22:58:34,834 (client:354) INFO: {'Role': 'Client #6', 'Round': 62, 'Results_raw': {'train_loss': 14.782557, 'val_loss': 14.761248, 'test_loss': 16.206143}}
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2024-10-14 22:59:32,822 (client:354) INFO: {'Role': 'Client #9', 'Round': 62, 'Results_raw': {'train_loss': 17.496031, 'val_loss': 17.495412, 'test_loss': 18.31363}}
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2024-10-14 23:00:31,867 (client:354) INFO: {'Role': 'Client #5', 'Round': 62, 'Results_raw': {'train_loss': 15.632519, 'val_loss': 16.550787, 'test_loss': 18.578447}}
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2024-10-14 23:01:32,512 (client:354) INFO: {'Role': 'Client #7', 'Round': 62, 'Results_raw': {'train_loss': 15.058826, 'val_loss': 15.335504, 'test_loss': 15.983917}}
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2024-10-14 23:02:33,460 (client:354) INFO: {'Role': 'Client #1', 'Round': 62, 'Results_raw': {'train_loss': 10.453305, 'val_loss': 10.42932, 'test_loss': 11.543336}}
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2024-10-14 23:02:33,464 (server:615) INFO: {'Role': 'Server #', 'Round': 61, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.120647), 'test_loss': np.float64(99121.436261), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.117617), 'val_loss': np.float64(99105.72905)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.120647), 'test_loss': np.float64(99121.436261), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.117617), 'val_loss': np.float64(99105.72905)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.288375), 'test_avg_loss_bottom_decile': np.float64(16.190512), 'test_avg_loss_top_decile': np.float64(23.327207), 'test_avg_loss_min': np.float64(15.803703), 'test_avg_loss_max': np.float64(23.327207), 'test_avg_loss_bottom10%': np.float64(15.803703), 'test_avg_loss_top10%': np.float64(23.327207), 'test_avg_loss_cos1': np.float64(0.992914), 'test_avg_loss_entropy': np.float64(2.29545), 'test_loss_std': np.float64(11862.934763), 'test_loss_bottom_decile': np.float64(83931.614716), 'test_loss_top_decile': np.float64(120928.239807), 'test_loss_min': np.float64(81926.396576), 'test_loss_max': np.float64(120928.239807), 'test_loss_bottom10%': np.float64(81926.396576), 'test_loss_top10%': np.float64(120928.239807), 'test_loss_cos1': np.float64(0.992914), 'test_loss_entropy': np.float64(2.29545), 'val_avg_loss_std': np.float64(2.509239), 'val_avg_loss_bottom_decile': np.float64(15.880639), 'val_avg_loss_top_decile': np.float64(23.094553), 'val_avg_loss_min': np.float64(15.313272), 'val_avg_loss_max': np.float64(23.094553), 'val_avg_loss_bottom10%': np.float64(15.313272), 'val_avg_loss_top10%': np.float64(23.094553), 'val_avg_loss_cos1': np.float64(0.991496), 'val_avg_loss_entropy': np.float64(2.293929), 'val_loss_std': np.float64(13007.893228), 'val_loss_bottom_decile': np.float64(82325.234833), 'val_loss_top_decile': np.float64(119722.162231), 'val_loss_min': np.float64(79384.001251), 'val_loss_max': np.float64(119722.162231), 'val_loss_bottom10%': np.float64(79384.001251), 'val_loss_top10%': np.float64(119722.162231), 'val_loss_cos1': np.float64(0.991496), 'val_loss_entropy': np.float64(2.293929)}}
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2024-10-14 23:02:33,510 (server:353) INFO: Server: Starting evaluation at the end of round 62.
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2024-10-14 23:02:33,510 (server:359) INFO: ----------- Starting a new training round (Round #63) -------------
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2024-10-14 23:05:10,475 (client:354) INFO: {'Role': 'Client #8', 'Round': 63, 'Results_raw': {'train_loss': 13.016318, 'val_loss': 13.168671, 'test_loss': 13.896648}}
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2024-10-14 23:06:15,940 (client:354) INFO: {'Role': 'Client #3', 'Round': 63, 'Results_raw': {'train_loss': 9.874711, 'val_loss': 10.48089, 'test_loss': 11.87056}}
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2024-10-14 23:07:14,712 (client:354) INFO: {'Role': 'Client #10', 'Round': 63, 'Results_raw': {'train_loss': 14.696278, 'val_loss': 15.207525, 'test_loss': 16.800797}}
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2024-10-14 23:08:09,297 (client:354) INFO: {'Role': 'Client #1', 'Round': 63, 'Results_raw': {'train_loss': 10.435261, 'val_loss': 10.562921, 'test_loss': 11.688577}}
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2024-10-14 23:09:08,998 (client:354) INFO: {'Role': 'Client #9', 'Round': 63, 'Results_raw': {'train_loss': 17.523254, 'val_loss': 17.799328, 'test_loss': 18.880374}}
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2024-10-14 23:10:07,314 (client:354) INFO: {'Role': 'Client #7', 'Round': 63, 'Results_raw': {'train_loss': 15.0342, 'val_loss': 15.585579, 'test_loss': 16.062786}}
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2024-10-14 23:11:07,306 (client:354) INFO: {'Role': 'Client #4', 'Round': 63, 'Results_raw': {'train_loss': 14.780621, 'val_loss': 15.102054, 'test_loss': 16.284839}}
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2024-10-14 23:12:05,171 (client:354) INFO: {'Role': 'Client #5', 'Round': 63, 'Results_raw': {'train_loss': 15.62676, 'val_loss': 16.544599, 'test_loss': 18.61254}}
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2024-10-14 23:12:58,641 (client:354) INFO: {'Role': 'Client #2', 'Round': 63, 'Results_raw': {'train_loss': 8.529351, 'val_loss': 8.630052, 'test_loss': 9.181645}}
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2024-10-14 23:14:00,126 (client:354) INFO: {'Role': 'Client #6', 'Round': 63, 'Results_raw': {'train_loss': 14.759947, 'val_loss': 14.809722, 'test_loss': 16.243983}}
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2024-10-14 23:14:00,130 (server:615) INFO: {'Role': 'Server #', 'Round': 62, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.077303), 'test_loss': np.float64(98896.737628), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.090775), 'val_loss': np.float64(98966.577008)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.077303), 'test_loss': np.float64(98896.737628), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.090775), 'val_loss': np.float64(98966.577008)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.302257), 'test_avg_loss_bottom_decile': np.float64(16.133411), 'test_avg_loss_top_decile': np.float64(23.310408), 'test_avg_loss_min': np.float64(15.735913), 'test_avg_loss_max': np.float64(23.310408), 'test_avg_loss_bottom10%': np.float64(15.735913), 'test_avg_loss_top10%': np.float64(23.310408), 'test_avg_loss_cos1': np.float64(0.992797), 'test_avg_loss_entropy': np.float64(2.29533), 'test_loss_std': np.float64(11934.902041), 'test_loss_bottom_decile': np.float64(83635.60437), 'test_loss_top_decile': np.float64(120841.157288), 'test_loss_min': np.float64(81574.972046), 'test_loss_max': np.float64(120841.157288), 'test_loss_bottom10%': np.float64(81574.972046), 'test_loss_top10%': np.float64(120841.157288), 'test_loss_cos1': np.float64(0.992797), 'test_loss_entropy': np.float64(2.29533), 'val_avg_loss_std': np.float64(2.529227), 'val_avg_loss_bottom_decile': np.float64(15.850501), 'val_avg_loss_top_decile': np.float64(23.097163), 'val_avg_loss_min': np.float64(15.250806), 'val_avg_loss_max': np.float64(23.097163), 'val_avg_loss_bottom10%': np.float64(15.250806), 'val_avg_loss_top10%': np.float64(23.097163), 'val_avg_loss_cos1': np.float64(0.991338), 'val_avg_loss_entropy': np.float64(2.293765), 'val_loss_std': np.float64(13111.513275), 'val_loss_bottom_decile': np.float64(82168.995209), 'val_loss_top_decile': np.float64(119735.691345), 'val_loss_min': np.float64(79060.180176), 'val_loss_max': np.float64(119735.691345), 'val_loss_bottom10%': np.float64(79060.180176), 'val_loss_top10%': np.float64(119735.691345), 'val_loss_cos1': np.float64(0.991338), 'val_loss_entropy': np.float64(2.293765)}}
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2024-10-14 23:14:00,178 (server:353) INFO: Server: Starting evaluation at the end of round 63.
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2024-10-14 23:14:00,178 (server:359) INFO: ----------- Starting a new training round (Round #64) -------------
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2024-10-14 23:16:33,107 (client:354) INFO: {'Role': 'Client #10', 'Round': 64, 'Results_raw': {'train_loss': 14.676984, 'val_loss': 15.249732, 'test_loss': 16.907197}}
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2024-10-14 23:17:36,731 (client:354) INFO: {'Role': 'Client #9', 'Round': 64, 'Results_raw': {'train_loss': 17.484518, 'val_loss': 17.56246, 'test_loss': 18.466619}}
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2024-10-14 23:18:34,015 (client:354) INFO: {'Role': 'Client #1', 'Round': 64, 'Results_raw': {'train_loss': 10.421695, 'val_loss': 10.356151, 'test_loss': 11.525521}}
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2024-10-14 23:19:33,941 (client:354) INFO: {'Role': 'Client #5', 'Round': 64, 'Results_raw': {'train_loss': 15.628971, 'val_loss': 16.686911, 'test_loss': 18.83184}}
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2024-10-14 23:20:29,091 (client:354) INFO: {'Role': 'Client #2', 'Round': 64, 'Results_raw': {'train_loss': 8.521767, 'val_loss': 8.386776, 'test_loss': 8.963437}}
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2024-10-14 23:21:25,178 (client:354) INFO: {'Role': 'Client #7', 'Round': 64, 'Results_raw': {'train_loss': 15.038208, 'val_loss': 15.455826, 'test_loss': 16.146375}}
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2024-10-14 23:22:22,746 (client:354) INFO: {'Role': 'Client #6', 'Round': 64, 'Results_raw': {'train_loss': 14.767803, 'val_loss': 14.820418, 'test_loss': 16.369739}}
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2024-10-14 23:23:20,911 (client:354) INFO: {'Role': 'Client #8', 'Round': 64, 'Results_raw': {'train_loss': 13.030415, 'val_loss': 13.187219, 'test_loss': 13.91254}}
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2024-10-14 23:24:19,866 (client:354) INFO: {'Role': 'Client #3', 'Round': 64, 'Results_raw': {'train_loss': 9.901155, 'val_loss': 10.657954, 'test_loss': 12.123357}}
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2024-10-14 23:25:17,849 (client:354) INFO: {'Role': 'Client #4', 'Round': 64, 'Results_raw': {'train_loss': 14.78779, 'val_loss': 15.015641, 'test_loss': 16.057608}}
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2024-10-14 23:25:17,853 (server:615) INFO: {'Role': 'Server #', 'Round': 63, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.06243), 'test_loss': np.float64(98819.638843), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.087394), 'val_loss': np.float64(98949.050253)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.06243), 'test_loss': np.float64(98819.638843), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.087394), 'val_loss': np.float64(98949.050253)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.319874), 'test_avg_loss_bottom_decile': np.float64(15.987346), 'test_avg_loss_top_decile': np.float64(23.276624), 'test_avg_loss_min': np.float64(15.763494), 'test_avg_loss_max': np.float64(23.276624), 'test_avg_loss_bottom10%': np.float64(15.763494), 'test_avg_loss_top10%': np.float64(23.276624), 'test_avg_loss_cos1': np.float64(0.992676), 'test_avg_loss_entropy': np.float64(2.295202), 'test_loss_std': np.float64(12026.227845), 'test_loss_bottom_decile': np.float64(82878.403931), 'test_loss_top_decile': np.float64(120666.020203), 'test_loss_min': np.float64(81717.9534), 'test_loss_max': np.float64(120666.020203), 'test_loss_bottom10%': np.float64(81717.9534), 'test_loss_top10%': np.float64(120666.020203), 'test_loss_cos1': np.float64(0.992676), 'test_loss_entropy': np.float64(2.295202), 'val_avg_loss_std': np.float64(2.554428), 'val_avg_loss_bottom_decile': np.float64(15.702959), 'val_avg_loss_top_decile': np.float64(23.096062), 'val_avg_loss_min': np.float64(15.287263), 'val_avg_loss_max': np.float64(23.096062), 'val_avg_loss_bottom10%': np.float64(15.287263), 'val_avg_loss_top10%': np.float64(23.096062), 'val_avg_loss_cos1': np.float64(0.991164), 'val_avg_loss_entropy': np.float64(2.293583), 'val_loss_std': np.float64(13242.155205), 'val_loss_bottom_decile': np.float64(81404.141846), 'val_loss_top_decile': np.float64(119729.984802), 'val_loss_min': np.float64(79249.173218), 'val_loss_max': np.float64(119729.984802), 'val_loss_bottom10%': np.float64(79249.173218), 'val_loss_top10%': np.float64(119729.984802), 'val_loss_cos1': np.float64(0.991164), 'val_loss_entropy': np.float64(2.293583)}}
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2024-10-14 23:25:17,889 (server:353) INFO: Server: Starting evaluation at the end of round 64.
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2024-10-14 23:25:17,890 (server:359) INFO: ----------- Starting a new training round (Round #65) -------------
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2024-10-14 23:28:00,441 (client:354) INFO: {'Role': 'Client #2', 'Round': 65, 'Results_raw': {'train_loss': 8.547701, 'val_loss': 8.480211, 'test_loss': 9.025295}}
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2024-10-14 23:28:59,301 (client:354) INFO: {'Role': 'Client #5', 'Round': 65, 'Results_raw': {'train_loss': 15.572612, 'val_loss': 16.574903, 'test_loss': 18.733051}}
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2024-10-14 23:29:58,079 (client:354) INFO: {'Role': 'Client #10', 'Round': 65, 'Results_raw': {'train_loss': 14.678472, 'val_loss': 15.187869, 'test_loss': 16.864623}}
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2024-10-14 23:30:58,631 (client:354) INFO: {'Role': 'Client #1', 'Round': 65, 'Results_raw': {'train_loss': 10.446962, 'val_loss': 10.445121, 'test_loss': 11.546306}}
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2024-10-14 23:31:58,738 (client:354) INFO: {'Role': 'Client #7', 'Round': 65, 'Results_raw': {'train_loss': 15.0385, 'val_loss': 15.403077, 'test_loss': 16.136306}}
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2024-10-14 23:32:57,726 (client:354) INFO: {'Role': 'Client #3', 'Round': 65, 'Results_raw': {'train_loss': 9.894281, 'val_loss': 10.548364, 'test_loss': 12.013248}}
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2024-10-14 23:33:57,272 (client:354) INFO: {'Role': 'Client #9', 'Round': 65, 'Results_raw': {'train_loss': 17.479427, 'val_loss': 17.92601, 'test_loss': 18.868675}}
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2024-10-14 23:34:55,786 (client:354) INFO: {'Role': 'Client #8', 'Round': 65, 'Results_raw': {'train_loss': 13.012501, 'val_loss': 13.216788, 'test_loss': 13.873637}}
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2024-10-14 23:35:54,603 (client:354) INFO: {'Role': 'Client #4', 'Round': 65, 'Results_raw': {'train_loss': 14.763409, 'val_loss': 15.113463, 'test_loss': 16.102077}}
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2024-10-14 23:36:55,356 (client:354) INFO: {'Role': 'Client #6', 'Round': 65, 'Results_raw': {'train_loss': 14.754477, 'val_loss': 14.978391, 'test_loss': 16.511838}}
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2024-10-14 23:36:55,363 (server:615) INFO: {'Role': 'Server #', 'Round': 64, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.042231), 'test_loss': np.float64(98714.922919), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.055419), 'val_loss': np.float64(98783.29093)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.042231), 'test_loss': np.float64(98714.922919), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.055419), 'val_loss': np.float64(98783.29093)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.314814), 'test_avg_loss_bottom_decile': np.float64(16.015797), 'test_avg_loss_top_decile': np.float64(23.227775), 'test_avg_loss_min': np.float64(15.720518), 'test_avg_loss_max': np.float64(23.227775), 'test_avg_loss_bottom10%': np.float64(15.720518), 'test_avg_loss_top10%': np.float64(23.227775), 'test_avg_loss_cos1': np.float64(0.992692), 'test_avg_loss_entropy': np.float64(2.295216), 'test_loss_std': np.float64(11999.99599), 'test_loss_bottom_decile': np.float64(83025.892242), 'test_loss_top_decile': np.float64(120412.783508), 'test_loss_min': np.float64(81495.162994), 'test_loss_max': np.float64(120412.783508), 'test_loss_bottom10%': np.float64(81495.162994), 'test_loss_top10%': np.float64(120412.783508), 'test_loss_cos1': np.float64(0.992692), 'test_loss_entropy': np.float64(2.295216), 'val_avg_loss_std': np.float64(2.543252), 'val_avg_loss_bottom_decile': np.float64(15.719052), 'val_avg_loss_top_decile': np.float64(23.024957), 'val_avg_loss_min': np.float64(15.236133), 'val_avg_loss_max': np.float64(23.024957), 'val_avg_loss_bottom10%': np.float64(15.236133), 'val_avg_loss_top10%': np.float64(23.024957), 'val_avg_loss_cos1': np.float64(0.991211), 'val_avg_loss_entropy': np.float64(2.293629), 'val_loss_std': np.float64(13184.219036), 'val_loss_bottom_decile': np.float64(81487.565857), 'val_loss_top_decile': np.float64(119361.375671), 'val_loss_min': np.float64(78984.110962), 'val_loss_max': np.float64(119361.375671), 'val_loss_bottom10%': np.float64(78984.110962), 'val_loss_top10%': np.float64(119361.375671), 'val_loss_cos1': np.float64(0.991211), 'val_loss_entropy': np.float64(2.293629)}}
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2024-10-14 23:36:55,420 (server:353) INFO: Server: Starting evaluation at the end of round 65.
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2024-10-14 23:36:55,421 (server:359) INFO: ----------- Starting a new training round (Round #66) -------------
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2024-10-14 23:39:24,972 (client:354) INFO: {'Role': 'Client #4', 'Round': 66, 'Results_raw': {'train_loss': 14.763962, 'val_loss': 15.167328, 'test_loss': 16.429983}}
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2024-10-14 23:40:25,228 (client:354) INFO: {'Role': 'Client #6', 'Round': 66, 'Results_raw': {'train_loss': 14.743384, 'val_loss': 14.802064, 'test_loss': 16.239624}}
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2024-10-14 23:41:21,294 (client:354) INFO: {'Role': 'Client #9', 'Round': 66, 'Results_raw': {'train_loss': 17.453185, 'val_loss': 17.641677, 'test_loss': 18.616703}}
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2024-10-14 23:42:15,514 (client:354) INFO: {'Role': 'Client #5', 'Round': 66, 'Results_raw': {'train_loss': 15.595959, 'val_loss': 16.730536, 'test_loss': 18.854278}}
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2024-10-14 23:43:09,645 (client:354) INFO: {'Role': 'Client #2', 'Round': 66, 'Results_raw': {'train_loss': 8.572251, 'val_loss': 8.401738, 'test_loss': 8.922395}}
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2024-10-14 23:44:04,037 (client:354) INFO: {'Role': 'Client #10', 'Round': 66, 'Results_raw': {'train_loss': 14.709378, 'val_loss': 15.212703, 'test_loss': 16.768965}}
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2024-10-14 23:44:57,533 (client:354) INFO: {'Role': 'Client #8', 'Round': 66, 'Results_raw': {'train_loss': 13.008046, 'val_loss': 13.140998, 'test_loss': 13.879891}}
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2024-10-14 23:45:51,773 (client:354) INFO: {'Role': 'Client #1', 'Round': 66, 'Results_raw': {'train_loss': 10.414049, 'val_loss': 10.490337, 'test_loss': 11.667438}}
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2024-10-14 23:46:47,198 (client:354) INFO: {'Role': 'Client #7', 'Round': 66, 'Results_raw': {'train_loss': 15.025159, 'val_loss': 15.424149, 'test_loss': 15.902431}}
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2024-10-14 23:47:43,200 (client:354) INFO: {'Role': 'Client #3', 'Round': 66, 'Results_raw': {'train_loss': 9.854317, 'val_loss': 10.529433, 'test_loss': 11.95256}}
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2024-10-14 23:47:43,205 (server:615) INFO: {'Role': 'Server #', 'Round': 65, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.951642), 'test_loss': np.float64(98245.310052), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.968799), 'val_loss': np.float64(98334.256302)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.951642), 'test_loss': np.float64(98245.310052), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.968799), 'val_loss': np.float64(98334.256302)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.39883), 'test_avg_loss_bottom_decile': np.float64(15.714951), 'test_avg_loss_top_decile': np.float64(23.225789), 'test_avg_loss_min': np.float64(15.550915), 'test_avg_loss_max': np.float64(23.225789), 'test_avg_loss_bottom10%': np.float64(15.550915), 'test_avg_loss_top10%': np.float64(23.225789), 'test_avg_loss_cos1': np.float64(0.992084), 'test_avg_loss_entropy': np.float64(2.294582), 'test_loss_std': np.float64(12435.533326), 'test_loss_bottom_decile': np.float64(81466.306732), 'test_loss_top_decile': np.float64(120402.492432), 'test_loss_min': np.float64(80615.942413), 'test_loss_max': np.float64(120402.492432), 'test_loss_bottom10%': np.float64(80615.942413), 'test_loss_top10%': np.float64(120402.492432), 'test_loss_cos1': np.float64(0.992084), 'test_loss_entropy': np.float64(2.294582), 'val_avg_loss_std': np.float64(2.632012), 'val_avg_loss_bottom_decile': np.float64(15.418856), 'val_avg_loss_top_decile': np.float64(23.013377), 'val_avg_loss_min': np.float64(15.067487), 'val_avg_loss_max': np.float64(23.013377), 'val_avg_loss_bottom10%': np.float64(15.067487), 'val_avg_loss_top10%': np.float64(23.013377), 'val_avg_loss_cos1': np.float64(0.99051), 'val_avg_loss_entropy': np.float64(2.292886), 'val_loss_std': np.float64(13644.349184), 'val_loss_bottom_decile': np.float64(79931.350433), 'val_loss_top_decile': np.float64(119301.345093), 'val_loss_min': np.float64(78109.852509), 'val_loss_max': np.float64(119301.345093), 'val_loss_bottom10%': np.float64(78109.852509), 'val_loss_top10%': np.float64(119301.345093), 'val_loss_cos1': np.float64(0.99051), 'val_loss_entropy': np.float64(2.292886)}}
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2024-10-14 23:47:43,253 (server:353) INFO: Server: Starting evaluation at the end of round 66.
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2024-10-14 23:47:43,253 (server:359) INFO: ----------- Starting a new training round (Round #67) -------------
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2024-10-14 23:50:07,424 (client:354) INFO: {'Role': 'Client #3', 'Round': 67, 'Results_raw': {'train_loss': 9.838287, 'val_loss': 10.58614, 'test_loss': 12.041499}}
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2024-10-14 23:51:05,672 (client:354) INFO: {'Role': 'Client #9', 'Round': 67, 'Results_raw': {'train_loss': 17.464547, 'val_loss': 17.748924, 'test_loss': 18.734185}}
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2024-10-14 23:52:06,407 (client:354) INFO: {'Role': 'Client #5', 'Round': 67, 'Results_raw': {'train_loss': 15.546307, 'val_loss': 16.577516, 'test_loss': 18.872437}}
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2024-10-14 23:53:01,191 (client:354) INFO: {'Role': 'Client #7', 'Round': 67, 'Results_raw': {'train_loss': 15.026378, 'val_loss': 15.365613, 'test_loss': 16.294525}}
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2024-10-14 23:53:55,047 (client:354) INFO: {'Role': 'Client #4', 'Round': 67, 'Results_raw': {'train_loss': 14.78747, 'val_loss': 15.068353, 'test_loss': 16.262178}}
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2024-10-14 23:54:52,949 (client:354) INFO: {'Role': 'Client #1', 'Round': 67, 'Results_raw': {'train_loss': 10.414857, 'val_loss': 10.465346, 'test_loss': 11.561484}}
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2024-10-14 23:55:48,030 (client:354) INFO: {'Role': 'Client #6', 'Round': 67, 'Results_raw': {'train_loss': 14.736955, 'val_loss': 14.819503, 'test_loss': 16.223018}}
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2024-10-14 23:56:44,852 (client:354) INFO: {'Role': 'Client #10', 'Round': 67, 'Results_raw': {'train_loss': 14.650447, 'val_loss': 15.223333, 'test_loss': 16.889174}}
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2024-10-14 23:57:41,934 (client:354) INFO: {'Role': 'Client #2', 'Round': 67, 'Results_raw': {'train_loss': 8.516964, 'val_loss': 8.443636, 'test_loss': 9.165636}}
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2024-10-14 23:58:35,954 (client:354) INFO: {'Role': 'Client #8', 'Round': 67, 'Results_raw': {'train_loss': 13.016329, 'val_loss': 13.202229, 'test_loss': 14.085242}}
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2024-10-14 23:58:35,959 (server:615) INFO: {'Role': 'Server #', 'Round': 66, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.070452), 'test_loss': np.float64(98861.222836), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.083582), 'val_loss': np.float64(98929.288391)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.070452), 'test_loss': np.float64(98861.222836), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.083582), 'val_loss': np.float64(98929.288391)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.318084), 'test_avg_loss_bottom_decile': np.float64(16.135163), 'test_avg_loss_top_decile': np.float64(23.308365), 'test_avg_loss_min': np.float64(15.682951), 'test_avg_loss_max': np.float64(23.308365), 'test_avg_loss_bottom10%': np.float64(15.682951), 'test_avg_loss_top10%': np.float64(23.308365), 'test_avg_loss_cos1': np.float64(0.992693), 'test_avg_loss_entropy': np.float64(2.295223), 'test_loss_std': np.float64(12016.945254), 'test_loss_bottom_decile': np.float64(83644.683472), 'test_loss_top_decile': np.float64(120830.563782), 'test_loss_min': np.float64(81300.41861), 'test_loss_max': np.float64(120830.563782), 'test_loss_bottom10%': np.float64(81300.41861), 'test_loss_top10%': np.float64(120830.563782), 'test_loss_cos1': np.float64(0.992693), 'test_loss_entropy': np.float64(2.295223), 'val_avg_loss_std': np.float64(2.543711), 'val_avg_loss_bottom_decile': np.float64(15.845101), 'val_avg_loss_top_decile': np.float64(23.076092), 'val_avg_loss_min': np.float64(15.202202), 'val_avg_loss_max': np.float64(23.076092), 'val_avg_loss_bottom10%': np.float64(15.202202), 'val_avg_loss_top10%': np.float64(23.076092), 'val_avg_loss_cos1': np.float64(0.991233), 'val_avg_loss_entropy': np.float64(2.293655), 'val_loss_std': np.float64(13186.596568), 'val_loss_bottom_decile': np.float64(82141.002106), 'val_loss_top_decile': np.float64(119626.458801), 'val_loss_min': np.float64(78808.21521), 'val_loss_max': np.float64(119626.458801), 'val_loss_bottom10%': np.float64(78808.21521), 'val_loss_top10%': np.float64(119626.458801), 'val_loss_cos1': np.float64(0.991233), 'val_loss_entropy': np.float64(2.293655)}}
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2024-10-14 23:58:36,007 (server:353) INFO: Server: Starting evaluation at the end of round 67.
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2024-10-14 23:58:36,008 (server:359) INFO: ----------- Starting a new training round (Round #68) -------------
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2024-10-15 00:00:58,800 (client:354) INFO: {'Role': 'Client #9', 'Round': 68, 'Results_raw': {'train_loss': 17.482089, 'val_loss': 17.630178, 'test_loss': 18.364412}}
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2024-10-15 00:01:56,406 (client:354) INFO: {'Role': 'Client #2', 'Round': 68, 'Results_raw': {'train_loss': 8.493836, 'val_loss': 8.54934, 'test_loss': 9.142066}}
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2024-10-15 00:02:56,107 (client:354) INFO: {'Role': 'Client #5', 'Round': 68, 'Results_raw': {'train_loss': 15.569083, 'val_loss': 16.63213, 'test_loss': 18.858926}}
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2024-10-15 00:03:53,722 (client:354) INFO: {'Role': 'Client #1', 'Round': 68, 'Results_raw': {'train_loss': 10.435469, 'val_loss': 10.358144, 'test_loss': 11.517296}}
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2024-10-15 00:04:48,352 (client:354) INFO: {'Role': 'Client #10', 'Round': 68, 'Results_raw': {'train_loss': 14.667157, 'val_loss': 15.296836, 'test_loss': 16.871204}}
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2024-10-15 00:05:45,938 (client:354) INFO: {'Role': 'Client #4', 'Round': 68, 'Results_raw': {'train_loss': 14.744007, 'val_loss': 14.952022, 'test_loss': 16.08058}}
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2024-10-15 00:06:42,087 (client:354) INFO: {'Role': 'Client #3', 'Round': 68, 'Results_raw': {'train_loss': 9.878926, 'val_loss': 10.634955, 'test_loss': 12.114976}}
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2024-10-15 00:07:39,783 (client:354) INFO: {'Role': 'Client #7', 'Round': 68, 'Results_raw': {'train_loss': 14.988423, 'val_loss': 15.406629, 'test_loss': 16.12971}}
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2024-10-15 00:08:35,672 (client:354) INFO: {'Role': 'Client #8', 'Round': 68, 'Results_raw': {'train_loss': 13.011446, 'val_loss': 13.129403, 'test_loss': 13.720685}}
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2024-10-15 00:09:32,454 (client:354) INFO: {'Role': 'Client #6', 'Round': 68, 'Results_raw': {'train_loss': 14.728473, 'val_loss': 14.835378, 'test_loss': 16.261539}}
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2024-10-15 00:09:32,459 (server:615) INFO: {'Role': 'Server #', 'Round': 67, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.162856), 'test_loss': np.float64(99340.248071), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.148074), 'val_loss': np.float64(99263.616443)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(19.162856), 'test_loss': np.float64(99340.248071), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(19.148074), 'val_loss': np.float64(99263.616443)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.22788), 'test_avg_loss_bottom_decile': np.float64(16.452428), 'test_avg_loss_top_decile': np.float64(23.278303), 'test_avg_loss_min': np.float64(15.854556), 'test_avg_loss_max': np.float64(23.278303), 'test_avg_loss_bottom10%': np.float64(15.854556), 'test_avg_loss_top10%': np.float64(23.278303), 'test_avg_loss_cos1': np.float64(0.99331), 'test_avg_loss_entropy': np.float64(2.295857), 'test_loss_std': np.float64(11549.329567), 'test_loss_bottom_decile': np.float64(85289.385254), 'test_loss_top_decile': np.float64(120674.723328), 'test_loss_min': np.float64(82190.017181), 'test_loss_max': np.float64(120674.723328), 'test_loss_bottom10%': np.float64(82190.017181), 'test_loss_top10%': np.float64(120674.723328), 'test_loss_cos1': np.float64(0.99331), 'test_loss_entropy': np.float64(2.295857), 'val_avg_loss_std': np.float64(2.45244), 'val_avg_loss_bottom_decile': np.float64(16.141882), 'val_avg_loss_top_decile': np.float64(23.031513), 'val_avg_loss_min': np.float64(15.345872), 'val_avg_loss_max': np.float64(23.031513), 'val_avg_loss_bottom10%': np.float64(15.345872), 'val_avg_loss_top10%': np.float64(23.031513), 'val_avg_loss_cos1': np.float64(0.991898), 'val_avg_loss_entropy': np.float64(2.294349), 'val_loss_std': np.float64(12713.448365), 'val_loss_bottom_decile': np.float64(83679.51886), 'val_loss_top_decile': np.float64(119395.363831), 'val_loss_min': np.float64(79553.002228), 'val_loss_max': np.float64(119395.363831), 'val_loss_bottom10%': np.float64(79553.002228), 'val_loss_top10%': np.float64(119395.363831), 'val_loss_cos1': np.float64(0.991898), 'val_loss_entropy': np.float64(2.294349)}}
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2024-10-15 00:09:32,499 (server:353) INFO: Server: Starting evaluation at the end of round 68.
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2024-10-15 00:09:32,499 (server:359) INFO: ----------- Starting a new training round (Round #69) -------------
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2024-10-15 00:11:55,201 (client:354) INFO: {'Role': 'Client #10', 'Round': 69, 'Results_raw': {'train_loss': 14.659336, 'val_loss': 15.244031, 'test_loss': 16.865996}}
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2024-10-15 00:12:50,823 (client:354) INFO: {'Role': 'Client #8', 'Round': 69, 'Results_raw': {'train_loss': 13.0001, 'val_loss': 13.169834, 'test_loss': 13.854563}}
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2024-10-15 00:13:43,569 (client:354) INFO: {'Role': 'Client #6', 'Round': 69, 'Results_raw': {'train_loss': 14.713955, 'val_loss': 14.859689, 'test_loss': 16.187411}}
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2024-10-15 00:14:39,849 (client:354) INFO: {'Role': 'Client #5', 'Round': 69, 'Results_raw': {'train_loss': 15.585291, 'val_loss': 16.658942, 'test_loss': 18.865734}}
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2024-10-15 00:15:40,904 (client:354) INFO: {'Role': 'Client #9', 'Round': 69, 'Results_raw': {'train_loss': 17.415053, 'val_loss': 17.586202, 'test_loss': 18.357076}}
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2024-10-15 00:16:36,727 (client:354) INFO: {'Role': 'Client #7', 'Round': 69, 'Results_raw': {'train_loss': 15.012379, 'val_loss': 15.339556, 'test_loss': 16.1473}}
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2024-10-15 00:17:32,743 (client:354) INFO: {'Role': 'Client #2', 'Round': 69, 'Results_raw': {'train_loss': 8.458531, 'val_loss': 8.421448, 'test_loss': 9.137471}}
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2024-10-15 00:18:28,356 (client:354) INFO: {'Role': 'Client #4', 'Round': 69, 'Results_raw': {'train_loss': 14.74598, 'val_loss': 15.090303, 'test_loss': 16.273879}}
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2024-10-15 00:19:28,985 (client:354) INFO: {'Role': 'Client #1', 'Round': 69, 'Results_raw': {'train_loss': 10.420779, 'val_loss': 10.398333, 'test_loss': 11.402764}}
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2024-10-15 00:20:25,374 (client:354) INFO: {'Role': 'Client #3', 'Round': 69, 'Results_raw': {'train_loss': 9.886058, 'val_loss': 10.509421, 'test_loss': 11.888257}}
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2024-10-15 00:20:25,378 (server:615) INFO: {'Role': 'Server #', 'Round': 68, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.980026), 'test_loss': np.float64(98392.455908), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.992598), 'val_loss': np.float64(98457.629892)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.980026), 'test_loss': np.float64(98392.455908), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.992598), 'val_loss': np.float64(98457.629892)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.336759), 'test_avg_loss_bottom_decile': np.float64(15.969223), 'test_avg_loss_top_decile': np.float64(23.237221), 'test_avg_loss_min': np.float64(15.602492), 'test_avg_loss_max': np.float64(23.237221), 'test_avg_loss_bottom10%': np.float64(15.602492), 'test_avg_loss_top10%': np.float64(23.237221), 'test_avg_loss_cos1': np.float64(0.992506), 'test_avg_loss_entropy': np.float64(2.295031), 'test_loss_std': np.float64(12113.760916), 'test_loss_bottom_decile': np.float64(82784.453461), 'test_loss_top_decile': np.float64(120461.755676), 'test_loss_min': np.float64(80883.318115), 'test_loss_max': np.float64(120461.755676), 'test_loss_bottom10%': np.float64(80883.318115), 'test_loss_top10%': np.float64(120461.755676), 'test_loss_cos1': np.float64(0.992506), 'test_loss_entropy': np.float64(2.295031), 'val_avg_loss_std': np.float64(2.574737), 'val_avg_loss_bottom_decile': np.float64(15.654344), 'val_avg_loss_top_decile': np.float64(23.019853), 'val_avg_loss_min': np.float64(15.105172), 'val_avg_loss_max': np.float64(23.019853), 'val_avg_loss_bottom10%': np.float64(15.105172), 'val_avg_loss_top10%': np.float64(23.019853), 'val_avg_loss_cos1': np.float64(0.990936), 'val_avg_loss_entropy': np.float64(2.293345), 'val_loss_std': np.float64(13347.436241), 'val_loss_bottom_decile': np.float64(81152.119354), 'val_loss_top_decile': np.float64(119334.920471), 'val_loss_min': np.float64(78305.209869), 'val_loss_max': np.float64(119334.920471), 'val_loss_bottom10%': np.float64(78305.209869), 'val_loss_top10%': np.float64(119334.920471), 'val_loss_cos1': np.float64(0.990936), 'val_loss_entropy': np.float64(2.293345)}}
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2024-10-15 00:20:25,418 (server:353) INFO: Server: Starting evaluation at the end of round 69.
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2024-10-15 00:20:25,418 (server:359) INFO: ----------- Starting a new training round (Round #70) -------------
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2024-10-15 00:22:49,207 (client:354) INFO: {'Role': 'Client #4', 'Round': 70, 'Results_raw': {'train_loss': 14.72351, 'val_loss': 15.100839, 'test_loss': 16.608474}}
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2024-10-15 00:23:45,485 (client:354) INFO: {'Role': 'Client #1', 'Round': 70, 'Results_raw': {'train_loss': 10.421449, 'val_loss': 10.414949, 'test_loss': 11.426777}}
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2024-10-15 00:24:42,107 (client:354) INFO: {'Role': 'Client #10', 'Round': 70, 'Results_raw': {'train_loss': 14.666792, 'val_loss': 15.174521, 'test_loss': 16.745697}}
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2024-10-15 00:25:40,289 (client:354) INFO: {'Role': 'Client #9', 'Round': 70, 'Results_raw': {'train_loss': 17.409783, 'val_loss': 17.553948, 'test_loss': 18.272681}}
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2024-10-15 00:26:37,107 (client:354) INFO: {'Role': 'Client #6', 'Round': 70, 'Results_raw': {'train_loss': 14.712501, 'val_loss': 14.824449, 'test_loss': 15.909894}}
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2024-10-15 00:27:33,397 (client:354) INFO: {'Role': 'Client #7', 'Round': 70, 'Results_raw': {'train_loss': 14.988471, 'val_loss': 15.273161, 'test_loss': 16.094593}}
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2024-10-15 00:28:28,288 (client:354) INFO: {'Role': 'Client #2', 'Round': 70, 'Results_raw': {'train_loss': 8.45714, 'val_loss': 8.490765, 'test_loss': 8.941885}}
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2024-10-15 00:29:21,571 (client:354) INFO: {'Role': 'Client #8', 'Round': 70, 'Results_raw': {'train_loss': 12.943375, 'val_loss': 13.217238, 'test_loss': 13.985564}}
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2024-10-15 00:30:13,943 (client:354) INFO: {'Role': 'Client #3', 'Round': 70, 'Results_raw': {'train_loss': 9.869477, 'val_loss': 10.762131, 'test_loss': 12.168696}}
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2024-10-15 00:31:10,791 (client:354) INFO: {'Role': 'Client #5', 'Round': 70, 'Results_raw': {'train_loss': 15.579437, 'val_loss': 16.732237, 'test_loss': 19.049354}}
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2024-10-15 00:31:10,796 (server:615) INFO: {'Role': 'Server #', 'Round': 69, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.953796), 'test_loss': np.float64(98256.47677), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.964898), 'val_loss': np.float64(98314.031677)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.953796), 'test_loss': np.float64(98256.47677), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.964898), 'val_loss': np.float64(98314.031677)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.290113), 'test_avg_loss_bottom_decile': np.float64(15.984274), 'test_avg_loss_top_decile': np.float64(23.13397), 'test_avg_loss_min': np.float64(15.689328), 'test_avg_loss_max': np.float64(23.13397), 'test_avg_loss_bottom10%': np.float64(15.689328), 'test_avg_loss_top10%': np.float64(23.13397), 'test_avg_loss_cos1': np.float64(0.992779), 'test_avg_loss_entropy': np.float64(2.295311), 'test_loss_std': np.float64(11871.947056), 'test_loss_bottom_decile': np.float64(82862.477722), 'test_loss_top_decile': np.float64(119926.500427), 'test_loss_min': np.float64(81333.478455), 'test_loss_max': np.float64(119926.500427), 'test_loss_bottom10%': np.float64(81333.478455), 'test_loss_top10%': np.float64(119926.500427), 'test_loss_cos1': np.float64(0.992779), 'test_loss_entropy': np.float64(2.295311), 'val_avg_loss_std': np.float64(2.517584), 'val_avg_loss_bottom_decile': np.float64(15.685838), 'val_avg_loss_top_decile': np.float64(22.907969), 'val_avg_loss_min': np.float64(15.200372), 'val_avg_loss_max': np.float64(22.907969), 'val_avg_loss_bottom10%': np.float64(15.200372), 'val_avg_loss_top10%': np.float64(22.907969), 'val_avg_loss_cos1': np.float64(0.991304), 'val_avg_loss_entropy': np.float64(2.293728), 'val_loss_std': np.float64(13051.157947), 'val_loss_bottom_decile': np.float64(81315.383209), 'val_loss_top_decile': np.float64(118754.911316), 'val_loss_min': np.float64(78798.72818), 'val_loss_max': np.float64(118754.911316), 'val_loss_bottom10%': np.float64(78798.72818), 'val_loss_top10%': np.float64(118754.911316), 'val_loss_cos1': np.float64(0.991304), 'val_loss_entropy': np.float64(2.293728)}}
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2024-10-15 00:31:10,835 (server:353) INFO: Server: Starting evaluation at the end of round 70.
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2024-10-15 00:31:10,835 (server:359) INFO: ----------- Starting a new training round (Round #71) -------------
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2024-10-15 00:33:34,701 (client:354) INFO: {'Role': 'Client #10', 'Round': 71, 'Results_raw': {'train_loss': 14.652916, 'val_loss': 15.221433, 'test_loss': 16.989012}}
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2024-10-15 00:34:34,995 (client:354) INFO: {'Role': 'Client #3', 'Round': 71, 'Results_raw': {'train_loss': 9.815335, 'val_loss': 10.519156, 'test_loss': 12.045347}}
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2024-10-15 00:35:29,303 (client:354) INFO: {'Role': 'Client #6', 'Round': 71, 'Results_raw': {'train_loss': 14.729027, 'val_loss': 14.892356, 'test_loss': 16.167326}}
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2024-10-15 00:36:23,074 (client:354) INFO: {'Role': 'Client #4', 'Round': 71, 'Results_raw': {'train_loss': 14.756028, 'val_loss': 14.952525, 'test_loss': 16.181481}}
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2024-10-15 00:37:17,110 (client:354) INFO: {'Role': 'Client #5', 'Round': 71, 'Results_raw': {'train_loss': 15.580417, 'val_loss': 16.631627, 'test_loss': 18.775637}}
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2024-10-15 00:38:13,810 (client:354) INFO: {'Role': 'Client #9', 'Round': 71, 'Results_raw': {'train_loss': 17.423448, 'val_loss': 17.689821, 'test_loss': 18.506524}}
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2024-10-15 00:39:17,792 (client:354) INFO: {'Role': 'Client #7', 'Round': 71, 'Results_raw': {'train_loss': 14.971434, 'val_loss': 15.480556, 'test_loss': 16.300622}}
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2024-10-15 00:40:15,147 (client:354) INFO: {'Role': 'Client #1', 'Round': 71, 'Results_raw': {'train_loss': 10.41912, 'val_loss': 10.657555, 'test_loss': 11.58751}}
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2024-10-15 00:41:10,845 (client:354) INFO: {'Role': 'Client #8', 'Round': 71, 'Results_raw': {'train_loss': 12.98525, 'val_loss': 13.141286, 'test_loss': 13.841224}}
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2024-10-15 00:42:08,994 (client:354) INFO: {'Role': 'Client #2', 'Round': 71, 'Results_raw': {'train_loss': 8.471098, 'val_loss': 8.368553, 'test_loss': 8.986745}}
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2024-10-15 00:42:08,998 (server:615) INFO: {'Role': 'Server #', 'Round': 70, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.890091), 'test_loss': np.float64(97926.23208), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.910885), 'val_loss': np.float64(98034.027731)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.890091), 'test_loss': np.float64(97926.23208), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.910885), 'val_loss': np.float64(98034.027731)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.315431), 'test_avg_loss_bottom_decile': np.float64(15.803154), 'test_avg_loss_top_decile': np.float64(23.049163), 'test_avg_loss_min': np.float64(15.617136), 'test_avg_loss_max': np.float64(23.049163), 'test_avg_loss_bottom10%': np.float64(15.617136), 'test_avg_loss_top10%': np.float64(23.049163), 'test_avg_loss_cos1': np.float64(0.992571), 'test_avg_loss_entropy': np.float64(2.29509), 'test_loss_std': np.float64(12003.195064), 'test_loss_bottom_decile': np.float64(81923.548492), 'test_loss_top_decile': np.float64(119486.858948), 'test_loss_min': np.float64(80959.234009), 'test_loss_max': np.float64(119486.858948), 'test_loss_bottom10%': np.float64(80959.234009), 'test_loss_top10%': np.float64(119486.858948), 'test_loss_cos1': np.float64(0.992571), 'test_loss_entropy': np.float64(2.29509), 'val_avg_loss_std': np.float64(2.54796), 'val_avg_loss_bottom_decile': np.float64(15.516367), 'val_avg_loss_top_decile': np.float64(22.845556), 'val_avg_loss_min': np.float64(15.133946), 'val_avg_loss_max': np.float64(22.845556), 'val_avg_loss_bottom10%': np.float64(15.133946), 'val_avg_loss_top10%': np.float64(22.845556), 'val_avg_loss_cos1': np.float64(0.991045), 'val_avg_loss_entropy': np.float64(2.293453), 'val_loss_std': np.float64(13208.623454), 'val_loss_bottom_decile': np.float64(80436.844788), 'val_loss_top_decile': np.float64(118431.363037), 'val_loss_min': np.float64(78454.376221), 'val_loss_max': np.float64(118431.363037), 'val_loss_bottom10%': np.float64(78454.376221), 'val_loss_top10%': np.float64(118431.363037), 'val_loss_cos1': np.float64(0.991045), 'val_loss_entropy': np.float64(2.293453)}}
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2024-10-15 00:42:09,046 (server:353) INFO: Server: Starting evaluation at the end of round 71.
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2024-10-15 00:42:09,047 (server:359) INFO: ----------- Starting a new training round (Round #72) -------------
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2024-10-15 00:44:31,551 (client:354) INFO: {'Role': 'Client #9', 'Round': 72, 'Results_raw': {'train_loss': 17.457575, 'val_loss': 17.626068, 'test_loss': 18.303324}}
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2024-10-15 00:45:26,393 (client:354) INFO: {'Role': 'Client #5', 'Round': 72, 'Results_raw': {'train_loss': 15.54043, 'val_loss': 16.64094, 'test_loss': 18.76452}}
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2024-10-15 00:46:24,158 (client:354) INFO: {'Role': 'Client #10', 'Round': 72, 'Results_raw': {'train_loss': 14.623496, 'val_loss': 15.356316, 'test_loss': 17.258036}}
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2024-10-15 00:47:18,432 (client:354) INFO: {'Role': 'Client #4', 'Round': 72, 'Results_raw': {'train_loss': 14.730943, 'val_loss': 15.029844, 'test_loss': 16.125585}}
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2024-10-15 00:48:12,720 (client:354) INFO: {'Role': 'Client #6', 'Round': 72, 'Results_raw': {'train_loss': 14.717121, 'val_loss': 14.852806, 'test_loss': 16.373095}}
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2024-10-15 00:49:08,239 (client:354) INFO: {'Role': 'Client #7', 'Round': 72, 'Results_raw': {'train_loss': 14.964166, 'val_loss': 15.270124, 'test_loss': 15.954176}}
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2024-10-15 00:50:01,772 (client:354) INFO: {'Role': 'Client #1', 'Round': 72, 'Results_raw': {'train_loss': 10.401669, 'val_loss': 10.478126, 'test_loss': 11.526801}}
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2024-10-15 00:51:02,816 (client:354) INFO: {'Role': 'Client #8', 'Round': 72, 'Results_raw': {'train_loss': 12.958462, 'val_loss': 13.193461, 'test_loss': 13.947133}}
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2024-10-15 00:51:58,173 (client:354) INFO: {'Role': 'Client #3', 'Round': 72, 'Results_raw': {'train_loss': 9.775392, 'val_loss': 10.536751, 'test_loss': 11.983723}}
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2024-10-15 00:52:55,863 (client:354) INFO: {'Role': 'Client #2', 'Round': 72, 'Results_raw': {'train_loss': 8.474571, 'val_loss': 8.4611, 'test_loss': 9.051697}}
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2024-10-15 00:52:55,867 (server:615) INFO: {'Role': 'Server #', 'Round': 71, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.989279), 'test_loss': np.float64(98440.424368), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.985465), 'val_loss': np.float64(98420.651172)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.989279), 'test_loss': np.float64(98440.424368), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.985465), 'val_loss': np.float64(98420.651172)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.302563), 'test_avg_loss_bottom_decile': np.float64(15.990622), 'test_avg_loss_top_decile': np.float64(23.164095), 'test_avg_loss_min': np.float64(15.650417), 'test_avg_loss_max': np.float64(23.164095), 'test_avg_loss_bottom10%': np.float64(15.650417), 'test_avg_loss_top10%': np.float64(23.164095), 'test_avg_loss_cos1': np.float64(0.992729), 'test_avg_loss_entropy': np.float64(2.295254), 'test_loss_std': np.float64(11936.48631), 'test_loss_bottom_decile': np.float64(82895.386536), 'test_loss_top_decile': np.float64(120082.669556), 'test_loss_min': np.float64(81131.762085), 'test_loss_max': np.float64(120082.669556), 'test_loss_bottom10%': np.float64(81131.762085), 'test_loss_top10%': np.float64(120082.669556), 'test_loss_cos1': np.float64(0.992729), 'test_loss_entropy': np.float64(2.295254), 'val_avg_loss_std': np.float64(2.525544), 'val_avg_loss_bottom_decile': np.float64(15.694499), 'val_avg_loss_top_decile': np.float64(22.911763), 'val_avg_loss_min': np.float64(15.149899), 'val_avg_loss_max': np.float64(22.911763), 'val_avg_loss_bottom10%': np.float64(15.149899), 'val_avg_loss_top10%': np.float64(22.911763), 'val_avg_loss_cos1': np.float64(0.991268), 'val_avg_loss_entropy': np.float64(2.293686), 'val_loss_std': np.float64(13092.417988), 'val_loss_bottom_decile': np.float64(81360.281738), 'val_loss_top_decile': np.float64(118774.577637), 'val_loss_min': np.float64(78537.078766), 'val_loss_max': np.float64(118774.577637), 'val_loss_bottom10%': np.float64(78537.078766), 'val_loss_top10%': np.float64(118774.577637), 'val_loss_cos1': np.float64(0.991268), 'val_loss_entropy': np.float64(2.293686)}}
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2024-10-15 00:52:55,904 (server:353) INFO: Server: Starting evaluation at the end of round 72.
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2024-10-15 00:52:55,904 (server:359) INFO: ----------- Starting a new training round (Round #73) -------------
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2024-10-15 00:55:26,303 (client:354) INFO: {'Role': 'Client #1', 'Round': 73, 'Results_raw': {'train_loss': 10.383717, 'val_loss': 10.411927, 'test_loss': 11.514474}}
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2024-10-15 00:56:24,931 (client:354) INFO: {'Role': 'Client #8', 'Round': 73, 'Results_raw': {'train_loss': 12.967646, 'val_loss': 13.176555, 'test_loss': 14.022468}}
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2024-10-15 00:57:22,129 (client:354) INFO: {'Role': 'Client #5', 'Round': 73, 'Results_raw': {'train_loss': 15.524092, 'val_loss': 16.601261, 'test_loss': 18.759528}}
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2024-10-15 00:58:19,427 (client:354) INFO: {'Role': 'Client #9', 'Round': 73, 'Results_raw': {'train_loss': 17.39249, 'val_loss': 17.702969, 'test_loss': 18.581339}}
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2024-10-15 00:59:13,699 (client:354) INFO: {'Role': 'Client #6', 'Round': 73, 'Results_raw': {'train_loss': 14.72822, 'val_loss': 14.919842, 'test_loss': 16.342206}}
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2024-10-15 01:00:08,460 (client:354) INFO: {'Role': 'Client #2', 'Round': 73, 'Results_raw': {'train_loss': 8.539773, 'val_loss': 8.556337, 'test_loss': 9.269603}}
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2024-10-15 01:01:05,489 (client:354) INFO: {'Role': 'Client #3', 'Round': 73, 'Results_raw': {'train_loss': 9.794046, 'val_loss': 10.485299, 'test_loss': 11.942781}}
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2024-10-15 01:02:03,053 (client:354) INFO: {'Role': 'Client #10', 'Round': 73, 'Results_raw': {'train_loss': 14.640629, 'val_loss': 15.283175, 'test_loss': 16.698985}}
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2024-10-15 01:02:56,553 (client:354) INFO: {'Role': 'Client #7', 'Round': 73, 'Results_raw': {'train_loss': 14.990144, 'val_loss': 15.29718, 'test_loss': 15.813902}}
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2024-10-15 01:03:55,223 (client:354) INFO: {'Role': 'Client #4', 'Round': 73, 'Results_raw': {'train_loss': 14.720404, 'val_loss': 15.123594, 'test_loss': 16.281536}}
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2024-10-15 01:03:55,228 (server:615) INFO: {'Role': 'Server #', 'Round': 72, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.930701), 'test_loss': np.float64(98136.752795), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.924793), 'val_loss': np.float64(98106.126028)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.930701), 'test_loss': np.float64(98136.752795), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.924793), 'val_loss': np.float64(98106.126028)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.280276), 'test_avg_loss_bottom_decile': np.float64(15.940998), 'test_avg_loss_top_decile': np.float64(23.084745), 'test_avg_loss_min': np.float64(15.694413), 'test_avg_loss_max': np.float64(23.084745), 'test_avg_loss_bottom10%': np.float64(15.694413), 'test_avg_loss_top10%': np.float64(23.084745), 'test_avg_loss_cos1': np.float64(0.992823), 'test_avg_loss_entropy': np.float64(2.295353), 'test_loss_std': np.float64(11820.950049), 'test_loss_bottom_decile': np.float64(82638.135071), 'test_loss_top_decile': np.float64(119671.31958), 'test_loss_min': np.float64(81359.837219), 'test_loss_max': np.float64(119671.31958), 'test_loss_bottom10%': np.float64(81359.837219), 'test_loss_top10%': np.float64(119671.31958), 'test_loss_cos1': np.float64(0.992823), 'test_loss_entropy': np.float64(2.295353), 'val_avg_loss_std': np.float64(2.499129), 'val_avg_loss_bottom_decile': np.float64(15.661186), 'val_avg_loss_top_decile': np.float64(22.801443), 'val_avg_loss_min': np.float64(15.195002), 'val_avg_loss_max': np.float64(22.801443), 'val_avg_loss_bottom10%': np.float64(15.195002), 'val_avg_loss_top10%': np.float64(22.801443), 'val_avg_loss_cos1': np.float64(0.991393), 'val_avg_loss_entropy': np.float64(2.293817), 'val_loss_std': np.float64(12955.486744), 'val_loss_bottom_decile': np.float64(81187.58963), 'val_loss_top_decile': np.float64(118202.681458), 'val_loss_min': np.float64(78770.889221), 'val_loss_max': np.float64(118202.681458), 'val_loss_bottom10%': np.float64(78770.889221), 'val_loss_top10%': np.float64(118202.681458), 'val_loss_cos1': np.float64(0.991393), 'val_loss_entropy': np.float64(2.293817)}}
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2024-10-15 01:03:55,268 (server:353) INFO: Server: Starting evaluation at the end of round 73.
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2024-10-15 01:03:55,269 (server:359) INFO: ----------- Starting a new training round (Round #74) -------------
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2024-10-15 01:06:13,999 (client:354) INFO: {'Role': 'Client #4', 'Round': 74, 'Results_raw': {'train_loss': 14.729695, 'val_loss': 14.990354, 'test_loss': 16.21237}}
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2024-10-15 01:07:08,960 (client:354) INFO: {'Role': 'Client #8', 'Round': 74, 'Results_raw': {'train_loss': 12.960794, 'val_loss': 13.105968, 'test_loss': 13.853639}}
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2024-10-15 01:08:05,472 (client:354) INFO: {'Role': 'Client #5', 'Round': 74, 'Results_raw': {'train_loss': 15.526222, 'val_loss': 16.542481, 'test_loss': 18.702551}}
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2024-10-15 01:08:58,777 (client:354) INFO: {'Role': 'Client #6', 'Round': 74, 'Results_raw': {'train_loss': 14.701108, 'val_loss': 14.96892, 'test_loss': 16.639298}}
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2024-10-15 01:09:55,061 (client:354) INFO: {'Role': 'Client #3', 'Round': 74, 'Results_raw': {'train_loss': 9.80572, 'val_loss': 10.656249, 'test_loss': 12.242224}}
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2024-10-15 01:10:51,852 (client:354) INFO: {'Role': 'Client #9', 'Round': 74, 'Results_raw': {'train_loss': 17.426198, 'val_loss': 17.574952, 'test_loss': 18.385701}}
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2024-10-15 01:11:49,367 (client:354) INFO: {'Role': 'Client #1', 'Round': 74, 'Results_raw': {'train_loss': 10.416845, 'val_loss': 10.4952, 'test_loss': 11.511717}}
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2024-10-15 01:12:46,454 (client:354) INFO: {'Role': 'Client #10', 'Round': 74, 'Results_raw': {'train_loss': 14.626231, 'val_loss': 15.220633, 'test_loss': 17.083026}}
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2024-10-15 01:13:43,022 (client:354) INFO: {'Role': 'Client #2', 'Round': 74, 'Results_raw': {'train_loss': 8.43445, 'val_loss': 8.351604, 'test_loss': 8.965456}}
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2024-10-15 01:14:38,118 (client:354) INFO: {'Role': 'Client #7', 'Round': 74, 'Results_raw': {'train_loss': 14.972481, 'val_loss': 15.408417, 'test_loss': 16.040713}}
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2024-10-15 01:14:38,122 (server:615) INFO: {'Role': 'Server #', 'Round': 73, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.906735), 'test_loss': np.float64(98012.514175), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.922472), 'val_loss': np.float64(98094.097159)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.906735), 'test_loss': np.float64(98012.514175), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.922472), 'val_loss': np.float64(98094.097159)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.328083), 'test_avg_loss_bottom_decile': np.float64(15.886875), 'test_avg_loss_top_decile': np.float64(23.130731), 'test_avg_loss_min': np.float64(15.619463), 'test_avg_loss_max': np.float64(23.130731), 'test_avg_loss_bottom10%': np.float64(15.619463), 'test_avg_loss_top10%': np.float64(23.130731), 'test_avg_loss_cos1': np.float64(0.992504), 'test_avg_loss_entropy': np.float64(2.295031), 'test_loss_std': np.float64(12068.784572), 'test_loss_bottom_decile': np.float64(82357.559052), 'test_loss_top_decile': np.float64(119909.71106), 'test_loss_min': np.float64(80971.295197), 'test_loss_max': np.float64(119909.71106), 'test_loss_bottom10%': np.float64(80971.295197), 'test_loss_top10%': np.float64(119909.71106), 'test_loss_cos1': np.float64(0.992504), 'test_loss_entropy': np.float64(2.295031), 'val_avg_loss_std': np.float64(2.55788), 'val_avg_loss_bottom_decile': np.float64(15.584205), 'val_avg_loss_top_decile': np.float64(22.863152), 'val_avg_loss_min': np.float64(15.120915), 'val_avg_loss_max': np.float64(22.863152), 'val_avg_loss_bottom10%': np.float64(15.120915), 'val_avg_loss_top10%': np.float64(22.863152), 'val_avg_loss_cos1': np.float64(0.990987), 'val_avg_loss_entropy': np.float64(2.293397), 'val_loss_std': np.float64(13260.050529), 'val_loss_bottom_decile': np.float64(80788.517578), 'val_loss_top_decile': np.float64(118522.579041), 'val_loss_min': np.float64(78386.825897), 'val_loss_max': np.float64(118522.579041), 'val_loss_bottom10%': np.float64(78386.825897), 'val_loss_top10%': np.float64(118522.579041), 'val_loss_cos1': np.float64(0.990987), 'val_loss_entropy': np.float64(2.293397)}}
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2024-10-15 01:14:38,167 (server:353) INFO: Server: Starting evaluation at the end of round 74.
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2024-10-15 01:14:38,167 (server:359) INFO: ----------- Starting a new training round (Round #75) -------------
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2024-10-15 01:16:58,110 (client:354) INFO: {'Role': 'Client #6', 'Round': 75, 'Results_raw': {'train_loss': 14.726213, 'val_loss': 14.8869, 'test_loss': 16.378119}}
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2024-10-15 01:17:51,744 (client:354) INFO: {'Role': 'Client #7', 'Round': 75, 'Results_raw': {'train_loss': 14.966194, 'val_loss': 15.356186, 'test_loss': 16.190851}}
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2024-10-15 01:18:45,151 (client:354) INFO: {'Role': 'Client #4', 'Round': 75, 'Results_raw': {'train_loss': 14.728414, 'val_loss': 15.02658, 'test_loss': 16.3846}}
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2024-10-15 01:19:39,933 (client:354) INFO: {'Role': 'Client #9', 'Round': 75, 'Results_raw': {'train_loss': 17.381618, 'val_loss': 17.828657, 'test_loss': 18.804993}}
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2024-10-15 01:20:36,591 (client:354) INFO: {'Role': 'Client #8', 'Round': 75, 'Results_raw': {'train_loss': 12.965288, 'val_loss': 13.106092, 'test_loss': 13.766129}}
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2024-10-15 01:21:29,948 (client:354) INFO: {'Role': 'Client #10', 'Round': 75, 'Results_raw': {'train_loss': 14.630793, 'val_loss': 15.232696, 'test_loss': 16.77538}}
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2024-10-15 01:22:23,372 (client:354) INFO: {'Role': 'Client #2', 'Round': 75, 'Results_raw': {'train_loss': 8.433937, 'val_loss': 8.370012, 'test_loss': 8.938208}}
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2024-10-15 01:23:22,157 (client:354) INFO: {'Role': 'Client #3', 'Round': 75, 'Results_raw': {'train_loss': 9.772657, 'val_loss': 10.436013, 'test_loss': 11.910548}}
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2024-10-15 01:24:19,507 (client:354) INFO: {'Role': 'Client #5', 'Round': 75, 'Results_raw': {'train_loss': 15.532861, 'val_loss': 16.587037, 'test_loss': 18.798299}}
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2024-10-15 01:25:16,038 (client:354) INFO: {'Role': 'Client #1', 'Round': 75, 'Results_raw': {'train_loss': 10.365269, 'val_loss': 10.331394, 'test_loss': 11.442103}}
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2024-10-15 01:25:16,042 (server:615) INFO: {'Role': 'Server #', 'Round': 74, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.965329), 'test_loss': np.float64(98316.265323), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.963795), 'val_loss': np.float64(98308.311459)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.965329), 'test_loss': np.float64(98316.265323), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.963795), 'val_loss': np.float64(98308.311459)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.369237), 'test_avg_loss_bottom_decile': np.float64(15.870533), 'test_avg_loss_top_decile': np.float64(23.287772), 'test_avg_loss_min': np.float64(15.554972), 'test_avg_loss_max': np.float64(23.287772), 'test_avg_loss_bottom10%': np.float64(15.554972), 'test_avg_loss_top10%': np.float64(23.287772), 'test_avg_loss_cos1': np.float64(0.992287), 'test_avg_loss_entropy': np.float64(2.294805), 'test_loss_std': np.float64(12282.122113), 'test_loss_bottom_decile': np.float64(82272.844116), 'test_loss_top_decile': np.float64(120723.807678), 'test_loss_min': np.float64(80636.974365), 'test_loss_max': np.float64(120723.807678), 'test_loss_bottom10%': np.float64(80636.974365), 'test_loss_top10%': np.float64(120723.807678), 'test_loss_cos1': np.float64(0.992287), 'test_loss_entropy': np.float64(2.294805), 'val_avg_loss_std': np.float64(2.605336), 'val_avg_loss_bottom_decile': np.float64(15.562963), 'val_avg_loss_top_decile': np.float64(23.047183), 'val_avg_loss_min': np.float64(15.031409), 'val_avg_loss_max': np.float64(23.047183), 'val_avg_loss_bottom10%': np.float64(15.031409), 'val_avg_loss_top10%': np.float64(23.047183), 'val_avg_loss_cos1': np.float64(0.990694), 'val_avg_loss_entropy': np.float64(2.293091), 'val_loss_std': np.float64(13506.06424), 'val_loss_bottom_decile': np.float64(80678.399719), 'val_loss_top_decile': np.float64(119476.594299), 'val_loss_min': np.float64(77922.823029), 'val_loss_max': np.float64(119476.594299), 'val_loss_bottom10%': np.float64(77922.823029), 'val_loss_top10%': np.float64(119476.594299), 'val_loss_cos1': np.float64(0.990694), 'val_loss_entropy': np.float64(2.293091)}}
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2024-10-15 01:25:16,091 (server:353) INFO: Server: Starting evaluation at the end of round 75.
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2024-10-15 01:25:16,091 (server:359) INFO: ----------- Starting a new training round (Round #76) -------------
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2024-10-15 01:27:43,216 (client:354) INFO: {'Role': 'Client #5', 'Round': 76, 'Results_raw': {'train_loss': 15.507775, 'val_loss': 16.709762, 'test_loss': 19.076242}}
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2024-10-15 01:28:39,627 (client:354) INFO: {'Role': 'Client #2', 'Round': 76, 'Results_raw': {'train_loss': 8.517655, 'val_loss': 8.487634, 'test_loss': 8.927992}}
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2024-10-15 01:29:37,009 (client:354) INFO: {'Role': 'Client #9', 'Round': 76, 'Results_raw': {'train_loss': 17.414429, 'val_loss': 17.553892, 'test_loss': 18.428467}}
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2024-10-15 01:30:31,738 (client:354) INFO: {'Role': 'Client #3', 'Round': 76, 'Results_raw': {'train_loss': 9.78679, 'val_loss': 10.588464, 'test_loss': 12.119709}}
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2024-10-15 01:31:29,002 (client:354) INFO: {'Role': 'Client #4', 'Round': 76, 'Results_raw': {'train_loss': 14.705686, 'val_loss': 15.154435, 'test_loss': 16.512216}}
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2024-10-15 01:32:24,212 (client:354) INFO: {'Role': 'Client #10', 'Round': 76, 'Results_raw': {'train_loss': 14.611523, 'val_loss': 15.134842, 'test_loss': 16.833988}}
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2024-10-15 01:33:17,932 (client:354) INFO: {'Role': 'Client #1', 'Round': 76, 'Results_raw': {'train_loss': 10.434836, 'val_loss': 10.284004, 'test_loss': 11.440545}}
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2024-10-15 01:34:14,177 (client:354) INFO: {'Role': 'Client #6', 'Round': 76, 'Results_raw': {'train_loss': 14.721698, 'val_loss': 14.867635, 'test_loss': 16.15827}}
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2024-10-15 01:35:08,812 (client:354) INFO: {'Role': 'Client #7', 'Round': 76, 'Results_raw': {'train_loss': 14.919292, 'val_loss': 15.49919, 'test_loss': 16.460138}}
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2024-10-15 01:36:07,914 (client:354) INFO: {'Role': 'Client #8', 'Round': 76, 'Results_raw': {'train_loss': 12.943172, 'val_loss': 13.281113, 'test_loss': 13.911039}}
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2024-10-15 01:36:07,919 (server:615) INFO: {'Role': 'Server #', 'Round': 75, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.89853), 'test_loss': np.float64(97969.977835), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.889328), 'val_loss': np.float64(97922.278665)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.89853), 'test_loss': np.float64(97969.977835), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.889328), 'val_loss': np.float64(97922.278665)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.342245), 'test_avg_loss_bottom_decile': np.float64(15.8418), 'test_avg_loss_top_decile': np.float64(23.151392), 'test_avg_loss_min': np.float64(15.584439), 'test_avg_loss_max': np.float64(23.151392), 'test_avg_loss_bottom10%': np.float64(15.584439), 'test_avg_loss_top10%': np.float64(23.151392), 'test_avg_loss_cos1': np.float64(0.992407), 'test_avg_loss_entropy': np.float64(2.29493), 'test_loss_std': np.float64(12142.19639), 'test_loss_bottom_decile': np.float64(82123.890533), 'test_loss_top_decile': np.float64(120016.81604), 'test_loss_min': np.float64(80789.732086), 'test_loss_max': np.float64(120016.81604), 'test_loss_bottom10%': np.float64(80789.732086), 'test_loss_top10%': np.float64(120016.81604), 'test_loss_cos1': np.float64(0.992407), 'test_loss_entropy': np.float64(2.29493), 'val_avg_loss_std': np.float64(2.576181), 'val_avg_loss_bottom_decile': np.float64(15.530358), 'val_avg_loss_top_decile': np.float64(22.88997), 'val_avg_loss_min': np.float64(15.059024), 'val_avg_loss_max': np.float64(22.88997), 'val_avg_loss_bottom10%': np.float64(15.059024), 'val_avg_loss_top10%': np.float64(22.88997), 'val_avg_loss_cos1': np.float64(0.990828), 'val_avg_loss_entropy': np.float64(2.293233), 'val_loss_std': np.float64(13354.92409), 'val_loss_bottom_decile': np.float64(80509.37793), 'val_loss_top_decile': np.float64(118661.604553), 'val_loss_min': np.float64(78065.982422), 'val_loss_max': np.float64(118661.604553), 'val_loss_bottom10%': np.float64(78065.982422), 'val_loss_top10%': np.float64(118661.604553), 'val_loss_cos1': np.float64(0.990828), 'val_loss_entropy': np.float64(2.293233)}}
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2024-10-15 01:36:07,965 (server:353) INFO: Server: Starting evaluation at the end of round 76.
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2024-10-15 01:36:07,966 (server:359) INFO: ----------- Starting a new training round (Round #77) -------------
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2024-10-15 01:38:30,068 (client:354) INFO: {'Role': 'Client #10', 'Round': 77, 'Results_raw': {'train_loss': 14.620666, 'val_loss': 15.283916, 'test_loss': 16.902619}}
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2024-10-15 01:39:26,035 (client:354) INFO: {'Role': 'Client #6', 'Round': 77, 'Results_raw': {'train_loss': 14.714768, 'val_loss': 14.97363, 'test_loss': 16.468499}}
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2024-10-15 01:40:20,336 (client:354) INFO: {'Role': 'Client #3', 'Round': 77, 'Results_raw': {'train_loss': 9.835051, 'val_loss': 10.571957, 'test_loss': 12.094912}}
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2024-10-15 01:41:15,302 (client:354) INFO: {'Role': 'Client #4', 'Round': 77, 'Results_raw': {'train_loss': 14.692745, 'val_loss': 15.019102, 'test_loss': 16.004973}}
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2024-10-15 01:42:12,260 (client:354) INFO: {'Role': 'Client #5', 'Round': 77, 'Results_raw': {'train_loss': 15.50515, 'val_loss': 16.628282, 'test_loss': 19.014721}}
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2024-10-15 01:43:09,214 (client:354) INFO: {'Role': 'Client #8', 'Round': 77, 'Results_raw': {'train_loss': 12.926458, 'val_loss': 13.166506, 'test_loss': 13.948985}}
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2024-10-15 01:44:05,842 (client:354) INFO: {'Role': 'Client #7', 'Round': 77, 'Results_raw': {'train_loss': 14.9352, 'val_loss': 15.432263, 'test_loss': 16.070373}}
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2024-10-15 01:44:58,209 (client:354) INFO: {'Role': 'Client #9', 'Round': 77, 'Results_raw': {'train_loss': 17.387985, 'val_loss': 17.581805, 'test_loss': 18.480018}}
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2024-10-15 01:45:53,096 (client:354) INFO: {'Role': 'Client #1', 'Round': 77, 'Results_raw': {'train_loss': 10.369369, 'val_loss': 10.420057, 'test_loss': 11.607687}}
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2024-10-15 01:46:46,112 (client:354) INFO: {'Role': 'Client #2', 'Round': 77, 'Results_raw': {'train_loss': 8.459086, 'val_loss': 8.474708, 'test_loss': 9.127901}}
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2024-10-15 01:46:46,117 (server:615) INFO: {'Role': 'Server #', 'Round': 76, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.953106), 'test_loss': np.float64(98252.902451), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.935527), 'val_loss': np.float64(98161.773679)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.953106), 'test_loss': np.float64(98252.902451), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.935527), 'val_loss': np.float64(98161.773679)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.286734), 'test_avg_loss_bottom_decile': np.float64(15.939562), 'test_avg_loss_top_decile': np.float64(23.133626), 'test_avg_loss_min': np.float64(15.742295), 'test_avg_loss_max': np.float64(23.133626), 'test_avg_loss_bottom10%': np.float64(15.742295), 'test_avg_loss_top10%': np.float64(23.133626), 'test_avg_loss_cos1': np.float64(0.9928), 'test_avg_loss_entropy': np.float64(2.295333), 'test_loss_std': np.float64(11854.429849), 'test_loss_bottom_decile': np.float64(82630.687286), 'test_loss_top_decile': np.float64(119924.715698), 'test_loss_min': np.float64(81608.0578), 'test_loss_max': np.float64(119924.715698), 'test_loss_bottom10%': np.float64(81608.0578), 'test_loss_top10%': np.float64(119924.715698), 'test_loss_cos1': np.float64(0.9928), 'test_loss_entropy': np.float64(2.295333), 'val_avg_loss_std': np.float64(2.512841), 'val_avg_loss_bottom_decile': np.float64(15.643257), 'val_avg_loss_top_decile': np.float64(22.877569), 'val_avg_loss_min': np.float64(15.218432), 'val_avg_loss_max': np.float64(22.877569), 'val_avg_loss_bottom10%': np.float64(15.218432), 'val_avg_loss_top10%': np.float64(22.877569), 'val_avg_loss_cos1': np.float64(0.991309), 'val_avg_loss_entropy': np.float64(2.293738), 'val_loss_std': np.float64(13026.565799), 'val_loss_bottom_decile': np.float64(81094.643555), 'val_loss_top_decile': np.float64(118597.316528), 'val_loss_min': np.float64(78892.350159), 'val_loss_max': np.float64(118597.316528), 'val_loss_bottom10%': np.float64(78892.350159), 'val_loss_top10%': np.float64(118597.316528), 'val_loss_cos1': np.float64(0.991309), 'val_loss_entropy': np.float64(2.293738)}}
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2024-10-15 01:46:46,162 (server:353) INFO: Server: Starting evaluation at the end of round 77.
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2024-10-15 01:46:46,163 (server:359) INFO: ----------- Starting a new training round (Round #78) -------------
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2024-10-15 01:49:12,174 (client:354) INFO: {'Role': 'Client #1', 'Round': 78, 'Results_raw': {'train_loss': 10.37314, 'val_loss': 10.416546, 'test_loss': 11.608801}}
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2024-10-15 01:50:07,101 (client:354) INFO: {'Role': 'Client #3', 'Round': 78, 'Results_raw': {'train_loss': 9.789441, 'val_loss': 10.514764, 'test_loss': 11.939502}}
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2024-10-15 01:51:01,592 (client:354) INFO: {'Role': 'Client #5', 'Round': 78, 'Results_raw': {'train_loss': 15.504795, 'val_loss': 16.529724, 'test_loss': 18.626213}}
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2024-10-15 01:51:58,530 (client:354) INFO: {'Role': 'Client #4', 'Round': 78, 'Results_raw': {'train_loss': 14.654198, 'val_loss': 15.078275, 'test_loss': 16.379462}}
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2024-10-15 01:52:53,515 (client:354) INFO: {'Role': 'Client #8', 'Round': 78, 'Results_raw': {'train_loss': 12.968408, 'val_loss': 13.167204, 'test_loss': 13.964529}}
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2024-10-15 01:53:49,895 (client:354) INFO: {'Role': 'Client #9', 'Round': 78, 'Results_raw': {'train_loss': 17.378517, 'val_loss': 17.669416, 'test_loss': 18.61645}}
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2024-10-15 01:54:49,269 (client:354) INFO: {'Role': 'Client #2', 'Round': 78, 'Results_raw': {'train_loss': 8.432968, 'val_loss': 8.462153, 'test_loss': 9.135549}}
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2024-10-15 01:55:46,554 (client:354) INFO: {'Role': 'Client #6', 'Round': 78, 'Results_raw': {'train_loss': 14.692463, 'val_loss': 14.809276, 'test_loss': 16.10141}}
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2024-10-15 01:56:40,974 (client:354) INFO: {'Role': 'Client #10', 'Round': 78, 'Results_raw': {'train_loss': 14.618217, 'val_loss': 15.227735, 'test_loss': 17.023749}}
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2024-10-15 01:57:37,200 (client:354) INFO: {'Role': 'Client #7', 'Round': 78, 'Results_raw': {'train_loss': 14.94939, 'val_loss': 15.306921, 'test_loss': 16.048709}}
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2024-10-15 01:57:37,205 (server:615) INFO: {'Role': 'Server #', 'Round': 77, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.927419), 'test_loss': np.float64(98119.739923), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.906477), 'val_loss': np.float64(98011.177582)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.927419), 'test_loss': np.float64(98119.739923), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.906477), 'val_loss': np.float64(98011.177582)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.303372), 'test_avg_loss_bottom_decile': np.float64(15.893178), 'test_avg_loss_top_decile': np.float64(23.132468), 'test_avg_loss_min': np.float64(15.63807), 'test_avg_loss_max': np.float64(23.132468), 'test_avg_loss_bottom10%': np.float64(15.63807), 'test_avg_loss_top10%': np.float64(23.132468), 'test_avg_loss_cos1': np.float64(0.992676), 'test_avg_loss_entropy': np.float64(2.295206), 'test_loss_std': np.float64(11940.68249), 'test_loss_bottom_decile': np.float64(82390.236725), 'test_loss_top_decile': np.float64(119918.715393), 'test_loss_min': np.float64(81067.755951), 'test_loss_max': np.float64(119918.715393), 'test_loss_bottom10%': np.float64(81067.755951), 'test_loss_top10%': np.float64(119918.715393), 'test_loss_cos1': np.float64(0.992676), 'test_loss_entropy': np.float64(2.295206), 'val_avg_loss_std': np.float64(2.53523), 'val_avg_loss_bottom_decile': np.float64(15.566125), 'val_avg_loss_top_decile': np.float64(22.875612), 'val_avg_loss_min': np.float64(15.107217), 'val_avg_loss_max': np.float64(22.875612), 'val_avg_loss_bottom10%': np.float64(15.107217), 'val_avg_loss_top10%': np.float64(22.875612), 'val_avg_loss_cos1': np.float64(0.991129), 'val_avg_loss_entropy': np.float64(2.29355), 'val_loss_std': np.float64(13142.633474), 'val_loss_bottom_decile': np.float64(80694.789856), 'val_loss_top_decile': np.float64(118587.171143), 'val_loss_min': np.float64(78315.811554), 'val_loss_max': np.float64(118587.171143), 'val_loss_bottom10%': np.float64(78315.811554), 'val_loss_top10%': np.float64(118587.171143), 'val_loss_cos1': np.float64(0.991129), 'val_loss_entropy': np.float64(2.29355)}}
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2024-10-15 01:57:37,251 (server:353) INFO: Server: Starting evaluation at the end of round 78.
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2024-10-15 01:57:37,252 (server:359) INFO: ----------- Starting a new training round (Round #79) -------------
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2024-10-15 01:59:59,686 (client:354) INFO: {'Role': 'Client #10', 'Round': 79, 'Results_raw': {'train_loss': 14.608455, 'val_loss': 15.314582, 'test_loss': 17.093684}}
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2024-10-15 02:00:54,441 (client:354) INFO: {'Role': 'Client #1', 'Round': 79, 'Results_raw': {'train_loss': 10.378053, 'val_loss': 10.383783, 'test_loss': 11.556445}}
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2024-10-15 02:01:51,383 (client:354) INFO: {'Role': 'Client #4', 'Round': 79, 'Results_raw': {'train_loss': 14.70229, 'val_loss': 15.013861, 'test_loss': 16.214404}}
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2024-10-15 02:02:45,874 (client:354) INFO: {'Role': 'Client #9', 'Round': 79, 'Results_raw': {'train_loss': 17.426764, 'val_loss': 17.612196, 'test_loss': 18.578039}}
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2024-10-15 02:03:40,153 (client:354) INFO: {'Role': 'Client #5', 'Round': 79, 'Results_raw': {'train_loss': 15.501551, 'val_loss': 16.504755, 'test_loss': 18.65168}}
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2024-10-15 02:04:36,186 (client:354) INFO: {'Role': 'Client #7', 'Round': 79, 'Results_raw': {'train_loss': 14.937512, 'val_loss': 15.329917, 'test_loss': 15.937185}}
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2024-10-15 02:05:33,830 (client:354) INFO: {'Role': 'Client #3', 'Round': 79, 'Results_raw': {'train_loss': 9.774711, 'val_loss': 10.582497, 'test_loss': 12.049383}}
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2024-10-15 02:06:28,230 (client:354) INFO: {'Role': 'Client #6', 'Round': 79, 'Results_raw': {'train_loss': 14.6743, 'val_loss': 14.889029, 'test_loss': 16.622653}}
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2024-10-15 02:07:25,606 (client:354) INFO: {'Role': 'Client #8', 'Round': 79, 'Results_raw': {'train_loss': 12.936725, 'val_loss': 13.120079, 'test_loss': 13.692181}}
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2024-10-15 02:08:22,665 (client:354) INFO: {'Role': 'Client #2', 'Round': 79, 'Results_raw': {'train_loss': 8.453051, 'val_loss': 8.388762, 'test_loss': 8.993373}}
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2024-10-15 02:08:22,670 (server:615) INFO: {'Role': 'Server #', 'Round': 78, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.924372), 'test_loss': np.float64(98103.946481), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.906803), 'val_loss': np.float64(98012.866476)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.924372), 'test_loss': np.float64(98103.946481), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.906803), 'val_loss': np.float64(98012.866476)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.307506), 'test_avg_loss_bottom_decile': np.float64(15.857412), 'test_avg_loss_top_decile': np.float64(23.122132), 'test_avg_loss_min': np.float64(15.61749), 'test_avg_loss_max': np.float64(23.122132), 'test_avg_loss_bottom10%': np.float64(15.61749), 'test_avg_loss_top10%': np.float64(23.122132), 'test_avg_loss_cos1': np.float64(0.992648), 'test_avg_loss_entropy': np.float64(2.295173), 'test_loss_std': np.float64(11962.112514), 'test_loss_bottom_decile': np.float64(82204.821564), 'test_loss_top_decile': np.float64(119865.131714), 'test_loss_min': np.float64(80961.066864), 'test_loss_max': np.float64(119865.131714), 'test_loss_bottom10%': np.float64(80961.066864), 'test_loss_top10%': np.float64(119865.131714), 'test_loss_cos1': np.float64(0.992648), 'test_loss_entropy': np.float64(2.295173), 'val_avg_loss_std': np.float64(2.534353), 'val_avg_loss_bottom_decile': np.float64(15.522414), 'val_avg_loss_top_decile': np.float64(22.859808), 'val_avg_loss_min': np.float64(15.100052), 'val_avg_loss_max': np.float64(22.859808), 'val_avg_loss_bottom10%': np.float64(15.100052), 'val_avg_loss_top10%': np.float64(22.859808), 'val_avg_loss_cos1': np.float64(0.991135), 'val_avg_loss_entropy': np.float64(2.293549), 'val_loss_std': np.float64(13138.087763), 'val_loss_bottom_decile': np.float64(80468.193634), 'val_loss_top_decile': np.float64(118505.244446), 'val_loss_min': np.float64(78278.667908), 'val_loss_max': np.float64(118505.244446), 'val_loss_bottom10%': np.float64(78278.667908), 'val_loss_top10%': np.float64(118505.244446), 'val_loss_cos1': np.float64(0.991135), 'val_loss_entropy': np.float64(2.293549)}}
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2024-10-15 02:08:22,716 (server:353) INFO: Server: Starting evaluation at the end of round 79.
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2024-10-15 02:08:22,717 (server:359) INFO: ----------- Starting a new training round (Round #80) -------------
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2024-10-15 02:10:48,580 (client:354) INFO: {'Role': 'Client #5', 'Round': 80, 'Results_raw': {'train_loss': 15.467564, 'val_loss': 16.661173, 'test_loss': 18.92587}}
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2024-10-15 02:11:42,047 (client:354) INFO: {'Role': 'Client #1', 'Round': 80, 'Results_raw': {'train_loss': 10.385221, 'val_loss': 10.463297, 'test_loss': 11.622311}}
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2024-10-15 02:12:35,075 (client:354) INFO: {'Role': 'Client #4', 'Round': 80, 'Results_raw': {'train_loss': 14.671696, 'val_loss': 15.059415, 'test_loss': 16.235644}}
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2024-10-15 02:13:32,700 (client:354) INFO: {'Role': 'Client #6', 'Round': 80, 'Results_raw': {'train_loss': 14.66334, 'val_loss': 14.806745, 'test_loss': 16.394647}}
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2024-10-15 02:14:26,627 (client:354) INFO: {'Role': 'Client #2', 'Round': 80, 'Results_raw': {'train_loss': 8.454036, 'val_loss': 8.484006, 'test_loss': 9.247191}}
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2024-10-15 02:15:22,246 (client:354) INFO: {'Role': 'Client #10', 'Round': 80, 'Results_raw': {'train_loss': 14.601021, 'val_loss': 15.227586, 'test_loss': 16.811109}}
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2024-10-15 02:16:21,910 (client:354) INFO: {'Role': 'Client #8', 'Round': 80, 'Results_raw': {'train_loss': 12.915129, 'val_loss': 13.314029, 'test_loss': 14.009837}}
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2024-10-15 02:17:18,216 (client:354) INFO: {'Role': 'Client #7', 'Round': 80, 'Results_raw': {'train_loss': 14.95495, 'val_loss': 15.231243, 'test_loss': 15.985554}}
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2024-10-15 02:18:15,216 (client:354) INFO: {'Role': 'Client #3', 'Round': 80, 'Results_raw': {'train_loss': 9.763464, 'val_loss': 10.502133, 'test_loss': 12.034844}}
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2024-10-15 02:19:11,959 (client:354) INFO: {'Role': 'Client #9', 'Round': 80, 'Results_raw': {'train_loss': 17.383349, 'val_loss': 17.596205, 'test_loss': 18.426574}}
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2024-10-15 02:19:11,963 (server:615) INFO: {'Role': 'Server #', 'Round': 79, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.923837), 'test_loss': np.float64(98101.169354), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.910676), 'val_loss': np.float64(98032.944452)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.923837), 'test_loss': np.float64(98101.169354), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.910676), 'val_loss': np.float64(98032.944452)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.343432), 'test_avg_loss_bottom_decile': np.float64(15.799101), 'test_avg_loss_top_decile': np.float64(23.231484), 'test_avg_loss_min': np.float64(15.63614), 'test_avg_loss_max': np.float64(23.231484), 'test_avg_loss_bottom10%': np.float64(15.63614), 'test_avg_loss_top10%': np.float64(23.231484), 'test_avg_loss_cos1': np.float64(0.99242), 'test_avg_loss_entropy': np.float64(2.294946), 'test_loss_std': np.float64(12148.349476), 'test_loss_bottom_decile': np.float64(81902.537964), 'test_loss_top_decile': np.float64(120432.015503), 'test_loss_min': np.float64(81057.749786), 'test_loss_max': np.float64(120432.015503), 'test_loss_bottom10%': np.float64(81057.749786), 'test_loss_top10%': np.float64(120432.015503), 'test_loss_cos1': np.float64(0.99242), 'test_loss_entropy': np.float64(2.294946), 'val_avg_loss_std': np.float64(2.57326), 'val_avg_loss_bottom_decile': np.float64(15.481188), 'val_avg_loss_top_decile': np.float64(22.964147), 'val_avg_loss_min': np.float64(15.108367), 'val_avg_loss_max': np.float64(22.964147), 'val_avg_loss_bottom10%': np.float64(15.108367), 'val_avg_loss_top10%': np.float64(22.964147), 'val_avg_loss_cos1': np.float64(0.990868), 'val_avg_loss_entropy': np.float64(2.293277), 'val_loss_std': np.float64(13339.780399), 'val_loss_bottom_decile': np.float64(80254.479218), 'val_loss_top_decile': np.float64(119046.137634), 'val_loss_min': np.float64(78321.773041), 'val_loss_max': np.float64(119046.137634), 'val_loss_bottom10%': np.float64(78321.773041), 'val_loss_top10%': np.float64(119046.137634), 'val_loss_cos1': np.float64(0.990868), 'val_loss_entropy': np.float64(2.293277)}}
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2024-10-15 02:19:11,997 (server:353) INFO: Server: Starting evaluation at the end of round 80.
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2024-10-15 02:19:11,997 (server:359) INFO: ----------- Starting a new training round (Round #81) -------------
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2024-10-15 02:21:34,505 (client:354) INFO: {'Role': 'Client #9', 'Round': 81, 'Results_raw': {'train_loss': 17.38402, 'val_loss': 17.658073, 'test_loss': 18.557089}}
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2024-10-15 02:22:28,893 (client:354) INFO: {'Role': 'Client #6', 'Round': 81, 'Results_raw': {'train_loss': 14.703573, 'val_loss': 14.834774, 'test_loss': 16.224284}}
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2024-10-15 02:23:25,195 (client:354) INFO: {'Role': 'Client #2', 'Round': 81, 'Results_raw': {'train_loss': 8.412544, 'val_loss': 8.419118, 'test_loss': 9.067943}}
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2024-10-15 02:24:21,255 (client:354) INFO: {'Role': 'Client #8', 'Round': 81, 'Results_raw': {'train_loss': 12.913235, 'val_loss': 13.100602, 'test_loss': 13.816388}}
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2024-10-15 02:25:16,701 (client:354) INFO: {'Role': 'Client #3', 'Round': 81, 'Results_raw': {'train_loss': 9.7594, 'val_loss': 10.520088, 'test_loss': 11.856759}}
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2024-10-15 02:26:24,111 (client:354) INFO: {'Role': 'Client #5', 'Round': 81, 'Results_raw': {'train_loss': 15.492357, 'val_loss': 16.638718, 'test_loss': 18.94093}}
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2024-10-15 02:27:36,124 (client:354) INFO: {'Role': 'Client #10', 'Round': 81, 'Results_raw': {'train_loss': 14.595225, 'val_loss': 15.2556, 'test_loss': 16.918165}}
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2024-10-15 02:28:30,939 (client:354) INFO: {'Role': 'Client #1', 'Round': 81, 'Results_raw': {'train_loss': 10.34512, 'val_loss': 10.472877, 'test_loss': 11.530264}}
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2024-10-15 02:29:27,284 (client:354) INFO: {'Role': 'Client #4', 'Round': 81, 'Results_raw': {'train_loss': 14.708725, 'val_loss': 15.095598, 'test_loss': 16.678188}}
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2024-10-15 02:30:23,221 (client:354) INFO: {'Role': 'Client #7', 'Round': 81, 'Results_raw': {'train_loss': 14.917179, 'val_loss': 15.305204, 'test_loss': 16.139471}}
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2024-10-15 02:30:23,225 (server:615) INFO: {'Role': 'Server #', 'Round': 80, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.962828), 'test_loss': np.float64(98303.300891), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.9366), 'val_loss': np.float64(98167.333258)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.962828), 'test_loss': np.float64(98303.300891), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.9366), 'val_loss': np.float64(98167.333258)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.25206), 'test_avg_loss_bottom_decile': np.float64(16.033451), 'test_avg_loss_top_decile': np.float64(23.067381), 'test_avg_loss_min': np.float64(15.721974), 'test_avg_loss_max': np.float64(23.067381), 'test_avg_loss_bottom10%': np.float64(15.721974), 'test_avg_loss_top10%': np.float64(23.067381), 'test_avg_loss_cos1': np.float64(0.993022), 'test_avg_loss_entropy': np.float64(2.295558), 'test_loss_std': np.float64(11674.67791), 'test_loss_bottom_decile': np.float64(83117.409546), 'test_loss_top_decile': np.float64(119581.303955), 'test_loss_min': np.float64(81502.713165), 'test_loss_max': np.float64(119581.303955), 'test_loss_bottom10%': np.float64(81502.713165), 'test_loss_top10%': np.float64(119581.303955), 'test_loss_cos1': np.float64(0.993022), 'test_loss_entropy': np.float64(2.295558), 'val_avg_loss_std': np.float64(2.486707), 'val_avg_loss_bottom_decile': np.float64(15.680992), 'val_avg_loss_top_decile': np.float64(22.809582), 'val_avg_loss_min': np.float64(15.185371), 'val_avg_loss_max': np.float64(22.809582), 'val_avg_loss_bottom10%': np.float64(15.185371), 'val_avg_loss_top10%': np.float64(22.809582), 'val_avg_loss_cos1': np.float64(0.991488), 'val_avg_loss_entropy': np.float64(2.29392), 'val_loss_std': np.float64(12891.091259), 'val_loss_bottom_decile': np.float64(81290.264038), 'val_loss_top_decile': np.float64(118244.871338), 'val_loss_min': np.float64(78720.965515), 'val_loss_max': np.float64(118244.871338), 'val_loss_bottom10%': np.float64(78720.965515), 'val_loss_top10%': np.float64(118244.871338), 'val_loss_cos1': np.float64(0.991488), 'val_loss_entropy': np.float64(2.29392)}}
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2024-10-15 02:30:23,272 (server:353) INFO: Server: Starting evaluation at the end of round 81.
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2024-10-15 02:30:23,273 (server:359) INFO: ----------- Starting a new training round (Round #82) -------------
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2024-10-15 02:32:44,390 (client:354) INFO: {'Role': 'Client #10', 'Round': 82, 'Results_raw': {'train_loss': 14.588128, 'val_loss': 15.263244, 'test_loss': 16.854063}}
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2024-10-15 02:33:43,106 (client:354) INFO: {'Role': 'Client #1', 'Round': 82, 'Results_raw': {'train_loss': 10.349609, 'val_loss': 10.406585, 'test_loss': 11.500515}}
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2024-10-15 02:34:36,627 (client:354) INFO: {'Role': 'Client #7', 'Round': 82, 'Results_raw': {'train_loss': 14.919491, 'val_loss': 15.344319, 'test_loss': 15.98402}}
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2024-10-15 02:35:39,443 (client:354) INFO: {'Role': 'Client #3', 'Round': 82, 'Results_raw': {'train_loss': 9.755416, 'val_loss': 10.474428, 'test_loss': 11.968081}}
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2024-10-15 02:36:31,362 (client:354) INFO: {'Role': 'Client #6', 'Round': 82, 'Results_raw': {'train_loss': 14.680974, 'val_loss': 14.828634, 'test_loss': 16.391719}}
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2024-10-15 02:37:21,777 (client:354) INFO: {'Role': 'Client #4', 'Round': 82, 'Results_raw': {'train_loss': 14.644139, 'val_loss': 14.979692, 'test_loss': 16.249048}}
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2024-10-15 02:38:13,218 (client:354) INFO: {'Role': 'Client #8', 'Round': 82, 'Results_raw': {'train_loss': 12.927685, 'val_loss': 13.166147, 'test_loss': 13.988118}}
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2024-10-15 02:39:08,633 (client:354) INFO: {'Role': 'Client #5', 'Round': 82, 'Results_raw': {'train_loss': 15.498526, 'val_loss': 16.663701, 'test_loss': 18.983022}}
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2024-10-15 02:40:02,214 (client:354) INFO: {'Role': 'Client #9', 'Round': 82, 'Results_raw': {'train_loss': 17.358124, 'val_loss': 17.50342, 'test_loss': 18.253409}}
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2024-10-15 02:40:56,128 (client:354) INFO: {'Role': 'Client #2', 'Round': 82, 'Results_raw': {'train_loss': 8.48657, 'val_loss': 8.413143, 'test_loss': 9.12278}}
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2024-10-15 02:40:56,133 (server:615) INFO: {'Role': 'Server #', 'Round': 81, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.837227), 'test_loss': np.float64(97652.185556), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.832595), 'val_loss': np.float64(97628.174677)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.837227), 'test_loss': np.float64(97652.185556), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.832595), 'val_loss': np.float64(97628.174677)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.358952), 'test_avg_loss_bottom_decile': np.float64(15.691514), 'test_avg_loss_top_decile': np.float64(23.13329), 'test_avg_loss_min': np.float64(15.510306), 'test_avg_loss_max': np.float64(23.13329), 'test_avg_loss_bottom10%': np.float64(15.510306), 'test_avg_loss_top10%': np.float64(23.13329), 'test_avg_loss_cos1': np.float64(0.99225), 'test_avg_loss_entropy': np.float64(2.294769), 'test_loss_std': np.float64(12228.805627), 'test_loss_bottom_decile': np.float64(81344.807251), 'test_loss_top_decile': np.float64(119922.972961), 'test_loss_min': np.float64(80405.424805), 'test_loss_max': np.float64(119922.972961), 'test_loss_bottom10%': np.float64(80405.424805), 'test_loss_top10%': np.float64(119922.972961), 'test_loss_cos1': np.float64(0.99225), 'test_loss_entropy': np.float64(2.294769), 'val_avg_loss_std': np.float64(2.585318), 'val_avg_loss_bottom_decile': np.float64(15.393198), 'val_avg_loss_top_decile': np.float64(22.866264), 'val_avg_loss_min': np.float64(14.986181), 'val_avg_loss_max': np.float64(22.866264), 'val_avg_loss_bottom10%': np.float64(14.986181), 'val_avg_loss_top10%': np.float64(22.866264), 'val_avg_loss_cos1': np.float64(0.990708), 'val_avg_loss_entropy': np.float64(2.293107), 'val_loss_std': np.float64(13402.286068), 'val_loss_bottom_decile': np.float64(79798.337128), 'val_loss_top_decile': np.float64(118538.710205), 'val_loss_min': np.float64(77688.360382), 'val_loss_max': np.float64(118538.710205), 'val_loss_bottom10%': np.float64(77688.360382), 'val_loss_top10%': np.float64(118538.710205), 'val_loss_cos1': np.float64(0.990708), 'val_loss_entropy': np.float64(2.293107)}}
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2024-10-15 02:40:56,183 (server:353) INFO: Server: Starting evaluation at the end of round 82.
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2024-10-15 02:40:56,183 (server:359) INFO: ----------- Starting a new training round (Round #83) -------------
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2024-10-15 02:43:14,106 (client:354) INFO: {'Role': 'Client #1', 'Round': 83, 'Results_raw': {'train_loss': 10.329643, 'val_loss': 10.350022, 'test_loss': 11.415044}}
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2024-10-15 02:44:06,904 (client:354) INFO: {'Role': 'Client #9', 'Round': 83, 'Results_raw': {'train_loss': 17.386692, 'val_loss': 17.872348, 'test_loss': 18.71657}}
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2024-10-15 02:45:04,476 (client:354) INFO: {'Role': 'Client #7', 'Round': 83, 'Results_raw': {'train_loss': 14.932438, 'val_loss': 15.366448, 'test_loss': 16.327454}}
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2024-10-15 02:46:04,208 (client:354) INFO: {'Role': 'Client #2', 'Round': 83, 'Results_raw': {'train_loss': 8.478936, 'val_loss': 8.447373, 'test_loss': 9.142608}}
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2024-10-15 02:46:57,621 (client:354) INFO: {'Role': 'Client #4', 'Round': 83, 'Results_raw': {'train_loss': 14.724598, 'val_loss': 14.902492, 'test_loss': 16.067925}}
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2024-10-15 02:47:52,209 (client:354) INFO: {'Role': 'Client #3', 'Round': 83, 'Results_raw': {'train_loss': 9.737118, 'val_loss': 10.518551, 'test_loss': 11.945405}}
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2024-10-15 02:48:47,873 (client:354) INFO: {'Role': 'Client #6', 'Round': 83, 'Results_raw': {'train_loss': 14.664606, 'val_loss': 14.80782, 'test_loss': 16.484878}}
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2024-10-15 02:49:42,371 (client:354) INFO: {'Role': 'Client #10', 'Round': 83, 'Results_raw': {'train_loss': 14.566007, 'val_loss': 15.296779, 'test_loss': 17.092722}}
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2024-10-15 02:50:35,847 (client:354) INFO: {'Role': 'Client #5', 'Round': 83, 'Results_raw': {'train_loss': 15.4809, 'val_loss': 16.57297, 'test_loss': 18.853257}}
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2024-10-15 02:51:32,345 (client:354) INFO: {'Role': 'Client #8', 'Round': 83, 'Results_raw': {'train_loss': 12.917644, 'val_loss': 13.110198, 'test_loss': 13.893377}}
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2024-10-15 02:51:32,349 (server:615) INFO: {'Role': 'Server #', 'Round': 82, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.882062), 'test_loss': np.float64(97884.610132), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.862618), 'val_loss': np.float64(97783.81246)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.882062), 'test_loss': np.float64(97884.610132), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.862618), 'val_loss': np.float64(97783.81246)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.344027), 'test_avg_loss_bottom_decile': np.float64(15.857361), 'test_avg_loss_top_decile': np.float64(23.203513), 'test_avg_loss_min': np.float64(15.547247), 'test_avg_loss_max': np.float64(23.203513), 'test_avg_loss_bottom10%': np.float64(15.547247), 'test_avg_loss_top10%': np.float64(23.203513), 'test_avg_loss_cos1': np.float64(0.992383), 'test_avg_loss_entropy': np.float64(2.294916), 'test_loss_std': np.float64(12151.433408), 'test_loss_bottom_decile': np.float64(82204.561249), 'test_loss_top_decile': np.float64(120287.012329), 'test_loss_min': np.float64(80596.926605), 'test_loss_max': np.float64(120287.012329), 'test_loss_bottom10%': np.float64(80596.926605), 'test_loss_top10%': np.float64(120287.012329), 'test_loss_cos1': np.float64(0.992383), 'test_loss_entropy': np.float64(2.294916), 'val_avg_loss_std': np.float64(2.582931), 'val_avg_loss_bottom_decile': np.float64(15.498051), 'val_avg_loss_top_decile': np.float64(22.925187), 'val_avg_loss_min': np.float64(15.000168), 'val_avg_loss_max': np.float64(22.925187), 'val_avg_loss_bottom10%': np.float64(15.000168), 'val_avg_loss_top10%': np.float64(22.925187), 'val_avg_loss_cos1': np.float64(0.990754), 'val_avg_loss_entropy': np.float64(2.293164), 'val_loss_std': np.float64(13389.916571), 'val_loss_bottom_decile': np.float64(80341.898315), 'val_loss_top_decile': np.float64(118844.168884), 'val_loss_min': np.float64(77760.869354), 'val_loss_max': np.float64(118844.168884), 'val_loss_bottom10%': np.float64(77760.869354), 'val_loss_top10%': np.float64(118844.168884), 'val_loss_cos1': np.float64(0.990754), 'val_loss_entropy': np.float64(2.293164)}}
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2024-10-15 02:51:32,394 (server:353) INFO: Server: Starting evaluation at the end of round 83.
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2024-10-15 02:51:32,395 (server:359) INFO: ----------- Starting a new training round (Round #84) -------------
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2024-10-15 02:53:52,169 (client:354) INFO: {'Role': 'Client #2', 'Round': 84, 'Results_raw': {'train_loss': 8.467857, 'val_loss': 8.464527, 'test_loss': 9.200294}}
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2024-10-15 02:54:46,610 (client:354) INFO: {'Role': 'Client #4', 'Round': 84, 'Results_raw': {'train_loss': 14.65617, 'val_loss': 14.956375, 'test_loss': 16.076836}}
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2024-10-15 02:55:41,556 (client:354) INFO: {'Role': 'Client #10', 'Round': 84, 'Results_raw': {'train_loss': 14.593895, 'val_loss': 15.259644, 'test_loss': 17.001857}}
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2024-10-15 02:56:36,314 (client:354) INFO: {'Role': 'Client #9', 'Round': 84, 'Results_raw': {'train_loss': 17.369491, 'val_loss': 17.565846, 'test_loss': 18.413667}}
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2024-10-15 02:57:29,597 (client:354) INFO: {'Role': 'Client #7', 'Round': 84, 'Results_raw': {'train_loss': 14.906569, 'val_loss': 15.34357, 'test_loss': 16.210789}}
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2024-10-15 02:58:23,198 (client:354) INFO: {'Role': 'Client #6', 'Round': 84, 'Results_raw': {'train_loss': 14.671995, 'val_loss': 14.86583, 'test_loss': 16.3931}}
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2024-10-15 02:59:14,612 (client:354) INFO: {'Role': 'Client #3', 'Round': 84, 'Results_raw': {'train_loss': 9.759123, 'val_loss': 10.474098, 'test_loss': 11.924601}}
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2024-10-15 03:00:09,431 (client:354) INFO: {'Role': 'Client #8', 'Round': 84, 'Results_raw': {'train_loss': 12.906459, 'val_loss': 13.068875, 'test_loss': 13.908556}}
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2024-10-15 03:01:04,328 (client:354) INFO: {'Role': 'Client #5', 'Round': 84, 'Results_raw': {'train_loss': 15.45015, 'val_loss': 16.497395, 'test_loss': 18.803495}}
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2024-10-15 03:01:59,611 (client:354) INFO: {'Role': 'Client #1', 'Round': 84, 'Results_raw': {'train_loss': 10.374466, 'val_loss': 10.372165, 'test_loss': 11.534011}}
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2024-10-15 03:01:59,615 (server:615) INFO: {'Role': 'Server #', 'Round': 83, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.792887), 'test_loss': np.float64(97422.32381), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.768481), 'val_loss': np.float64(97295.805673)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.792887), 'test_loss': np.float64(97422.32381), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.768481), 'val_loss': np.float64(97295.805673)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.295701), 'test_avg_loss_bottom_decile': np.float64(15.793503), 'test_avg_loss_top_decile': np.float64(22.919568), 'test_avg_loss_min': np.float64(15.547581), 'test_avg_loss_max': np.float64(22.919568), 'test_avg_loss_bottom10%': np.float64(15.547581), 'test_avg_loss_top10%': np.float64(22.919568), 'test_avg_loss_cos1': np.float64(0.992621), 'test_avg_loss_entropy': np.float64(2.295148), 'test_loss_std': np.float64(11900.915053), 'test_loss_bottom_decile': np.float64(81873.517822), 'test_loss_top_decile': np.float64(118815.040771), 'test_loss_min': np.float64(80598.661224), 'test_loss_max': np.float64(118815.040771), 'test_loss_bottom10%': np.float64(80598.661224), 'test_loss_top10%': np.float64(118815.040771), 'test_loss_cos1': np.float64(0.992621), 'test_loss_entropy': np.float64(2.295148), 'val_avg_loss_std': np.float64(2.526255), 'val_avg_loss_bottom_decile': np.float64(15.456228), 'val_avg_loss_top_decile': np.float64(22.645115), 'val_avg_loss_min': np.float64(15.029447), 'val_avg_loss_max': np.float64(22.645115), 'val_avg_loss_bottom10%': np.float64(15.029447), 'val_avg_loss_top10%': np.float64(22.645115), 'val_avg_loss_cos1': np.float64(0.991063), 'val_avg_loss_entropy': np.float64(2.293478), 'val_loss_std': np.float64(13096.106788), 'val_loss_bottom_decile': np.float64(80125.086426), 'val_loss_top_decile': np.float64(117392.276489), 'val_loss_min': np.float64(77912.651154), 'val_loss_max': np.float64(117392.276489), 'val_loss_bottom10%': np.float64(77912.651154), 'val_loss_top10%': np.float64(117392.276489), 'val_loss_cos1': np.float64(0.991063), 'val_loss_entropy': np.float64(2.293478)}}
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2024-10-15 03:01:59,656 (server:353) INFO: Server: Starting evaluation at the end of round 84.
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2024-10-15 03:01:59,657 (server:359) INFO: ----------- Starting a new training round (Round #85) -------------
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2024-10-15 03:04:18,554 (client:354) INFO: {'Role': 'Client #10', 'Round': 85, 'Results_raw': {'train_loss': 14.617064, 'val_loss': 15.327667, 'test_loss': 16.979687}}
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2024-10-15 03:05:16,811 (client:354) INFO: {'Role': 'Client #6', 'Round': 85, 'Results_raw': {'train_loss': 14.656826, 'val_loss': 14.720589, 'test_loss': 16.121325}}
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2024-10-15 03:06:09,852 (client:354) INFO: {'Role': 'Client #2', 'Round': 85, 'Results_raw': {'train_loss': 8.431288, 'val_loss': 8.427678, 'test_loss': 9.006943}}
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2024-10-15 03:07:02,840 (client:354) INFO: {'Role': 'Client #9', 'Round': 85, 'Results_raw': {'train_loss': 17.345584, 'val_loss': 17.588925, 'test_loss': 18.471659}}
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2024-10-15 03:07:59,255 (client:354) INFO: {'Role': 'Client #7', 'Round': 85, 'Results_raw': {'train_loss': 14.906833, 'val_loss': 15.38291, 'test_loss': 16.243243}}
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2024-10-15 03:08:54,578 (client:354) INFO: {'Role': 'Client #4', 'Round': 85, 'Results_raw': {'train_loss': 14.64432, 'val_loss': 14.955519, 'test_loss': 16.196583}}
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2024-10-15 03:09:48,200 (client:354) INFO: {'Role': 'Client #5', 'Round': 85, 'Results_raw': {'train_loss': 15.474482, 'val_loss': 16.607453, 'test_loss': 18.74765}}
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2024-10-15 03:10:45,786 (client:354) INFO: {'Role': 'Client #1', 'Round': 85, 'Results_raw': {'train_loss': 10.328364, 'val_loss': 10.305562, 'test_loss': 11.429981}}
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2024-10-15 03:11:40,309 (client:354) INFO: {'Role': 'Client #8', 'Round': 85, 'Results_raw': {'train_loss': 12.921416, 'val_loss': 13.124111, 'test_loss': 13.817016}}
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2024-10-15 03:12:38,497 (client:354) INFO: {'Role': 'Client #3', 'Round': 85, 'Results_raw': {'train_loss': 9.744685, 'val_loss': 10.601509, 'test_loss': 11.965675}}
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2024-10-15 03:12:38,501 (server:615) INFO: {'Role': 'Server #', 'Round': 84, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.866926), 'test_loss': np.float64(97806.14502), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.830631), 'val_loss': np.float64(97617.989252)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.866926), 'test_loss': np.float64(97806.14502), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.830631), 'val_loss': np.float64(97617.989252)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.307645), 'test_avg_loss_bottom_decile': np.float64(15.869089), 'test_avg_loss_top_decile': np.float64(23.106296), 'test_avg_loss_min': np.float64(15.574841), 'test_avg_loss_max': np.float64(23.106296), 'test_avg_loss_bottom10%': np.float64(15.574841), 'test_avg_loss_top10%': np.float64(23.106296), 'test_avg_loss_cos1': np.float64(0.992603), 'test_avg_loss_entropy': np.float64(2.295136), 'test_loss_std': np.float64(11962.833439), 'test_loss_bottom_decile': np.float64(82265.357056), 'test_loss_top_decile': np.float64(119783.040588), 'test_loss_min': np.float64(80739.976135), 'test_loss_max': np.float64(119783.040588), 'test_loss_bottom10%': np.float64(80739.976135), 'test_loss_top10%': np.float64(119783.040588), 'test_loss_cos1': np.float64(0.992603), 'test_loss_entropy': np.float64(2.295136), 'val_avg_loss_std': np.float64(2.539134), 'val_avg_loss_bottom_decile': np.float64(15.503949), 'val_avg_loss_top_decile': np.float64(22.8142), 'val_avg_loss_min': np.float64(15.023343), 'val_avg_loss_max': np.float64(22.8142), 'val_avg_loss_bottom10%': np.float64(15.023343), 'val_avg_loss_top10%': np.float64(22.8142), 'val_avg_loss_cos1': np.float64(0.991031), 'val_avg_loss_entropy': np.float64(2.293449), 'val_loss_std': np.float64(13162.869566), 'val_loss_bottom_decile': np.float64(80372.470459), 'val_loss_top_decile': np.float64(118268.811462), 'val_loss_min': np.float64(77881.009003), 'val_loss_max': np.float64(118268.811462), 'val_loss_bottom10%': np.float64(77881.009003), 'val_loss_top10%': np.float64(118268.811462), 'val_loss_cos1': np.float64(0.991031), 'val_loss_entropy': np.float64(2.293449)}}
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2024-10-15 03:12:38,539 (server:353) INFO: Server: Starting evaluation at the end of round 85.
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2024-10-15 03:12:38,539 (server:359) INFO: ----------- Starting a new training round (Round #86) -------------
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2024-10-15 03:14:59,315 (client:354) INFO: {'Role': 'Client #10', 'Round': 86, 'Results_raw': {'train_loss': 14.542678, 'val_loss': 15.19346, 'test_loss': 16.929621}}
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2024-10-15 03:15:53,064 (client:354) INFO: {'Role': 'Client #4', 'Round': 86, 'Results_raw': {'train_loss': 14.653036, 'val_loss': 15.111212, 'test_loss': 16.651932}}
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2024-10-15 03:16:48,968 (client:354) INFO: {'Role': 'Client #3', 'Round': 86, 'Results_raw': {'train_loss': 9.732043, 'val_loss': 10.527381, 'test_loss': 12.028765}}
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2024-10-15 03:17:46,918 (client:354) INFO: {'Role': 'Client #7', 'Round': 86, 'Results_raw': {'train_loss': 14.885, 'val_loss': 15.463723, 'test_loss': 16.251698}}
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2024-10-15 03:18:44,278 (client:354) INFO: {'Role': 'Client #9', 'Round': 86, 'Results_raw': {'train_loss': 17.366331, 'val_loss': 17.666657, 'test_loss': 18.368796}}
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2024-10-15 03:19:47,323 (client:354) INFO: {'Role': 'Client #6', 'Round': 86, 'Results_raw': {'train_loss': 14.65438, 'val_loss': 14.850303, 'test_loss': 16.185391}}
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2024-10-15 03:20:41,039 (client:354) INFO: {'Role': 'Client #8', 'Round': 86, 'Results_raw': {'train_loss': 12.896856, 'val_loss': 13.108234, 'test_loss': 13.897736}}
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2024-10-15 03:21:34,866 (client:354) INFO: {'Role': 'Client #2', 'Round': 86, 'Results_raw': {'train_loss': 8.428927, 'val_loss': 8.646108, 'test_loss': 9.355601}}
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2024-10-15 03:22:32,091 (client:354) INFO: {'Role': 'Client #1', 'Round': 86, 'Results_raw': {'train_loss': 10.321442, 'val_loss': 10.401519, 'test_loss': 11.381773}}
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2024-10-15 03:23:29,822 (client:354) INFO: {'Role': 'Client #5', 'Round': 86, 'Results_raw': {'train_loss': 15.453639, 'val_loss': 16.4275, 'test_loss': 18.542143}}
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2024-10-15 03:23:29,828 (server:615) INFO: {'Role': 'Server #', 'Round': 85, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.716007), 'test_loss': np.float64(97023.778635), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.70323), 'val_loss': np.float64(96957.545599)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.716007), 'test_loss': np.float64(97023.778635), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.70323), 'val_loss': np.float64(96957.545599)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.330462), 'test_avg_loss_bottom_decile': np.float64(15.666346), 'test_avg_loss_top_decile': np.float64(22.938847), 'test_avg_loss_min': np.float64(15.409576), 'test_avg_loss_max': np.float64(22.938847), 'test_avg_loss_bottom10%': np.float64(15.409576), 'test_avg_loss_top10%': np.float64(22.938847), 'test_avg_loss_cos1': np.float64(0.992337), 'test_avg_loss_entropy': np.float64(2.294855), 'test_loss_std': np.float64(12081.113457), 'test_loss_bottom_decile': np.float64(81214.33725), 'test_loss_top_decile': np.float64(118914.981018), 'test_loss_min': np.float64(79883.243103), 'test_loss_max': np.float64(118914.981018), 'test_loss_bottom10%': np.float64(79883.243103), 'test_loss_top10%': np.float64(118914.981018), 'test_loss_cos1': np.float64(0.992337), 'test_loss_entropy': np.float64(2.294855), 'val_avg_loss_std': np.float64(2.556012), 'val_avg_loss_bottom_decile': np.float64(15.347065), 'val_avg_loss_top_decile': np.float64(22.67896), 'val_avg_loss_min': np.float64(14.891983), 'val_avg_loss_max': np.float64(22.67896), 'val_avg_loss_bottom10%': np.float64(14.891983), 'val_avg_loss_top10%': np.float64(22.67896), 'val_avg_loss_cos1': np.float64(0.990791), 'val_avg_loss_entropy': np.float64(2.293191), 'val_loss_std': np.float64(13250.366623), 'val_loss_bottom_decile': np.float64(79559.184326), 'val_loss_top_decile': np.float64(117567.730835), 'val_loss_min': np.float64(77200.041626), 'val_loss_max': np.float64(117567.730835), 'val_loss_bottom10%': np.float64(77200.041626), 'val_loss_top10%': np.float64(117567.730835), 'val_loss_cos1': np.float64(0.990791), 'val_loss_entropy': np.float64(2.293191)}}
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2024-10-15 03:23:29,875 (server:353) INFO: Server: Starting evaluation at the end of round 86.
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2024-10-15 03:23:29,876 (server:359) INFO: ----------- Starting a new training round (Round #87) -------------
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2024-10-15 03:25:47,014 (client:354) INFO: {'Role': 'Client #4', 'Round': 87, 'Results_raw': {'train_loss': 14.665338, 'val_loss': 15.030545, 'test_loss': 16.357128}}
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2024-10-15 03:26:43,812 (client:354) INFO: {'Role': 'Client #5', 'Round': 87, 'Results_raw': {'train_loss': 15.455984, 'val_loss': 16.65714, 'test_loss': 18.788735}}
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2024-10-15 03:27:39,037 (client:354) INFO: {'Role': 'Client #10', 'Round': 87, 'Results_raw': {'train_loss': 14.540187, 'val_loss': 15.28689, 'test_loss': 17.137747}}
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2024-10-15 03:28:35,705 (client:354) INFO: {'Role': 'Client #7', 'Round': 87, 'Results_raw': {'train_loss': 14.934877, 'val_loss': 15.299001, 'test_loss': 16.057911}}
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2024-10-15 03:29:31,698 (client:354) INFO: {'Role': 'Client #9', 'Round': 87, 'Results_raw': {'train_loss': 17.349461, 'val_loss': 17.534061, 'test_loss': 18.22732}}
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2024-10-15 03:30:32,876 (client:354) INFO: {'Role': 'Client #2', 'Round': 87, 'Results_raw': {'train_loss': 8.409405, 'val_loss': 8.371892, 'test_loss': 8.936649}}
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2024-10-15 03:31:26,220 (client:354) INFO: {'Role': 'Client #1', 'Round': 87, 'Results_raw': {'train_loss': 10.322367, 'val_loss': 10.331217, 'test_loss': 11.401881}}
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2024-10-15 03:32:23,985 (client:354) INFO: {'Role': 'Client #3', 'Round': 87, 'Results_raw': {'train_loss': 9.73786, 'val_loss': 10.545892, 'test_loss': 12.013628}}
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2024-10-15 03:33:20,578 (client:354) INFO: {'Role': 'Client #8', 'Round': 87, 'Results_raw': {'train_loss': 12.923906, 'val_loss': 13.124433, 'test_loss': 13.757774}}
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2024-10-15 03:34:16,921 (client:354) INFO: {'Role': 'Client #6', 'Round': 87, 'Results_raw': {'train_loss': 14.659651, 'val_loss': 14.807446, 'test_loss': 16.516907}}
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2024-10-15 03:34:16,926 (server:615) INFO: {'Role': 'Server #', 'Round': 86, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.775048), 'test_loss': np.float64(97329.850317), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.753277), 'val_loss': np.float64(97216.988275)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.775048), 'test_loss': np.float64(97329.850317), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.753277), 'val_loss': np.float64(97216.988275)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.297387), 'test_avg_loss_bottom_decile': np.float64(15.79843), 'test_avg_loss_top_decile': np.float64(22.846988), 'test_avg_loss_min': np.float64(15.462805), 'test_avg_loss_max': np.float64(22.846988), 'test_avg_loss_bottom10%': np.float64(15.462805), 'test_avg_loss_top10%': np.float64(22.846988), 'test_avg_loss_cos1': np.float64(0.992597), 'test_avg_loss_entropy': np.float64(2.295112), 'test_loss_std': np.float64(11909.653341), 'test_loss_bottom_decile': np.float64(81899.060211), 'test_loss_top_decile': np.float64(118438.787842), 'test_loss_min': np.float64(80159.180939), 'test_loss_max': np.float64(118438.787842), 'test_loss_bottom10%': np.float64(80159.180939), 'test_loss_top10%': np.float64(118438.787842), 'test_loss_cos1': np.float64(0.992597), 'test_loss_entropy': np.float64(2.295112), 'val_avg_loss_std': np.float64(2.539324), 'val_avg_loss_bottom_decile': np.float64(15.445063), 'val_avg_loss_top_decile': np.float64(22.58421), 'val_avg_loss_min': np.float64(14.928857), 'val_avg_loss_max': np.float64(22.58421), 'val_avg_loss_bottom10%': np.float64(14.928857), 'val_avg_loss_top10%': np.float64(22.58421), 'val_avg_loss_cos1': np.float64(0.990957), 'val_avg_loss_entropy': np.float64(2.293357), 'val_loss_std': np.float64(13163.855454), 'val_loss_bottom_decile': np.float64(80067.204681), 'val_loss_top_decile': np.float64(117076.545532), 'val_loss_min': np.float64(77391.193848), 'val_loss_max': np.float64(117076.545532), 'val_loss_bottom10%': np.float64(77391.193848), 'val_loss_top10%': np.float64(117076.545532), 'val_loss_cos1': np.float64(0.990957), 'val_loss_entropy': np.float64(2.293357)}}
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2024-10-15 03:34:16,969 (server:353) INFO: Server: Starting evaluation at the end of round 87.
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2024-10-15 03:34:16,969 (server:359) INFO: ----------- Starting a new training round (Round #88) -------------
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2024-10-15 03:36:38,643 (client:354) INFO: {'Role': 'Client #9', 'Round': 88, 'Results_raw': {'train_loss': 17.33948, 'val_loss': 17.584781, 'test_loss': 18.418929}}
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2024-10-15 03:37:33,575 (client:354) INFO: {'Role': 'Client #10', 'Round': 88, 'Results_raw': {'train_loss': 14.533835, 'val_loss': 15.114206, 'test_loss': 16.70364}}
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2024-10-15 03:38:31,605 (client:354) INFO: {'Role': 'Client #3', 'Round': 88, 'Results_raw': {'train_loss': 9.732794, 'val_loss': 10.523282, 'test_loss': 12.070424}}
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2024-10-15 03:39:27,701 (client:354) INFO: {'Role': 'Client #2', 'Round': 88, 'Results_raw': {'train_loss': 8.418505, 'val_loss': 8.377545, 'test_loss': 9.045563}}
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2024-10-15 03:40:24,138 (client:354) INFO: {'Role': 'Client #5', 'Round': 88, 'Results_raw': {'train_loss': 15.471736, 'val_loss': 16.621981, 'test_loss': 18.90429}}
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2024-10-15 03:41:19,655 (client:354) INFO: {'Role': 'Client #7', 'Round': 88, 'Results_raw': {'train_loss': 14.90084, 'val_loss': 15.485401, 'test_loss': 16.082067}}
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2024-10-15 03:42:21,722 (client:354) INFO: {'Role': 'Client #1', 'Round': 88, 'Results_raw': {'train_loss': 10.320665, 'val_loss': 10.358435, 'test_loss': 11.554197}}
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2024-10-15 03:43:16,558 (client:354) INFO: {'Role': 'Client #8', 'Round': 88, 'Results_raw': {'train_loss': 12.909887, 'val_loss': 13.231142, 'test_loss': 13.991711}}
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2024-10-15 03:44:13,165 (client:354) INFO: {'Role': 'Client #6', 'Round': 88, 'Results_raw': {'train_loss': 14.634023, 'val_loss': 14.852466, 'test_loss': 16.396935}}
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2024-10-15 03:45:10,529 (client:354) INFO: {'Role': 'Client #4', 'Round': 88, 'Results_raw': {'train_loss': 14.670721, 'val_loss': 14.929969, 'test_loss': 15.976403}}
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2024-10-15 03:45:10,533 (server:615) INFO: {'Role': 'Server #', 'Round': 87, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.870455), 'test_loss': np.float64(97824.440607), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.861891), 'val_loss': np.float64(97780.042947)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.870455), 'test_loss': np.float64(97824.440607), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.861891), 'val_loss': np.float64(97780.042947)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.345905), 'test_avg_loss_bottom_decile': np.float64(15.795079), 'test_avg_loss_top_decile': np.float64(23.08102), 'test_avg_loss_min': np.float64(15.528889), 'test_avg_loss_max': np.float64(23.08102), 'test_avg_loss_bottom10%': np.float64(15.528889), 'test_avg_loss_top10%': np.float64(23.08102), 'test_avg_loss_cos1': np.float64(0.992361), 'test_avg_loss_entropy': np.float64(2.294878), 'test_loss_std': np.float64(12161.17192), 'test_loss_bottom_decile': np.float64(81881.687469), 'test_loss_top_decile': np.float64(119652.00531), 'test_loss_min': np.float64(80501.759796), 'test_loss_max': np.float64(119652.00531), 'test_loss_bottom10%': np.float64(80501.759796), 'test_loss_top10%': np.float64(119652.00531), 'test_loss_cos1': np.float64(0.992361), 'test_loss_entropy': np.float64(2.294878), 'val_avg_loss_std': np.float64(2.59681), 'val_avg_loss_bottom_decile': np.float64(15.435374), 'val_avg_loss_top_decile': np.float64(22.849724), 'val_avg_loss_min': np.float64(14.997381), 'val_avg_loss_max': np.float64(22.849724), 'val_avg_loss_bottom10%': np.float64(14.997381), 'val_avg_loss_top10%': np.float64(22.849724), 'val_avg_loss_cos1': np.float64(0.990655), 'val_avg_loss_entropy': np.float64(2.293051), 'val_loss_std': np.float64(13461.860771), 'val_loss_bottom_decile': np.float64(80016.981079), 'val_loss_top_decile': np.float64(118452.970764), 'val_loss_min': np.float64(77746.425049), 'val_loss_max': np.float64(118452.970764), 'val_loss_bottom10%': np.float64(77746.425049), 'val_loss_top10%': np.float64(118452.970764), 'val_loss_cos1': np.float64(0.990655), 'val_loss_entropy': np.float64(2.293051)}}
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2024-10-15 03:45:10,569 (server:353) INFO: Server: Starting evaluation at the end of round 88.
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2024-10-15 03:45:10,569 (server:359) INFO: ----------- Starting a new training round (Round #89) -------------
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2024-10-15 03:47:40,717 (client:354) INFO: {'Role': 'Client #8', 'Round': 89, 'Results_raw': {'train_loss': 12.909092, 'val_loss': 13.158943, 'test_loss': 13.837022}}
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2024-10-15 03:48:36,017 (client:354) INFO: {'Role': 'Client #10', 'Round': 89, 'Results_raw': {'train_loss': 14.560985, 'val_loss': 15.254118, 'test_loss': 16.797292}}
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2024-10-15 03:49:29,356 (client:354) INFO: {'Role': 'Client #7', 'Round': 89, 'Results_raw': {'train_loss': 14.874744, 'val_loss': 15.558721, 'test_loss': 16.454795}}
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2024-10-15 03:50:27,423 (client:354) INFO: {'Role': 'Client #5', 'Round': 89, 'Results_raw': {'train_loss': 15.443741, 'val_loss': 16.62922, 'test_loss': 18.706389}}
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2024-10-15 03:51:22,115 (client:354) INFO: {'Role': 'Client #9', 'Round': 89, 'Results_raw': {'train_loss': 17.351334, 'val_loss': 17.659507, 'test_loss': 18.562602}}
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2024-10-15 03:52:16,505 (client:354) INFO: {'Role': 'Client #4', 'Round': 89, 'Results_raw': {'train_loss': 14.608125, 'val_loss': 14.996109, 'test_loss': 16.329542}}
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2024-10-15 03:53:13,119 (client:354) INFO: {'Role': 'Client #2', 'Round': 89, 'Results_raw': {'train_loss': 8.385828, 'val_loss': 8.422703, 'test_loss': 9.158164}}
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2024-10-15 03:54:09,287 (client:354) INFO: {'Role': 'Client #1', 'Round': 89, 'Results_raw': {'train_loss': 10.319217, 'val_loss': 10.42966, 'test_loss': 11.481656}}
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2024-10-15 03:55:05,844 (client:354) INFO: {'Role': 'Client #6', 'Round': 89, 'Results_raw': {'train_loss': 14.631497, 'val_loss': 14.943389, 'test_loss': 16.519515}}
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2024-10-15 03:55:58,605 (client:354) INFO: {'Role': 'Client #3', 'Round': 89, 'Results_raw': {'train_loss': 9.763738, 'val_loss': 10.547169, 'test_loss': 12.059929}}
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2024-10-15 03:55:58,609 (server:615) INFO: {'Role': 'Server #', 'Round': 88, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.812911), 'test_loss': np.float64(97526.131519), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.785527), 'val_loss': np.float64(97384.170026)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.812911), 'test_loss': np.float64(97526.131519), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.785527), 'val_loss': np.float64(97384.170026)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.33335), 'test_avg_loss_bottom_decile': np.float64(15.776004), 'test_avg_loss_top_decile': np.float64(23.044176), 'test_avg_loss_min': np.float64(15.493685), 'test_avg_loss_max': np.float64(23.044176), 'test_avg_loss_bottom10%': np.float64(15.493685), 'test_avg_loss_top10%': np.float64(23.044176), 'test_avg_loss_cos1': np.float64(0.992396), 'test_avg_loss_entropy': np.float64(2.294921), 'test_loss_std': np.float64(12096.088817), 'test_loss_bottom_decile': np.float64(81782.806519), 'test_loss_top_decile': np.float64(119461.008118), 'test_loss_min': np.float64(80319.265228), 'test_loss_max': np.float64(119461.008118), 'test_loss_bottom10%': np.float64(80319.265228), 'test_loss_top10%': np.float64(119461.008118), 'test_loss_cos1': np.float64(0.992396), 'test_loss_entropy': np.float64(2.294921), 'val_avg_loss_std': np.float64(2.56472), 'val_avg_loss_bottom_decile': np.float64(15.433737), 'val_avg_loss_top_decile': np.float64(22.753156), 'val_avg_loss_min': np.float64(14.955402), 'val_avg_loss_max': np.float64(22.753156), 'val_avg_loss_bottom10%': np.float64(14.955402), 'val_avg_loss_top10%': np.float64(22.753156), 'val_avg_loss_cos1': np.float64(0.990809), 'val_avg_loss_entropy': np.float64(2.293214), 'val_loss_std': np.float64(13295.506613), 'val_loss_bottom_decile': np.float64(80008.494446), 'val_loss_top_decile': np.float64(117952.36322), 'val_loss_min': np.float64(77528.806091), 'val_loss_max': np.float64(117952.36322), 'val_loss_bottom10%': np.float64(77528.806091), 'val_loss_top10%': np.float64(117952.36322), 'val_loss_cos1': np.float64(0.990809), 'val_loss_entropy': np.float64(2.293214)}}
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2024-10-15 03:55:58,643 (server:353) INFO: Server: Starting evaluation at the end of round 89.
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2024-10-15 03:55:58,643 (server:359) INFO: ----------- Starting a new training round (Round #90) -------------
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2024-10-15 03:58:23,399 (client:354) INFO: {'Role': 'Client #5', 'Round': 90, 'Results_raw': {'train_loss': 15.427376, 'val_loss': 16.579109, 'test_loss': 18.73385}}
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2024-10-15 03:59:20,766 (client:354) INFO: {'Role': 'Client #2', 'Round': 90, 'Results_raw': {'train_loss': 8.381084, 'val_loss': 8.370242, 'test_loss': 8.933019}}
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2024-10-15 04:00:16,886 (client:354) INFO: {'Role': 'Client #1', 'Round': 90, 'Results_raw': {'train_loss': 10.352143, 'val_loss': 10.349449, 'test_loss': 11.504561}}
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2024-10-15 04:01:14,415 (client:354) INFO: {'Role': 'Client #8', 'Round': 90, 'Results_raw': {'train_loss': 12.888807, 'val_loss': 13.208004, 'test_loss': 14.022937}}
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2024-10-15 04:02:07,279 (client:354) INFO: {'Role': 'Client #10', 'Round': 90, 'Results_raw': {'train_loss': 14.553831, 'val_loss': 15.272445, 'test_loss': 16.914322}}
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2024-10-15 04:03:05,105 (client:354) INFO: {'Role': 'Client #3', 'Round': 90, 'Results_raw': {'train_loss': 9.738882, 'val_loss': 10.69813, 'test_loss': 12.176381}}
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2024-10-15 04:03:59,990 (client:354) INFO: {'Role': 'Client #9', 'Round': 90, 'Results_raw': {'train_loss': 17.337446, 'val_loss': 17.813726, 'test_loss': 18.695627}}
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2024-10-15 04:05:00,807 (client:354) INFO: {'Role': 'Client #7', 'Round': 90, 'Results_raw': {'train_loss': 14.890917, 'val_loss': 15.473859, 'test_loss': 16.233499}}
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2024-10-15 04:05:56,982 (client:354) INFO: {'Role': 'Client #4', 'Round': 90, 'Results_raw': {'train_loss': 14.634339, 'val_loss': 14.942564, 'test_loss': 16.361326}}
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2024-10-15 04:06:48,255 (client:354) INFO: {'Role': 'Client #6', 'Round': 90, 'Results_raw': {'train_loss': 14.619733, 'val_loss': 14.765114, 'test_loss': 16.369373}}
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2024-10-15 04:06:48,260 (server:615) INFO: {'Role': 'Server #', 'Round': 89, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.778294), 'test_loss': np.float64(97346.674997), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.739101), 'val_loss': np.float64(97143.502164)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.778294), 'test_loss': np.float64(97346.674997), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.739101), 'val_loss': np.float64(97143.502164)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.257878), 'test_avg_loss_bottom_decile': np.float64(15.885117), 'test_avg_loss_top_decile': np.float64(22.856661), 'test_avg_loss_min': np.float64(15.571194), 'test_avg_loss_max': np.float64(22.856661), 'test_avg_loss_bottom10%': np.float64(15.571194), 'test_avg_loss_top10%': np.float64(22.856661), 'test_avg_loss_cos1': np.float64(0.992849), 'test_avg_loss_entropy': np.float64(2.295386), 'test_loss_std': np.float64(11704.840777), 'test_loss_bottom_decile': np.float64(82348.448151), 'test_loss_top_decile': np.float64(118488.929382), 'test_loss_min': np.float64(80721.069), 'test_loss_max': np.float64(118488.929382), 'test_loss_bottom10%': np.float64(80721.069), 'test_loss_top10%': np.float64(118488.929382), 'test_loss_cos1': np.float64(0.992849), 'test_loss_entropy': np.float64(2.295386), 'val_avg_loss_std': np.float64(2.481651), 'val_avg_loss_bottom_decile': np.float64(15.520437), 'val_avg_loss_top_decile': np.float64(22.553612), 'val_avg_loss_min': np.float64(15.038759), 'val_avg_loss_max': np.float64(22.553612), 'val_avg_loss_bottom10%': np.float64(15.038759), 'val_avg_loss_top10%': np.float64(22.553612), 'val_avg_loss_cos1': np.float64(0.991345), 'val_avg_loss_entropy': np.float64(2.293778), 'val_loss_std': np.float64(12864.877012), 'val_loss_bottom_decile': np.float64(80457.946045), 'val_loss_top_decile': np.float64(116917.926453), 'val_loss_min': np.float64(77960.924408), 'val_loss_max': np.float64(116917.926453), 'val_loss_bottom10%': np.float64(77960.924408), 'val_loss_top10%': np.float64(116917.926453), 'val_loss_cos1': np.float64(0.991345), 'val_loss_entropy': np.float64(2.293778)}}
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2024-10-15 04:06:48,301 (server:353) INFO: Server: Starting evaluation at the end of round 90.
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2024-10-15 04:06:48,302 (server:359) INFO: ----------- Starting a new training round (Round #91) -------------
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2024-10-15 04:09:08,770 (client:354) INFO: {'Role': 'Client #5', 'Round': 91, 'Results_raw': {'train_loss': 15.428665, 'val_loss': 16.673069, 'test_loss': 19.027739}}
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2024-10-15 04:10:00,296 (client:354) INFO: {'Role': 'Client #10', 'Round': 91, 'Results_raw': {'train_loss': 14.52018, 'val_loss': 15.28491, 'test_loss': 16.872641}}
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2024-10-15 04:10:53,601 (client:354) INFO: {'Role': 'Client #8', 'Round': 91, 'Results_raw': {'train_loss': 12.908036, 'val_loss': 13.170037, 'test_loss': 13.823792}}
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2024-10-15 04:11:48,234 (client:354) INFO: {'Role': 'Client #6', 'Round': 91, 'Results_raw': {'train_loss': 14.625522, 'val_loss': 14.805702, 'test_loss': 16.200339}}
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2024-10-15 04:12:44,858 (client:354) INFO: {'Role': 'Client #2', 'Round': 91, 'Results_raw': {'train_loss': 8.421302, 'val_loss': 8.361905, 'test_loss': 9.093856}}
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2024-10-15 04:13:37,203 (client:354) INFO: {'Role': 'Client #4', 'Round': 91, 'Results_raw': {'train_loss': 14.628434, 'val_loss': 15.068729, 'test_loss': 16.555658}}
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2024-10-15 04:14:33,789 (client:354) INFO: {'Role': 'Client #3', 'Round': 91, 'Results_raw': {'train_loss': 9.684925, 'val_loss': 10.488634, 'test_loss': 11.947146}}
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2024-10-15 04:15:32,113 (client:354) INFO: {'Role': 'Client #9', 'Round': 91, 'Results_raw': {'train_loss': 17.307933, 'val_loss': 17.548032, 'test_loss': 18.108297}}
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2024-10-15 04:16:27,758 (client:354) INFO: {'Role': 'Client #7', 'Round': 91, 'Results_raw': {'train_loss': 14.886543, 'val_loss': 15.286355, 'test_loss': 15.995121}}
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2024-10-15 04:17:23,742 (client:354) INFO: {'Role': 'Client #1', 'Round': 91, 'Results_raw': {'train_loss': 10.336346, 'val_loss': 10.382264, 'test_loss': 11.501193}}
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2024-10-15 04:17:23,746 (server:615) INFO: {'Role': 'Server #', 'Round': 90, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.791866), 'test_loss': np.float64(97417.034805), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.755743), 'val_loss': np.float64(97229.772922)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.791866), 'test_loss': np.float64(97417.034805), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.755743), 'val_loss': np.float64(97229.772922)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.314945), 'test_avg_loss_bottom_decile': np.float64(15.71194), 'test_avg_loss_top_decile': np.float64(22.967401), 'test_avg_loss_min': np.float64(15.50312), 'test_avg_loss_max': np.float64(22.967401), 'test_avg_loss_bottom10%': np.float64(15.50312), 'test_avg_loss_top10%': np.float64(22.967401), 'test_avg_loss_cos1': np.float64(0.992498), 'test_avg_loss_entropy': np.float64(2.295017), 'test_loss_std': np.float64(12000.67236), 'test_loss_bottom_decile': np.float64(81450.696442), 'test_loss_top_decile': np.float64(119063.006775), 'test_loss_min': np.float64(80368.174133), 'test_loss_max': np.float64(119063.006775), 'test_loss_bottom10%': np.float64(80368.174133), 'test_loss_top10%': np.float64(119063.006775), 'test_loss_cos1': np.float64(0.992498), 'test_loss_entropy': np.float64(2.295017), 'val_avg_loss_std': np.float64(2.550121), 'val_avg_loss_bottom_decile': np.float64(15.372825), 'val_avg_loss_top_decile': np.float64(22.715404), 'val_avg_loss_min': np.float64(14.957181), 'val_avg_loss_max': np.float64(22.715404), 'val_avg_loss_bottom10%': np.float64(14.957181), 'val_avg_loss_top10%': np.float64(22.715404), 'val_avg_loss_cos1': np.float64(0.990883), 'val_avg_loss_entropy': np.float64(2.293289), 'val_loss_std': np.float64(13219.825868), 'val_loss_bottom_decile': np.float64(79692.723389), 'val_loss_top_decile': np.float64(117756.655701), 'val_loss_min': np.float64(77538.026337), 'val_loss_max': np.float64(117756.655701), 'val_loss_bottom10%': np.float64(77538.026337), 'val_loss_top10%': np.float64(117756.655701), 'val_loss_cos1': np.float64(0.990883), 'val_loss_entropy': np.float64(2.293289)}}
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2024-10-15 04:17:23,779 (server:353) INFO: Server: Starting evaluation at the end of round 91.
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2024-10-15 04:17:23,780 (server:359) INFO: ----------- Starting a new training round (Round #92) -------------
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2024-10-15 04:19:47,144 (client:354) INFO: {'Role': 'Client #7', 'Round': 92, 'Results_raw': {'train_loss': 14.891376, 'val_loss': 15.367849, 'test_loss': 16.0062}}
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2024-10-15 04:20:40,501 (client:354) INFO: {'Role': 'Client #3', 'Round': 92, 'Results_raw': {'train_loss': 9.746137, 'val_loss': 10.563063, 'test_loss': 12.132747}}
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2024-10-15 04:21:36,671 (client:354) INFO: {'Role': 'Client #6', 'Round': 92, 'Results_raw': {'train_loss': 14.619803, 'val_loss': 14.926303, 'test_loss': 16.870586}}
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2024-10-15 04:22:29,840 (client:354) INFO: {'Role': 'Client #4', 'Round': 92, 'Results_raw': {'train_loss': 14.594272, 'val_loss': 14.983375, 'test_loss': 16.120397}}
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2024-10-15 04:23:24,721 (client:354) INFO: {'Role': 'Client #10', 'Round': 92, 'Results_raw': {'train_loss': 14.527431, 'val_loss': 15.188762, 'test_loss': 16.919638}}
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2024-10-15 04:24:20,340 (client:354) INFO: {'Role': 'Client #9', 'Round': 92, 'Results_raw': {'train_loss': 17.295811, 'val_loss': 17.680011, 'test_loss': 18.471804}}
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2024-10-15 04:25:13,878 (client:354) INFO: {'Role': 'Client #5', 'Round': 92, 'Results_raw': {'train_loss': 15.439055, 'val_loss': 16.601819, 'test_loss': 18.792903}}
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2024-10-15 04:26:09,097 (client:354) INFO: {'Role': 'Client #2', 'Round': 92, 'Results_raw': {'train_loss': 8.406349, 'val_loss': 8.399394, 'test_loss': 9.05485}}
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2024-10-15 04:27:06,675 (client:354) INFO: {'Role': 'Client #1', 'Round': 92, 'Results_raw': {'train_loss': 10.300414, 'val_loss': 10.320202, 'test_loss': 11.434804}}
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2024-10-15 04:28:02,959 (client:354) INFO: {'Role': 'Client #8', 'Round': 92, 'Results_raw': {'train_loss': 12.881375, 'val_loss': 13.184818, 'test_loss': 13.85408}}
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2024-10-15 04:28:02,964 (server:615) INFO: {'Role': 'Server #', 'Round': 91, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.809963), 'test_loss': np.float64(97510.848032), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.798381), 'val_loss': np.float64(97450.805023)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.809963), 'test_loss': np.float64(97510.848032), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.798381), 'val_loss': np.float64(97450.805023)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.326939), 'test_avg_loss_bottom_decile': np.float64(15.760583), 'test_avg_loss_top_decile': np.float64(23.005147), 'test_avg_loss_min': np.float64(15.508096), 'test_avg_loss_max': np.float64(23.005147), 'test_avg_loss_bottom10%': np.float64(15.508096), 'test_avg_loss_top10%': np.float64(23.005147), 'test_avg_loss_cos1': np.float64(0.992435), 'test_avg_loss_entropy': np.float64(2.294956), 'test_loss_std': np.float64(12062.851858), 'test_loss_bottom_decile': np.float64(81702.864349), 'test_loss_top_decile': np.float64(119258.681946), 'test_loss_min': np.float64(80393.968292), 'test_loss_max': np.float64(119258.681946), 'test_loss_bottom10%': np.float64(80393.968292), 'test_loss_top10%': np.float64(119258.681946), 'test_loss_cos1': np.float64(0.992435), 'test_loss_entropy': np.float64(2.294956), 'val_avg_loss_std': np.float64(2.567356), 'val_avg_loss_bottom_decile': np.float64(15.419886), 'val_avg_loss_top_decile': np.float64(22.748997), 'val_avg_loss_min': np.float64(14.976799), 'val_avg_loss_max': np.float64(22.748997), 'val_avg_loss_bottom10%': np.float64(14.976799), 'val_avg_loss_top10%': np.float64(22.748997), 'val_avg_loss_cos1': np.float64(0.990802), 'val_avg_loss_entropy': np.float64(2.293204), 'val_loss_std': np.float64(13309.175343), 'val_loss_bottom_decile': np.float64(79936.687286), 'val_loss_top_decile': np.float64(117930.802917), 'val_loss_min': np.float64(77639.727783), 'val_loss_max': np.float64(117930.802917), 'val_loss_bottom10%': np.float64(77639.727783), 'val_loss_top10%': np.float64(117930.802917), 'val_loss_cos1': np.float64(0.990802), 'val_loss_entropy': np.float64(2.293204)}}
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2024-10-15 04:28:03,011 (server:353) INFO: Server: Starting evaluation at the end of round 92.
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2024-10-15 04:28:03,012 (server:359) INFO: ----------- Starting a new training round (Round #93) -------------
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2024-10-15 04:30:27,074 (client:354) INFO: {'Role': 'Client #7', 'Round': 93, 'Results_raw': {'train_loss': 14.894512, 'val_loss': 15.39021, 'test_loss': 16.064362}}
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2024-10-15 04:31:23,086 (client:354) INFO: {'Role': 'Client #8', 'Round': 93, 'Results_raw': {'train_loss': 12.878223, 'val_loss': 13.16911, 'test_loss': 14.035102}}
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2024-10-15 04:32:19,153 (client:354) INFO: {'Role': 'Client #9', 'Round': 93, 'Results_raw': {'train_loss': 17.310836, 'val_loss': 17.522877, 'test_loss': 18.415015}}
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2024-10-15 04:33:14,854 (client:354) INFO: {'Role': 'Client #3', 'Round': 93, 'Results_raw': {'train_loss': 9.736963, 'val_loss': 10.565045, 'test_loss': 12.069019}}
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2024-10-15 04:34:11,832 (client:354) INFO: {'Role': 'Client #5', 'Round': 93, 'Results_raw': {'train_loss': 15.403209, 'val_loss': 16.581019, 'test_loss': 18.898019}}
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2024-10-15 04:35:08,339 (client:354) INFO: {'Role': 'Client #6', 'Round': 93, 'Results_raw': {'train_loss': 14.610005, 'val_loss': 14.887201, 'test_loss': 16.210197}}
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2024-10-15 04:36:05,065 (client:354) INFO: {'Role': 'Client #1', 'Round': 93, 'Results_raw': {'train_loss': 10.304401, 'val_loss': 10.351946, 'test_loss': 11.510987}}
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2024-10-15 04:37:02,557 (client:354) INFO: {'Role': 'Client #10', 'Round': 93, 'Results_raw': {'train_loss': 14.534389, 'val_loss': 15.222371, 'test_loss': 16.745252}}
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2024-10-15 04:37:59,746 (client:354) INFO: {'Role': 'Client #4', 'Round': 93, 'Results_raw': {'train_loss': 14.6208, 'val_loss': 14.908663, 'test_loss': 16.163284}}
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2024-10-15 04:38:59,477 (client:354) INFO: {'Role': 'Client #2', 'Round': 93, 'Results_raw': {'train_loss': 8.400553, 'val_loss': 8.478906, 'test_loss': 9.192695}}
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2024-10-15 04:38:59,481 (server:615) INFO: {'Role': 'Server #', 'Round': 92, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.742548), 'test_loss': np.float64(97161.36806), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.714742), 'val_loss': np.float64(97017.222714)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.742548), 'test_loss': np.float64(97161.36806), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.714742), 'val_loss': np.float64(97017.222714)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.331809), 'test_avg_loss_bottom_decile': np.float64(15.648993), 'test_avg_loss_top_decile': np.float64(22.915219), 'test_avg_loss_min': np.float64(15.503345), 'test_avg_loss_max': np.float64(22.915219), 'test_avg_loss_bottom10%': np.float64(15.503345), 'test_avg_loss_top10%': np.float64(22.915219), 'test_avg_loss_cos1': np.float64(0.992349), 'test_avg_loss_entropy': np.float64(2.294868), 'test_loss_std': np.float64(12088.096155), 'test_loss_bottom_decile': np.float64(81124.377594), 'test_loss_top_decile': np.float64(118792.494507), 'test_loss_min': np.float64(80369.342865), 'test_loss_max': np.float64(118792.494507), 'test_loss_bottom10%': np.float64(80369.342865), 'test_loss_top10%': np.float64(118792.494507), 'test_loss_cos1': np.float64(0.992349), 'test_loss_entropy': np.float64(2.294868), 'val_avg_loss_std': np.float64(2.565368), 'val_avg_loss_bottom_decile': np.float64(15.311413), 'val_avg_loss_top_decile': np.float64(22.661329), 'val_avg_loss_min': np.float64(14.976035), 'val_avg_loss_max': np.float64(22.661329), 'val_avg_loss_bottom10%': np.float64(14.976035), 'val_avg_loss_top10%': np.float64(22.661329), 'val_avg_loss_cos1': np.float64(0.990735), 'val_avg_loss_entropy': np.float64(2.29314), 'val_loss_std': np.float64(13298.868472), 'val_loss_bottom_decile': np.float64(79374.36557), 'val_loss_top_decile': np.float64(117476.328979), 'val_loss_min': np.float64(77635.764099), 'val_loss_max': np.float64(117476.328979), 'val_loss_bottom10%': np.float64(77635.764099), 'val_loss_top10%': np.float64(117476.328979), 'val_loss_cos1': np.float64(0.990735), 'val_loss_entropy': np.float64(2.29314)}}
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2024-10-15 04:38:59,517 (server:353) INFO: Server: Starting evaluation at the end of round 93.
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2024-10-15 04:38:59,517 (server:359) INFO: ----------- Starting a new training round (Round #94) -------------
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2024-10-15 04:41:26,410 (client:354) INFO: {'Role': 'Client #4', 'Round': 94, 'Results_raw': {'train_loss': 14.582111, 'val_loss': 14.906442, 'test_loss': 16.055899}}
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2024-10-15 04:42:20,705 (client:354) INFO: {'Role': 'Client #9', 'Round': 94, 'Results_raw': {'train_loss': 17.325351, 'val_loss': 18.080618, 'test_loss': 19.025898}}
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2024-10-15 04:43:15,808 (client:354) INFO: {'Role': 'Client #8', 'Round': 94, 'Results_raw': {'train_loss': 12.892431, 'val_loss': 13.075862, 'test_loss': 13.851643}}
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2024-10-15 04:44:12,316 (client:354) INFO: {'Role': 'Client #10', 'Round': 94, 'Results_raw': {'train_loss': 14.544007, 'val_loss': 15.23961, 'test_loss': 17.037174}}
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2024-10-15 04:45:08,725 (client:354) INFO: {'Role': 'Client #3', 'Round': 94, 'Results_raw': {'train_loss': 9.702741, 'val_loss': 10.505406, 'test_loss': 12.000735}}
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2024-10-15 04:46:04,806 (client:354) INFO: {'Role': 'Client #6', 'Round': 94, 'Results_raw': {'train_loss': 14.606421, 'val_loss': 14.928069, 'test_loss': 16.360451}}
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2024-10-15 04:47:01,454 (client:354) INFO: {'Role': 'Client #5', 'Round': 94, 'Results_raw': {'train_loss': 15.42503, 'val_loss': 16.605751, 'test_loss': 18.914212}}
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2024-10-15 04:47:58,801 (client:354) INFO: {'Role': 'Client #1', 'Round': 94, 'Results_raw': {'train_loss': 10.300883, 'val_loss': 10.318472, 'test_loss': 11.51379}}
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2024-10-15 04:48:55,382 (client:354) INFO: {'Role': 'Client #2', 'Round': 94, 'Results_raw': {'train_loss': 8.396842, 'val_loss': 8.386208, 'test_loss': 9.089031}}
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2024-10-15 04:49:53,939 (client:354) INFO: {'Role': 'Client #7', 'Round': 94, 'Results_raw': {'train_loss': 14.87994, 'val_loss': 15.434085, 'test_loss': 16.209458}}
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2024-10-15 04:49:53,948 (server:615) INFO: {'Role': 'Server #', 'Round': 93, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.773691), 'test_loss': np.float64(97322.814493), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.735288), 'val_loss': np.float64(97123.732431)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.773691), 'test_loss': np.float64(97322.814493), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.735288), 'val_loss': np.float64(97123.732431)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.224792), 'test_avg_loss_bottom_decile': np.float64(16.047904), 'test_avg_loss_top_decile': np.float64(22.879875), 'test_avg_loss_min': np.float64(15.560153), 'test_avg_loss_max': np.float64(22.879875), 'test_avg_loss_bottom10%': np.float64(15.560153), 'test_avg_loss_top10%': np.float64(22.879875), 'test_avg_loss_cos1': np.float64(0.993051), 'test_avg_loss_entropy': np.float64(2.295603), 'test_loss_std': np.float64(11533.324031), 'test_loss_bottom_decile': np.float64(83192.335083), 'test_loss_top_decile': np.float64(118609.269409), 'test_loss_min': np.float64(80663.833313), 'test_loss_max': np.float64(118609.269409), 'test_loss_bottom10%': np.float64(80663.833313), 'test_loss_top10%': np.float64(118609.269409), 'test_loss_cos1': np.float64(0.993051), 'test_loss_entropy': np.float64(2.295603), 'val_avg_loss_std': np.float64(2.452052), 'val_avg_loss_bottom_decile': np.float64(15.691285), 'val_avg_loss_top_decile': np.float64(22.60856), 'val_avg_loss_min': np.float64(15.018258), 'val_avg_loss_max': np.float64(22.60856), 'val_avg_loss_bottom10%': np.float64(15.018258), 'val_avg_loss_top10%': np.float64(22.60856), 'val_avg_loss_cos1': np.float64(0.991544), 'val_avg_loss_entropy': np.float64(2.293995), 'val_loss_std': np.float64(12711.435268), 'val_loss_bottom_decile': np.float64(81343.619415), 'val_loss_top_decile': np.float64(117202.773071), 'val_loss_min': np.float64(77854.651031), 'val_loss_max': np.float64(117202.773071), 'val_loss_bottom10%': np.float64(77854.651031), 'val_loss_top10%': np.float64(117202.773071), 'val_loss_cos1': np.float64(0.991544), 'val_loss_entropy': np.float64(2.293995)}}
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2024-10-15 04:49:54,018 (server:353) INFO: Server: Starting evaluation at the end of round 94.
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2024-10-15 04:49:54,018 (server:359) INFO: ----------- Starting a new training round (Round #95) -------------
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2024-10-15 04:52:15,638 (client:354) INFO: {'Role': 'Client #10', 'Round': 95, 'Results_raw': {'train_loss': 14.513653, 'val_loss': 15.216324, 'test_loss': 17.092539}}
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2024-10-15 04:53:09,180 (client:354) INFO: {'Role': 'Client #4', 'Round': 95, 'Results_raw': {'train_loss': 14.599821, 'val_loss': 14.976331, 'test_loss': 16.142977}}
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2024-10-15 04:54:04,339 (client:354) INFO: {'Role': 'Client #3', 'Round': 95, 'Results_raw': {'train_loss': 9.70002, 'val_loss': 10.516934, 'test_loss': 11.987917}}
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2024-10-15 04:54:59,539 (client:354) INFO: {'Role': 'Client #9', 'Round': 95, 'Results_raw': {'train_loss': 17.365171, 'val_loss': 17.65414, 'test_loss': 18.565946}}
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2024-10-15 04:55:56,546 (client:354) INFO: {'Role': 'Client #1', 'Round': 95, 'Results_raw': {'train_loss': 10.307059, 'val_loss': 10.474097, 'test_loss': 11.629171}}
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2024-10-15 04:56:51,734 (client:354) INFO: {'Role': 'Client #5', 'Round': 95, 'Results_raw': {'train_loss': 15.409675, 'val_loss': 16.691556, 'test_loss': 19.027697}}
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2024-10-15 04:57:48,461 (client:354) INFO: {'Role': 'Client #2', 'Round': 95, 'Results_raw': {'train_loss': 8.41999, 'val_loss': 8.477796, 'test_loss': 9.227495}}
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2024-10-15 04:58:45,782 (client:354) INFO: {'Role': 'Client #6', 'Round': 95, 'Results_raw': {'train_loss': 14.599463, 'val_loss': 14.742358, 'test_loss': 16.334008}}
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2024-10-15 04:59:41,676 (client:354) INFO: {'Role': 'Client #8', 'Round': 95, 'Results_raw': {'train_loss': 12.873656, 'val_loss': 13.132948, 'test_loss': 14.000244}}
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2024-10-15 05:00:38,304 (client:354) INFO: {'Role': 'Client #7', 'Round': 95, 'Results_raw': {'train_loss': 14.854406, 'val_loss': 15.414198, 'test_loss': 16.188485}}
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2024-10-15 05:00:38,308 (server:615) INFO: {'Role': 'Server #', 'Round': 94, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.849116), 'test_loss': np.float64(97713.816534), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.798692), 'val_loss': np.float64(97452.418872)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.849116), 'test_loss': np.float64(97713.816534), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.798692), 'val_loss': np.float64(97452.418872)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.282809), 'test_avg_loss_bottom_decile': np.float64(15.879101), 'test_avg_loss_top_decile': np.float64(22.981703), 'test_avg_loss_min': np.float64(15.600619), 'test_avg_loss_max': np.float64(22.981703), 'test_avg_loss_bottom10%': np.float64(15.600619), 'test_avg_loss_top10%': np.float64(22.981703), 'test_avg_loss_cos1': np.float64(0.992746), 'test_avg_loss_entropy': np.float64(2.295276), 'test_loss_std': np.float64(11834.084181), 'test_loss_bottom_decile': np.float64(82317.259979), 'test_loss_top_decile': np.float64(119137.146057), 'test_loss_min': np.float64(80873.608276), 'test_loss_max': np.float64(119137.146057), 'test_loss_bottom10%': np.float64(80873.608276), 'test_loss_top10%': np.float64(119137.146057), 'test_loss_cos1': np.float64(0.992746), 'test_loss_entropy': np.float64(2.295276), 'val_avg_loss_std': np.float64(2.519082), 'val_avg_loss_bottom_decile': np.float64(15.515487), 'val_avg_loss_top_decile': np.float64(22.711387), 'val_avg_loss_min': np.float64(15.038749), 'val_avg_loss_max': np.float64(22.711387), 'val_avg_loss_bottom10%': np.float64(15.038749), 'val_avg_loss_top10%': np.float64(22.711387), 'val_avg_loss_cos1': np.float64(0.991141), 'val_avg_loss_entropy': np.float64(2.293563), 'val_loss_std': np.float64(13058.920916), 'val_loss_bottom_decile': np.float64(80432.283722), 'val_loss_top_decile': np.float64(117735.827881), 'val_loss_min': np.float64(77960.873749), 'val_loss_max': np.float64(117735.827881), 'val_loss_bottom10%': np.float64(77960.873749), 'val_loss_top10%': np.float64(117735.827881), 'val_loss_cos1': np.float64(0.991141), 'val_loss_entropy': np.float64(2.293563)}}
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2024-10-15 05:00:38,347 (server:353) INFO: Server: Starting evaluation at the end of round 95.
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2024-10-15 05:00:38,348 (server:359) INFO: ----------- Starting a new training round (Round #96) -------------
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2024-10-15 05:03:05,035 (client:354) INFO: {'Role': 'Client #3', 'Round': 96, 'Results_raw': {'train_loss': 9.684363, 'val_loss': 10.446445, 'test_loss': 11.963789}}
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2024-10-15 05:03:59,115 (client:354) INFO: {'Role': 'Client #6', 'Round': 96, 'Results_raw': {'train_loss': 14.600257, 'val_loss': 14.873498, 'test_loss': 16.191424}}
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2024-10-15 05:04:53,649 (client:354) INFO: {'Role': 'Client #8', 'Round': 96, 'Results_raw': {'train_loss': 12.858113, 'val_loss': 13.148767, 'test_loss': 13.966933}}
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2024-10-15 05:05:48,406 (client:354) INFO: {'Role': 'Client #10', 'Round': 96, 'Results_raw': {'train_loss': 14.517313, 'val_loss': 15.212015, 'test_loss': 16.920735}}
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2024-10-15 05:06:41,860 (client:354) INFO: {'Role': 'Client #1', 'Round': 96, 'Results_raw': {'train_loss': 10.315413, 'val_loss': 10.375328, 'test_loss': 11.598564}}
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2024-10-15 05:07:36,058 (client:354) INFO: {'Role': 'Client #5', 'Round': 96, 'Results_raw': {'train_loss': 15.395957, 'val_loss': 16.613266, 'test_loss': 18.637654}}
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2024-10-15 05:08:30,359 (client:354) INFO: {'Role': 'Client #4', 'Round': 96, 'Results_raw': {'train_loss': 14.592912, 'val_loss': 14.96309, 'test_loss': 16.228523}}
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2024-10-15 05:09:25,234 (client:354) INFO: {'Role': 'Client #7', 'Round': 96, 'Results_raw': {'train_loss': 14.856144, 'val_loss': 15.475534, 'test_loss': 16.25111}}
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2024-10-15 05:10:19,485 (client:354) INFO: {'Role': 'Client #9', 'Round': 96, 'Results_raw': {'train_loss': 17.298339, 'val_loss': 17.798165, 'test_loss': 18.833924}}
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2024-10-15 05:11:17,391 (client:354) INFO: {'Role': 'Client #2', 'Round': 96, 'Results_raw': {'train_loss': 8.352231, 'val_loss': 8.345762, 'test_loss': 9.057261}}
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2024-10-15 05:11:17,395 (server:615) INFO: {'Role': 'Server #', 'Round': 95, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.731445), 'test_loss': np.float64(97103.81282), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.671851), 'val_loss': np.float64(96794.877878)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.731445), 'test_loss': np.float64(97103.81282), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.671851), 'val_loss': np.float64(96794.877878)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.234776), 'test_avg_loss_bottom_decile': np.float64(15.860441), 'test_avg_loss_top_decile': np.float64(22.754454), 'test_avg_loss_min': np.float64(15.5126), 'test_avg_loss_max': np.float64(22.754454), 'test_avg_loss_bottom10%': np.float64(15.5126), 'test_avg_loss_top10%': np.float64(22.754454), 'test_avg_loss_cos1': np.float64(0.992958), 'test_avg_loss_entropy': np.float64(2.295492), 'test_loss_std': np.float64(11585.078634), 'test_loss_bottom_decile': np.float64(82220.527435), 'test_loss_top_decile': np.float64(117959.087158), 'test_loss_min': np.float64(80417.316498), 'test_loss_max': np.float64(117959.087158), 'test_loss_bottom10%': np.float64(80417.316498), 'test_loss_top10%': np.float64(117959.087158), 'test_loss_cos1': np.float64(0.992958), 'test_loss_entropy': np.float64(2.295492), 'val_avg_loss_std': np.float64(2.465944), 'val_avg_loss_bottom_decile': np.float64(15.481054), 'val_avg_loss_top_decile': np.float64(22.481036), 'val_avg_loss_min': np.float64(14.957542), 'val_avg_loss_max': np.float64(22.481036), 'val_avg_loss_bottom10%': np.float64(14.957542), 'val_avg_loss_top10%': np.float64(22.481036), 'val_avg_loss_cos1': np.float64(0.991392), 'val_avg_loss_entropy': np.float64(2.293824), 'val_loss_std': np.float64(12783.454209), 'val_loss_bottom_decile': np.float64(80253.782684), 'val_loss_top_decile': np.float64(116541.690002), 'val_loss_min': np.float64(77539.897186), 'val_loss_max': np.float64(116541.690002), 'val_loss_bottom10%': np.float64(77539.897186), 'val_loss_top10%': np.float64(116541.690002), 'val_loss_cos1': np.float64(0.991392), 'val_loss_entropy': np.float64(2.293824)}}
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2024-10-15 05:11:17,433 (server:353) INFO: Server: Starting evaluation at the end of round 96.
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2024-10-15 05:11:17,433 (server:359) INFO: ----------- Starting a new training round (Round #97) -------------
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2024-10-15 05:13:40,578 (client:354) INFO: {'Role': 'Client #9', 'Round': 97, 'Results_raw': {'train_loss': 17.273981, 'val_loss': 17.555832, 'test_loss': 18.440986}}
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2024-10-15 05:14:37,028 (client:354) INFO: {'Role': 'Client #6', 'Round': 97, 'Results_raw': {'train_loss': 14.593231, 'val_loss': 14.798936, 'test_loss': 16.425535}}
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2024-10-15 05:15:33,725 (client:354) INFO: {'Role': 'Client #1', 'Round': 97, 'Results_raw': {'train_loss': 10.27909, 'val_loss': 10.277319, 'test_loss': 11.455233}}
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2024-10-15 05:16:29,207 (client:354) INFO: {'Role': 'Client #8', 'Round': 97, 'Results_raw': {'train_loss': 12.853574, 'val_loss': 13.102614, 'test_loss': 13.871763}}
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2024-10-15 05:17:26,181 (client:354) INFO: {'Role': 'Client #7', 'Round': 97, 'Results_raw': {'train_loss': 14.861477, 'val_loss': 15.377936, 'test_loss': 16.239943}}
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2024-10-15 05:18:22,982 (client:354) INFO: {'Role': 'Client #4', 'Round': 97, 'Results_raw': {'train_loss': 14.581159, 'val_loss': 15.078473, 'test_loss': 16.613429}}
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2024-10-15 05:19:18,750 (client:354) INFO: {'Role': 'Client #2', 'Round': 97, 'Results_raw': {'train_loss': 8.392556, 'val_loss': 8.362707, 'test_loss': 8.977493}}
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2024-10-15 05:20:15,906 (client:354) INFO: {'Role': 'Client #5', 'Round': 97, 'Results_raw': {'train_loss': 15.387763, 'val_loss': 16.512316, 'test_loss': 18.823983}}
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2024-10-15 05:21:12,113 (client:354) INFO: {'Role': 'Client #3', 'Round': 97, 'Results_raw': {'train_loss': 9.68117, 'val_loss': 10.536387, 'test_loss': 12.047163}}
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2024-10-15 05:22:08,610 (client:354) INFO: {'Role': 'Client #10', 'Round': 97, 'Results_raw': {'train_loss': 14.512422, 'val_loss': 15.247401, 'test_loss': 17.052357}}
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2024-10-15 05:22:08,615 (server:615) INFO: {'Role': 'Server #', 'Round': 96, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.676564), 'test_loss': np.float64(96819.305402), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.631845), 'val_loss': np.float64(96587.481998)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.676564), 'test_loss': np.float64(96819.305402), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.631845), 'val_loss': np.float64(96587.481998)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.281695), 'test_avg_loss_bottom_decile': np.float64(15.733053), 'test_avg_loss_top_decile': np.float64(22.808153), 'test_avg_loss_min': np.float64(15.424282), 'test_avg_loss_max': np.float64(22.808153), 'test_avg_loss_bottom10%': np.float64(15.424282), 'test_avg_loss_top10%': np.float64(22.808153), 'test_avg_loss_cos1': np.float64(0.99262), 'test_avg_loss_entropy': np.float64(2.295148), 'test_loss_std': np.float64(11828.306709), 'test_loss_bottom_decile': np.float64(81560.148926), 'test_loss_top_decile': np.float64(118237.463684), 'test_loss_min': np.float64(79959.479889), 'test_loss_max': np.float64(118237.463684), 'test_loss_bottom10%': np.float64(79959.479889), 'test_loss_top10%': np.float64(118237.463684), 'test_loss_cos1': np.float64(0.99262), 'test_loss_entropy': np.float64(2.295148), 'val_avg_loss_std': np.float64(2.515966), 'val_avg_loss_bottom_decile': np.float64(15.385277), 'val_avg_loss_top_decile': np.float64(22.567408), 'val_avg_loss_min': np.float64(14.877241), 'val_avg_loss_max': np.float64(22.567408), 'val_avg_loss_bottom10%': np.float64(14.877241), 'val_avg_loss_top10%': np.float64(22.567408), 'val_avg_loss_cos1': np.float64(0.991005), 'val_avg_loss_entropy': np.float64(2.293425), 'val_loss_std': np.float64(13042.770238), 'val_loss_bottom_decile': np.float64(79757.274139), 'val_loss_top_decile': np.float64(116989.44165), 'val_loss_min': np.float64(77123.617249), 'val_loss_max': np.float64(116989.44165), 'val_loss_bottom10%': np.float64(77123.617249), 'val_loss_top10%': np.float64(116989.44165), 'val_loss_cos1': np.float64(0.991005), 'val_loss_entropy': np.float64(2.293425)}}
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2024-10-15 05:22:08,655 (server:353) INFO: Server: Starting evaluation at the end of round 97.
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2024-10-15 05:22:08,656 (server:359) INFO: ----------- Starting a new training round (Round #98) -------------
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2024-10-15 05:24:33,951 (client:354) INFO: {'Role': 'Client #4', 'Round': 98, 'Results_raw': {'train_loss': 14.556014, 'val_loss': 15.025766, 'test_loss': 16.535044}}
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2024-10-15 05:25:31,955 (client:354) INFO: {'Role': 'Client #10', 'Round': 98, 'Results_raw': {'train_loss': 14.506945, 'val_loss': 15.258287, 'test_loss': 17.032546}}
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2024-10-15 05:26:27,128 (client:354) INFO: {'Role': 'Client #7', 'Round': 98, 'Results_raw': {'train_loss': 14.853767, 'val_loss': 15.502684, 'test_loss': 16.412351}}
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2024-10-15 05:27:24,503 (client:354) INFO: {'Role': 'Client #5', 'Round': 98, 'Results_raw': {'train_loss': 15.377945, 'val_loss': 16.552442, 'test_loss': 18.753903}}
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2024-10-15 05:28:21,584 (client:354) INFO: {'Role': 'Client #2', 'Round': 98, 'Results_raw': {'train_loss': 8.37205, 'val_loss': 8.434789, 'test_loss': 9.182793}}
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2024-10-15 05:29:18,402 (client:354) INFO: {'Role': 'Client #6', 'Round': 98, 'Results_raw': {'train_loss': 14.609089, 'val_loss': 14.746563, 'test_loss': 16.187394}}
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2024-10-15 05:30:14,553 (client:354) INFO: {'Role': 'Client #3', 'Round': 98, 'Results_raw': {'train_loss': 9.696235, 'val_loss': 10.480972, 'test_loss': 11.992566}}
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2024-10-15 05:31:09,104 (client:354) INFO: {'Role': 'Client #1', 'Round': 98, 'Results_raw': {'train_loss': 10.306311, 'val_loss': 10.305804, 'test_loss': 11.420617}}
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2024-10-15 05:32:04,613 (client:354) INFO: {'Role': 'Client #9', 'Round': 98, 'Results_raw': {'train_loss': 17.287785, 'val_loss': 17.470668, 'test_loss': 18.254567}}
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2024-10-15 05:33:02,481 (client:354) INFO: {'Role': 'Client #8', 'Round': 98, 'Results_raw': {'train_loss': 12.856826, 'val_loss': 13.150797, 'test_loss': 13.782068}}
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2024-10-15 05:33:02,485 (server:615) INFO: {'Role': 'Server #', 'Round': 97, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.771565), 'test_loss': np.float64(97311.794815), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.708034), 'val_loss': np.float64(96982.450528)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.771565), 'test_loss': np.float64(97311.794815), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.708034), 'val_loss': np.float64(96982.450528)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.221588), 'test_avg_loss_bottom_decile': np.float64(15.945766), 'test_avg_loss_top_decile': np.float64(22.846406), 'test_avg_loss_min': np.float64(15.603586), 'test_avg_loss_max': np.float64(22.846406), 'test_avg_loss_bottom10%': np.float64(15.603586), 'test_avg_loss_top10%': np.float64(22.846406), 'test_avg_loss_cos1': np.float64(0.99307), 'test_avg_loss_entropy': np.float64(2.295614), 'test_loss_std': np.float64(11516.714458), 'test_loss_bottom_decile': np.float64(82662.850647), 'test_loss_top_decile': np.float64(118435.767212), 'test_loss_min': np.float64(80888.990753), 'test_loss_max': np.float64(118435.767212), 'test_loss_bottom10%': np.float64(80888.990753), 'test_loss_top10%': np.float64(118435.767212), 'test_loss_cos1': np.float64(0.99307), 'test_loss_entropy': np.float64(2.295614), 'val_avg_loss_std': np.float64(2.442864), 'val_avg_loss_bottom_decile': np.float64(15.582067), 'val_avg_loss_top_decile': np.float64(22.563354), 'val_avg_loss_min': np.float64(15.051667), 'val_avg_loss_max': np.float64(22.563354), 'val_avg_loss_bottom10%': np.float64(15.051667), 'val_avg_loss_top10%': np.float64(22.563354), 'val_avg_loss_cos1': np.float64(0.991582), 'val_avg_loss_entropy': np.float64(2.294031), 'val_loss_std': np.float64(12663.809382), 'val_loss_bottom_decile': np.float64(80777.437378), 'val_loss_top_decile': np.float64(116968.425049), 'val_loss_min': np.float64(78027.839722), 'val_loss_max': np.float64(116968.425049), 'val_loss_bottom10%': np.float64(78027.839722), 'val_loss_top10%': np.float64(116968.425049), 'val_loss_cos1': np.float64(0.991582), 'val_loss_entropy': np.float64(2.294031)}}
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2024-10-15 05:33:02,523 (server:353) INFO: Server: Starting evaluation at the end of round 98.
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2024-10-15 05:33:02,523 (server:359) INFO: ----------- Starting a new training round (Round #99) -------------
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2024-10-15 05:35:28,915 (client:354) INFO: {'Role': 'Client #1', 'Round': 99, 'Results_raw': {'train_loss': 10.276709, 'val_loss': 10.3479, 'test_loss': 11.517975}}
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2024-10-15 05:36:24,134 (client:354) INFO: {'Role': 'Client #5', 'Round': 99, 'Results_raw': {'train_loss': 15.395025, 'val_loss': 16.63014, 'test_loss': 18.822538}}
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2024-10-15 05:37:16,016 (client:354) INFO: {'Role': 'Client #10', 'Round': 99, 'Results_raw': {'train_loss': 14.497291, 'val_loss': 15.264517, 'test_loss': 16.960415}}
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2024-10-15 05:38:10,579 (client:354) INFO: {'Role': 'Client #3', 'Round': 99, 'Results_raw': {'train_loss': 9.710195, 'val_loss': 10.52511, 'test_loss': 11.965112}}
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2024-10-15 05:39:03,937 (client:354) INFO: {'Role': 'Client #4', 'Round': 99, 'Results_raw': {'train_loss': 14.603856, 'val_loss': 14.936955, 'test_loss': 16.236173}}
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2024-10-15 05:39:59,892 (client:354) INFO: {'Role': 'Client #9', 'Round': 99, 'Results_raw': {'train_loss': 17.56718, 'val_loss': 17.671416, 'test_loss': 18.287093}}
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2024-10-15 05:40:56,709 (client:354) INFO: {'Role': 'Client #8', 'Round': 99, 'Results_raw': {'train_loss': 12.880477, 'val_loss': 13.149696, 'test_loss': 13.920585}}
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2024-10-15 05:41:55,902 (client:354) INFO: {'Role': 'Client #2', 'Round': 99, 'Results_raw': {'train_loss': 8.380223, 'val_loss': 8.377862, 'test_loss': 9.011763}}
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2024-10-15 05:42:51,510 (client:354) INFO: {'Role': 'Client #6', 'Round': 99, 'Results_raw': {'train_loss': 14.603967, 'val_loss': 14.683855, 'test_loss': 15.986962}}
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2024-10-15 05:43:46,863 (client:354) INFO: {'Role': 'Client #7', 'Round': 99, 'Results_raw': {'train_loss': 14.866234, 'val_loss': 15.30517, 'test_loss': 16.005159}}
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2024-10-15 05:43:46,868 (server:615) INFO: {'Role': 'Server #', 'Round': 98, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.663945), 'test_loss': np.float64(96753.892105), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.627945), 'val_loss': np.float64(96567.267606)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.663945), 'test_loss': np.float64(96753.892105), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.627945), 'val_loss': np.float64(96567.267606)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.302846), 'test_avg_loss_bottom_decile': np.float64(15.65821), 'test_avg_loss_top_decile': np.float64(22.792546), 'test_avg_loss_min': np.float64(15.410179), 'test_avg_loss_max': np.float64(22.792546), 'test_avg_loss_bottom10%': np.float64(15.410179), 'test_avg_loss_top10%': np.float64(22.792546), 'test_avg_loss_cos1': np.float64(0.992474), 'test_avg_loss_entropy': np.float64(2.294995), 'test_loss_std': np.float64(11937.953607), 'test_loss_bottom_decile': np.float64(81172.162384), 'test_loss_top_decile': np.float64(118156.557373), 'test_loss_min': np.float64(79886.365906), 'test_loss_max': np.float64(118156.557373), 'test_loss_bottom10%': np.float64(79886.365906), 'test_loss_top10%': np.float64(118156.557373), 'test_loss_cos1': np.float64(0.992474), 'test_loss_entropy': np.float64(2.294995), 'val_avg_loss_std': np.float64(2.550069), 'val_avg_loss_bottom_decile': np.float64(15.291614), 'val_avg_loss_top_decile': np.float64(22.551077), 'val_avg_loss_min': np.float64(14.848422), 'val_avg_loss_max': np.float64(22.551077), 'val_avg_loss_bottom10%': np.float64(14.848422), 'val_avg_loss_top10%': np.float64(22.551077), 'val_avg_loss_cos1': np.float64(0.99076), 'val_avg_loss_entropy': np.float64(2.293164), 'val_loss_std': np.float64(13219.558789), 'val_loss_bottom_decile': np.float64(79271.727448), 'val_loss_top_decile': np.float64(116904.784241), 'val_loss_min': np.float64(76974.217194), 'val_loss_max': np.float64(116904.784241), 'val_loss_bottom10%': np.float64(76974.217194), 'val_loss_top10%': np.float64(116904.784241), 'val_loss_cos1': np.float64(0.99076), 'val_loss_entropy': np.float64(2.293164)}}
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2024-10-15 05:43:46,909 (server:370) INFO: Server: Training is finished! Starting evaluation.
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2024-10-15 05:45:12,769 (server:615) INFO: {'Role': 'Server #', 'Round': 99, 'Results_weighted_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.610223), 'test_loss': np.float64(96475.395718), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.595169), 'val_loss': np.float64(96397.358389)}, 'Results_avg': {'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.610223), 'test_loss': np.float64(96475.395718), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.595169), 'val_loss': np.float64(96397.358389)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(2.383978), 'test_avg_loss_bottom_decile': np.float64(15.331566), 'test_avg_loss_top_decile': np.float64(22.804504), 'test_avg_loss_min': np.float64(15.314442), 'test_avg_loss_max': np.float64(22.804504), 'test_avg_loss_bottom10%': np.float64(15.314442), 'test_avg_loss_top10%': np.float64(22.804504), 'test_avg_loss_cos1': np.float64(0.991895), 'test_avg_loss_entropy': np.float64(2.294386), 'test_loss_std': np.float64(12358.543424), 'test_loss_bottom_decile': np.float64(79478.838013), 'test_loss_top_decile': np.float64(118218.549438), 'test_loss_min': np.float64(79390.065399), 'test_loss_max': np.float64(118218.549438), 'test_loss_bottom10%': np.float64(79390.065399), 'test_loss_top10%': np.float64(118218.549438), 'test_loss_cos1': np.float64(0.991895), 'test_loss_entropy': np.float64(2.294386), 'val_avg_loss_std': np.float64(2.625896), 'val_avg_loss_bottom_decile': np.float64(14.988998), 'val_avg_loss_top_decile': np.float64(22.554756), 'val_avg_loss_min': np.float64(14.800723), 'val_avg_loss_max': np.float64(22.554756), 'val_avg_loss_bottom10%': np.float64(14.800723), 'val_avg_loss_top10%': np.float64(22.554756), 'val_avg_loss_cos1': np.float64(0.990176), 'val_avg_loss_entropy': np.float64(2.29254), 'val_loss_std': np.float64(13612.645189), 'val_loss_bottom_decile': np.float64(77702.967468), 'val_loss_top_decile': np.float64(116923.85675), 'val_loss_min': np.float64(76726.948273), 'val_loss_max': np.float64(116923.85675), 'val_loss_bottom10%': np.float64(76726.948273), 'val_loss_top10%': np.float64(116923.85675), 'val_loss_cos1': np.float64(0.990176), 'val_loss_entropy': np.float64(2.29254)}}
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2024-10-15 05:45:12,771 (server:420) INFO: Server: Final evaluation is finished! Starting merging results.
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2024-10-15 05:45:12,772 (server:546) INFO: {'Role': 'Server #', 'Round': 'Final', 'Results_raw': {'client_best_individual': {'val_loss': 76726.948273, 'test_total': 5184.0, 'test_avg_loss': 15.314442, 'test_loss': 79390.065399, 'val_total': 5184.0, 'val_avg_loss': 14.800723}, 'client_summarized_weighted_avg': {'val_loss': np.float64(96397.358389), 'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.610223), 'test_loss': np.float64(96475.395718), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.595169)}, 'client_summarized_avg': {'val_loss': np.float64(96397.358389), 'test_total': np.float64(5184.0), 'test_avg_loss': np.float64(18.610223), 'test_loss': np.float64(96475.395718), 'val_total': np.float64(5184.0), 'val_avg_loss': np.float64(18.595169)}, 'client_summarized_fairness': {'val_loss_entropy': np.float64(2.28173), 'val_loss_cos1': np.float64(0.98107), 'val_loss_top10%': np.float64(189398.18103), 'val_loss_bottom10%': np.float64(94814.290771), 'val_loss_max': np.float64(189398.18103), 'val_loss_min': np.float64(94814.290771), 'val_loss_top_decile': np.float64(189398.18103), 'val_loss_bottom_decile': np.float64(121564.941711), 'val_loss_std': np.float64(31024.129386), 'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.419558), 'test_avg_loss_bottom_decile': np.float64(23.170059), 'test_avg_loss_top_decile': np.float64(35.499718), 'test_avg_loss_min': np.float64(18.069055), 'test_avg_loss_max': np.float64(35.499718), 'test_avg_loss_bottom10%': np.float64(18.069055), 'test_avg_loss_top10%': np.float64(35.499718), 'test_avg_loss_cos1': np.float64(0.983329), 'test_avg_loss_entropy': np.float64(2.284346), 'test_loss_std': np.float64(28094.990754), 'test_loss_bottom_decile': np.float64(120113.584106), 'test_loss_top_decile': np.float64(184030.536865), 'test_loss_min': np.float64(93669.979675), 'test_loss_max': np.float64(184030.536865), 'test_loss_bottom10%': np.float64(93669.979675), 'test_loss_top10%': np.float64(184030.536865), 'test_loss_cos1': np.float64(0.983329), 'test_loss_entropy': np.float64(2.284346), 'val_avg_loss_std': np.float64(5.984593), 'val_avg_loss_bottom_decile': np.float64(23.450027), 'val_avg_loss_top_decile': np.float64(36.535143), 'val_avg_loss_min': np.float64(18.289794), 'val_avg_loss_max': np.float64(36.535143), 'val_avg_loss_bottom10%': np.float64(18.289794), 'val_avg_loss_top10%': np.float64(36.535143), 'val_avg_loss_cos1': np.float64(0.98107), 'val_avg_loss_entropy': np.float64(2.28173)}}}
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2024-10-15 05:45:12,774 (server:565) INFO: {'Role': 'Client #1', 'Round': 100, 'Results_raw': {'test_total': 5184, 'test_avg_loss': 15.314442, 'test_loss': 79390.065399, 'val_total': 5184, 'val_avg_loss': 14.800723, 'val_loss': 76726.948273}}
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2024-10-15 05:45:12,775 (server:565) INFO: {'Role': 'Client #2', 'Round': 100, 'Results_raw': {'test_total': 5184, 'test_avg_loss': 15.331566, 'test_loss': 79478.838013, 'val_total': 5184, 'val_avg_loss': 14.988998, 'val_loss': 77702.967468}}
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2024-10-15 05:45:12,775 (server:565) INFO: {'Role': 'Client #3', 'Round': 100, 'Results_raw': {'test_total': 5184, 'test_avg_loss': 16.702318, 'test_loss': 86584.814148, 'val_total': 5184, 'val_avg_loss': 16.124792, 'val_loss': 83590.922119}}
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2024-10-15 05:45:12,775 (server:565) INFO: {'Role': 'Client #4', 'Round': 100, 'Results_raw': {'test_total': 5184, 'test_avg_loss': 18.987576, 'test_loss': 98431.594177, 'val_total': 5184, 'val_avg_loss': 19.134374, 'val_loss': 99192.594299}}
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2024-10-15 05:45:12,776 (server:565) INFO: {'Role': 'Client #5', 'Round': 100, 'Results_raw': {'test_total': 5184, 'test_avg_loss': 22.804504, 'test_loss': 118218.549438, 'val_total': 5184, 'val_avg_loss': 22.554756, 'val_loss': 116923.85675}}
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2024-10-15 05:45:12,776 (server:565) INFO: {'Role': 'Client #6', 'Round': 100, 'Results_raw': {'test_total': 5184, 'test_avg_loss': 19.070057, 'test_loss': 98859.17511, 'val_total': 5184, 'val_avg_loss': 19.39368, 'val_loss': 100536.834717}}
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2024-10-15 05:45:12,776 (server:565) INFO: {'Role': 'Client #7', 'Round': 100, 'Results_raw': {'test_total': 5184, 'test_avg_loss': 19.974233, 'test_loss': 103546.425598, 'val_total': 5184, 'val_avg_loss': 20.614219, 'val_loss': 106864.109619}}
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2024-10-15 05:45:12,777 (server:565) INFO: {'Role': 'Client #8', 'Round': 100, 'Results_raw': {'test_total': 5184, 'test_avg_loss': 16.976659, 'test_loss': 88007.000183, 'val_total': 5184, 'val_avg_loss': 17.099369, 'val_loss': 88643.12793}}
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2024-10-15 05:45:12,777 (server:565) INFO: {'Role': 'Client #9', 'Round': 100, 'Results_raw': {'test_total': 5184, 'test_avg_loss': 21.515896, 'test_loss': 111538.404358, 'val_total': 5184, 'val_avg_loss': 22.090855, 'val_loss': 114518.993103}}
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2024-10-15 05:45:12,777 (server:565) INFO: {'Role': 'Client #10', 'Round': 100, 'Results_raw': {'test_total': 5184, 'test_avg_loss': 19.424979, 'test_loss': 100699.090759, 'val_total': 5184, 'val_avg_loss': 19.149929, 'val_loss': 99273.229614}}
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2024-10-15 05:45:12,778 (monitor:173) INFO: In worker #0, the system-related metrics are: {'id': 0, 'fl_end_time_minutes': 1122.815127, 'total_model_size': 0, 'total_flops': 0, 'total_upload_bytes': 0, 'total_download_bytes': 16839256, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,788 (client:582) INFO: ================= client 1 received finish message =================
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2024-10-15 05:45:12,791 (monitor:173) INFO: In worker #1, the system-related metrics are: {'id': 1, 'fl_end_time_minutes': 1122.815041, 'total_model_size': 563814, 'total_flops': 60414295200000.0, 'total_upload_bytes': 0, 'total_download_bytes': 3707200, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,791 (client:582) INFO: ================= client 2 received finish message =================
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2024-10-15 05:45:12,794 (monitor:173) INFO: In worker #2, the system-related metrics are: {'id': 2, 'fl_end_time_minutes': 1122.814673, 'total_model_size': 563814, 'total_flops': 60414295200000.0, 'total_upload_bytes': 0, 'total_download_bytes': 3707200, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,794 (client:582) INFO: ================= client 3 received finish message =================
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2024-10-15 05:45:12,797 (monitor:173) INFO: In worker #3, the system-related metrics are: {'id': 3, 'fl_end_time_minutes': 1122.814364, 'total_model_size': 563814, 'total_flops': 60414295200000.0, 'total_upload_bytes': 0, 'total_download_bytes': 3707200, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,797 (client:582) INFO: ================= client 4 received finish message =================
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2024-10-15 05:45:12,800 (monitor:173) INFO: In worker #4, the system-related metrics are: {'id': 4, 'fl_end_time_minutes': 1122.814081, 'total_model_size': 563814, 'total_flops': 60414295200000.0, 'total_upload_bytes': 0, 'total_download_bytes': 3707200, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,800 (client:582) INFO: ================= client 5 received finish message =================
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2024-10-15 05:45:12,803 (monitor:173) INFO: In worker #5, the system-related metrics are: {'id': 5, 'fl_end_time_minutes': 1122.813778, 'total_model_size': 563814, 'total_flops': 60414295200000.0, 'total_upload_bytes': 0, 'total_download_bytes': 3707200, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,803 (client:582) INFO: ================= client 6 received finish message =================
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2024-10-15 05:45:12,805 (monitor:173) INFO: In worker #6, the system-related metrics are: {'id': 6, 'fl_end_time_minutes': 1122.81352, 'total_model_size': 563814, 'total_flops': 60414295200000.0, 'total_upload_bytes': 0, 'total_download_bytes': 3707200, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,805 (client:582) INFO: ================= client 7 received finish message =================
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2024-10-15 05:45:12,807 (monitor:173) INFO: In worker #7, the system-related metrics are: {'id': 7, 'fl_end_time_minutes': 1122.813225, 'total_model_size': 563814, 'total_flops': 60414295200000.0, 'total_upload_bytes': 0, 'total_download_bytes': 3707200, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,807 (client:582) INFO: ================= client 8 received finish message =================
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2024-10-15 05:45:12,810 (monitor:173) INFO: In worker #8, the system-related metrics are: {'id': 8, 'fl_end_time_minutes': 1122.81296, 'total_model_size': 563814, 'total_flops': 60414295200000.0, 'total_upload_bytes': 0, 'total_download_bytes': 3707200, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,810 (client:582) INFO: ================= client 9 received finish message =================
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2024-10-15 05:45:12,812 (monitor:173) INFO: In worker #9, the system-related metrics are: {'id': 9, 'fl_end_time_minutes': 1122.812682, 'total_model_size': 563814, 'total_flops': 60414295200000.0, 'total_upload_bytes': 0, 'total_download_bytes': 3707200, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,812 (client:582) INFO: ================= client 10 received finish message =================
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2024-10-15 05:45:12,814 (monitor:173) INFO: In worker #10, the system-related metrics are: {'id': 10, 'fl_end_time_minutes': 1122.812326, 'total_model_size': 563814, 'total_flops': 60414295200000.0, 'total_upload_bytes': 0, 'total_download_bytes': 3707200, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-10-15 05:45:12,814 (monitor:338) INFO: We will compress the file eval_results.raw into a .gz file, and delete the old one
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2024-10-15 05:45:12,863 (monitor:246) INFO: After merging the system metrics from all works, we got avg: defaultdict(None, {'id': 'sys_avg', 'sys_avg/fl_end_time_minutes': np.float64(1122.813798), 'sys_avg/total_model_size': '500.55K', 'sys_avg/total_flops': '49.95T', 'sys_avg/total_upload_bytes': '0.0', 'sys_avg/total_download_bytes': '4.67M', 'sys_avg/global_convergence_round': np.float64(0.0), 'sys_avg/local_convergence_round': np.float64(0.0), 'sys_avg/global_convergence_time_minutes': np.float64(0.0), 'sys_avg/local_convergence_time_minutes': np.float64(0.0)})
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2024-10-15 05:45:12,863 (monitor:249) INFO: After merging the system metrics from all works, we got std: defaultdict(None, {'id': 'sys_std', 'sys_std/fl_end_time_minutes': np.float64(0.000904), 'sys_std/total_model_size': '158.29K', 'sys_std/total_flops': '15.8T', 'sys_std/total_upload_bytes': '0.0', 'sys_std/total_download_bytes': '3.6M', 'sys_std/global_convergence_round': np.float64(0.0), 'sys_std/local_convergence_round': np.float64(0.0), 'sys_std/global_convergence_time_minutes': np.float64(0.0), 'sys_std/local_convergence_time_minutes': np.float64(0.0)})
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