1414 lines
308 KiB
Plaintext
1414 lines
308 KiB
Plaintext
2024-11-13 16:42:51,402 (logging:124) INFO: the current machine is at 127.0.1.1
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2024-11-13 16:42:51,403 (logging:126) INFO: the current dir is /home/czzhangheng/code/FederatedScope
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2024-11-13 16:42:51,403 (logging:127) INFO: the output dir is exp/FedAvg_FedDGCN_on_trafficflow_lr0.003_lstep1/sub_exp_20241113164251
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2024-11-13 16:43:06,917 (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: MAPE
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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: 307
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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/PeMS04
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save_data: False
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scaler: [207.236632, 156.479362]
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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: 0
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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: 60
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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_FedDGCN_on_trafficflow_lr0.003_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: 70
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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: 30
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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: FedDGCN
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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_FedDGCN_on_trafficflow_lr0.003_lstep1/sub_exp_20241113164251
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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: 0.003
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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: 0.003
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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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2024-11-13 16:43:07,067 (utils:147) INFO: The device information file is not provided
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2024-11-13 16:43:07,129 (fed_runner:173) INFO: Server has been set up ...
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2024-11-13 16:43:07,159 (fed_runner:225) INFO: Client 1 has been set up ...
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2024-11-13 16:43:07,188 (fed_runner:225) INFO: Client 2 has been set up ...
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2024-11-13 16:43:07,219 (fed_runner:225) INFO: Client 3 has been set up ...
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2024-11-13 16:43:07,246 (fed_runner:225) INFO: Client 4 has been set up ...
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2024-11-13 16:43:07,267 (fed_runner:225) INFO: Client 5 has been set up ...
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2024-11-13 16:43:07,289 (fed_runner:225) INFO: Client 6 has been set up ...
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2024-11-13 16:43:07,308 (fed_runner:225) INFO: Client 7 has been set up ...
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2024-11-13 16:43:07,326 (fed_runner:225) INFO: Client 8 has been set up ...
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2024-11-13 16:43:07,347 (fed_runner:225) INFO: Client 9 has been set up ...
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2024-11-13 16:43:07,364 (fed_runner:225) INFO: Client 10 has been set up ...
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2024-11-13 16:43:07,364 (trainer:345) INFO: Model meta-info: <class 'federatedscope.trafficflow.model.FedDGCN.FedDGCN'>.
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2024-11-13 16:43:07,365 (trainer:353) INFO: Num of original para names: 50.
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2024-11-13 16:43:07,365 (trainer:354) INFO: Num of original trainable para names: 50.
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2024-11-13 16:43:07,365 (trainer:356) INFO: Num of preserved para names in local update: 50.
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Preserved para names in local update: {'encoder2.DGCRM_cells.0.gate.fc.fc3.weight', 'encoder2.DGCRM_cells.0.update.fc.fc3.bias', 'encoder1.DGCRM_cells.0.gate.bias_pool', 'encoder1.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder1.DGCRM_cells.0.update.fc.fc3.bias', 'encoder1.DGCRM_cells.0.update.fc.fc2.bias', 'encoder1.DGCRM_cells.0.update.fc.fc1.bias', 'encoder1.DGCRM_cells.0.gate.bias', 'node_embeddings1', 'encoder1.DGCRM_cells.0.gate.fc.fc3.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder1.DGCRM_cells.0.gate.weights', 'encoder2.DGCRM_cells.0.update.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc2.bias', 'T_i_D_emb', 'encoder2.DGCRM_cells.0.gate.weights_pool', 'encoder1.DGCRM_cells.0.gate.weights_pool', 'encoder2.DGCRM_cells.0.update.weights', 'encoder2.DGCRM_cells.0.update.fc.fc3.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc2.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder2.DGCRM_cells.0.update.bias_pool', 'encoder2.DGCRM_cells.0.update.fc.fc2.bias', 'end_conv2.weight', 'encoder2.DGCRM_cells.0.gate.weights', 'encoder1.DGCRM_cells.0.update.weights_pool', 'encoder1.DGCRM_cells.0.update.fc.fc1.weight', 'encoder1.DGCRM_cells.0.update.weights', 'encoder2.DGCRM_cells.0.update.fc.fc2.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc2.weight', 'encoder2.DGCRM_cells.0.update.fc.fc1.bias', 'encoder2.DGCRM_cells.0.update.fc.fc1.weight', 'end_conv1.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc3.bias', 'encoder2.DGCRM_cells.0.gate.bias', 'end_conv3.weight', 'encoder1.DGCRM_cells.0.update.bias_pool', 'end_conv2.bias', 'encoder1.DGCRM_cells.0.update.fc.fc3.weight', 'D_i_W_emb', 'encoder1.DGCRM_cells.0.update.bias', 'encoder2.DGCRM_cells.0.gate.bias_pool', 'encoder2.DGCRM_cells.0.update.weights_pool', 'node_embeddings2', 'end_conv3.bias', 'encoder1.DGCRM_cells.0.update.fc.fc2.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc3.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc2.bias', 'end_conv1.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc1.weight'}.
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2024-11-13 16:43:07,366 (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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2024-11-13 16:43:07,366 (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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2024-11-13 16:43:07,379 (server:843) INFO: ----------- Starting training (Round #0) -------------
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2024-11-13 16:44:09,521 (client:354) INFO: {'Role': 'Client #9', 'Round': 0, 'Results_raw': {'train_loss': 46.451783, 'val_loss': 18.475754, 'test_loss': 18.000184}}
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2024-11-13 16:45:10,303 (client:354) INFO: {'Role': 'Client #3', 'Round': 0, 'Results_raw': {'train_loss': 48.160099, 'val_loss': 18.204354, 'test_loss': 17.775087}}
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2024-11-13 16:46:08,980 (client:354) INFO: {'Role': 'Client #6', 'Round': 0, 'Results_raw': {'train_loss': 25.845822, 'val_loss': 10.237712, 'test_loss': 10.268221}}
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2024-11-13 16:47:09,758 (client:354) INFO: {'Role': 'Client #7', 'Round': 0, 'Results_raw': {'train_loss': 43.185894, 'val_loss': 16.358304, 'test_loss': 16.159459}}
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2024-11-13 16:48:09,564 (client:354) INFO: {'Role': 'Client #4', 'Round': 0, 'Results_raw': {'train_loss': 43.983491, 'val_loss': 17.45475, 'test_loss': 17.529081}}
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2024-11-13 16:49:08,859 (client:354) INFO: {'Role': 'Client #2', 'Round': 0, 'Results_raw': {'train_loss': 46.788515, 'val_loss': 19.188663, 'test_loss': 19.489936}}
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2024-11-13 16:50:08,958 (client:354) INFO: {'Role': 'Client #1', 'Round': 0, 'Results_raw': {'train_loss': 50.575151, 'val_loss': 21.14243, 'test_loss': 20.020398}}
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2024-11-13 16:51:09,083 (client:354) INFO: {'Role': 'Client #8', 'Round': 0, 'Results_raw': {'train_loss': 54.475473, 'val_loss': 20.593844, 'test_loss': 19.865156}}
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2024-11-13 16:52:12,591 (client:354) INFO: {'Role': 'Client #5', 'Round': 0, 'Results_raw': {'train_loss': 41.206609, 'val_loss': 16.318364, 'test_loss': 16.343421}}
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2024-11-13 16:53:03,148 (client:354) INFO: {'Role': 'Client #10', 'Round': 0, 'Results_raw': {'train_loss': 53.33829, 'val_loss': 21.328812, 'test_loss': 21.1475}}
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2024-11-13 16:53:03,190 (server:353) INFO: Server: Starting evaluation at the end of round 0.
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2024-11-13 16:53:03,190 (server:359) INFO: ----------- Starting a new training round (Round #1) -------------
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2024-11-13 16:54:46,166 (client:354) INFO: {'Role': 'Client #6', 'Round': 1, 'Results_raw': {'train_loss': 12.032344, 'val_loss': 9.735609, 'test_loss': 9.705492}}
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2024-11-13 16:55:22,489 (client:354) INFO: {'Role': 'Client #4', 'Round': 1, 'Results_raw': {'train_loss': 20.972378, 'val_loss': 16.269941, 'test_loss': 16.394812}}
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2024-11-13 16:55:59,872 (client:354) INFO: {'Role': 'Client #5', 'Round': 1, 'Results_raw': {'train_loss': 18.509239, 'val_loss': 14.761459, 'test_loss': 14.899855}}
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2024-11-13 16:56:39,051 (client:354) INFO: {'Role': 'Client #8', 'Round': 1, 'Results_raw': {'train_loss': 23.224056, 'val_loss': 17.072786, 'test_loss': 16.166018}}
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2024-11-13 16:57:16,031 (client:354) INFO: {'Role': 'Client #7', 'Round': 1, 'Results_raw': {'train_loss': 17.203584, 'val_loss': 13.428783, 'test_loss': 13.249525}}
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2024-11-13 16:57:52,831 (client:354) INFO: {'Role': 'Client #9', 'Round': 1, 'Results_raw': {'train_loss': 19.683591, 'val_loss': 16.505848, 'test_loss': 15.841738}}
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2024-11-13 16:58:30,639 (client:354) INFO: {'Role': 'Client #10', 'Round': 1, 'Results_raw': {'train_loss': 23.567477, 'val_loss': 18.239941, 'test_loss': 18.043762}}
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2024-11-13 16:59:07,892 (client:354) INFO: {'Role': 'Client #3', 'Round': 1, 'Results_raw': {'train_loss': 20.298349, 'val_loss': 15.568667, 'test_loss': 15.082248}}
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2024-11-13 16:59:48,259 (client:354) INFO: {'Role': 'Client #2', 'Round': 1, 'Results_raw': {'train_loss': 21.017961, 'val_loss': 16.840059, 'test_loss': 17.013751}}
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2024-11-13 17:00:26,014 (client:354) INFO: {'Role': 'Client #1', 'Round': 1, 'Results_raw': {'train_loss': 22.124599, 'val_loss': 18.702586, 'test_loss': 17.382394}}
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2024-11-13 17:00:26,019 (server:615) INFO: {'Role': 'Server #', 'Round': 0, 'Results_weighted_avg': {'test_loss': np.float64(197414.068536), 'test_avg_loss': np.float64(58.199902), 'test_total': np.float64(3392.0), 'val_loss': np.float64(206134.83327), 'val_avg_loss': np.float64(60.770882), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(197414.068536), 'test_avg_loss': np.float64(58.199902), 'test_total': np.float64(3392.0), 'val_loss': np.float64(206134.83327), 'val_avg_loss': np.float64(60.770882), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(44221.570644), 'test_loss_bottom_decile': np.float64(164565.058838), 'test_loss_top_decile': np.float64(257037.366577), 'test_loss_min': np.float64(97363.599854), 'test_loss_max': np.float64(257037.366577), 'test_loss_bottom10%': np.float64(97363.599854), 'test_loss_top10%': np.float64(257037.366577), 'test_loss_cos1': np.float64(0.975817), 'test_loss_entropy': np.float64(2.275183), 'test_avg_loss_std': np.float64(13.03702), 'test_avg_loss_bottom_decile': np.float64(48.515642), 'test_avg_loss_top_decile': np.float64(75.777526), 'test_avg_loss_min': np.float64(28.703891), 'test_avg_loss_max': np.float64(75.777526), 'test_avg_loss_bottom10%': np.float64(28.703891), 'test_avg_loss_top10%': np.float64(75.777526), 'test_avg_loss_cos1': np.float64(0.975817), 'test_avg_loss_entropy': np.float64(2.275183), 'val_loss_std': np.float64(47619.912951), 'val_loss_bottom_decile': np.float64(166428.48645), 'val_loss_top_decile': np.float64(276400.308716), 'val_loss_min': np.float64(100362.033997), 'val_loss_max': np.float64(276400.308716), 'val_loss_bottom10%': np.float64(100362.033997), 'val_loss_top10%': np.float64(276400.308716), 'val_loss_cos1': np.float64(0.974339), 'val_loss_entropy': np.float64(2.273632), 'val_avg_loss_std': np.float64(14.038889), 'val_avg_loss_bottom_decile': np.float64(49.065002), 'val_avg_loss_top_decile': np.float64(81.48594), 'val_avg_loss_min': np.float64(29.587864), 'val_avg_loss_max': np.float64(81.48594), 'val_avg_loss_bottom10%': np.float64(29.587864), 'val_avg_loss_top10%': np.float64(81.48594), 'val_avg_loss_cos1': np.float64(0.974339), 'val_avg_loss_entropy': np.float64(2.273632)}}
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2024-11-13 17:00:26,060 (server:353) INFO: Server: Starting evaluation at the end of round 1.
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2024-11-13 17:00:26,060 (server:359) INFO: ----------- Starting a new training round (Round #2) -------------
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2024-11-13 17:02:02,689 (client:354) INFO: {'Role': 'Client #2', 'Round': 2, 'Results_raw': {'train_loss': 18.086333, 'val_loss': 15.878439, 'test_loss': 15.998531}}
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2024-11-13 17:02:39,973 (client:354) INFO: {'Role': 'Client #7', 'Round': 2, 'Results_raw': {'train_loss': 15.0893, 'val_loss': 12.23802, 'test_loss': 12.199388}}
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2024-11-13 17:03:17,121 (client:354) INFO: {'Role': 'Client #3', 'Round': 2, 'Results_raw': {'train_loss': 17.497708, 'val_loss': 15.053553, 'test_loss': 14.810329}}
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2024-11-13 17:03:52,784 (client:354) INFO: {'Role': 'Client #6', 'Round': 2, 'Results_raw': {'train_loss': 10.60521, 'val_loss': 9.896753, 'test_loss': 9.836409}}
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2024-11-13 17:04:29,627 (client:354) INFO: {'Role': 'Client #8', 'Round': 2, 'Results_raw': {'train_loss': 19.421581, 'val_loss': 16.706554, 'test_loss': 15.853611}}
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2024-11-13 17:05:04,810 (client:354) INFO: {'Role': 'Client #9', 'Round': 2, 'Results_raw': {'train_loss': 17.577214, 'val_loss': 16.01428, 'test_loss': 15.306264}}
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2024-11-13 17:05:42,176 (client:354) INFO: {'Role': 'Client #10', 'Round': 2, 'Results_raw': {'train_loss': 20.108317, 'val_loss': 17.090522, 'test_loss': 16.896774}}
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2024-11-13 17:06:19,232 (client:354) INFO: {'Role': 'Client #1', 'Round': 2, 'Results_raw': {'train_loss': 19.732364, 'val_loss': 17.756236, 'test_loss': 16.588}}
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2024-11-13 17:06:57,296 (client:354) INFO: {'Role': 'Client #4', 'Round': 2, 'Results_raw': {'train_loss': 19.010558, 'val_loss': 15.891348, 'test_loss': 15.967098}}
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2024-11-13 17:07:35,820 (client:354) INFO: {'Role': 'Client #5', 'Round': 2, 'Results_raw': {'train_loss': 15.987884, 'val_loss': 14.035274, 'test_loss': 14.095126}}
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2024-11-13 17:07:35,824 (server:615) INFO: {'Role': 'Server #', 'Round': 1, 'Results_weighted_avg': {'test_loss': np.float64(62583.745908), 'test_avg_loss': np.float64(18.450397), 'test_total': np.float64(3392.0), 'val_loss': np.float64(64488.041882), 'val_avg_loss': np.float64(19.011805), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(62583.745908), 'test_avg_loss': np.float64(18.450397), 'test_total': np.float64(3392.0), 'val_loss': np.float64(64488.041882), 'val_avg_loss': np.float64(19.011805), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8828.876976), 'test_loss_bottom_decile': np.float64(54443.279419), 'test_loss_top_decile': np.float64(72215.853699), 'test_loss_min': np.float64(40491.422577), 'test_loss_max': np.float64(72215.853699), 'test_loss_bottom10%': np.float64(40491.422577), 'test_loss_top10%': np.float64(72215.853699), 'test_loss_cos1': np.float64(0.990195), 'test_loss_entropy': np.float64(2.291806), 'test_avg_loss_std': np.float64(2.602853), 'test_avg_loss_bottom_decile': np.float64(16.050495), 'test_avg_loss_top_decile': np.float64(21.290051), 'test_avg_loss_min': np.float64(11.93733), 'test_avg_loss_max': np.float64(21.290051), 'test_avg_loss_bottom10%': np.float64(11.93733), 'test_avg_loss_top10%': np.float64(21.290051), 'test_avg_loss_cos1': np.float64(0.990195), 'test_avg_loss_entropy': np.float64(2.291806), 'val_loss_std': np.float64(9477.595335), 'val_loss_bottom_decile': np.float64(56015.363281), 'val_loss_top_decile': np.float64(73756.608582), 'val_loss_min': np.float64(40748.440491), 'val_loss_max': np.float64(73756.608582), 'val_loss_bottom10%': np.float64(40748.440491), 'val_loss_top10%': np.float64(73756.608582), 'val_loss_cos1': np.float64(0.989372), 'val_loss_entropy': np.float64(2.290843), 'val_avg_loss_std': np.float64(2.794102), 'val_avg_loss_bottom_decile': np.float64(16.513963), 'val_avg_loss_top_decile': np.float64(21.744283), 'val_avg_loss_min': np.float64(12.013102), 'val_avg_loss_max': np.float64(21.744283), 'val_avg_loss_bottom10%': np.float64(12.013102), 'val_avg_loss_top10%': np.float64(21.744283), 'val_avg_loss_cos1': np.float64(0.989372), 'val_avg_loss_entropy': np.float64(2.290843)}}
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2024-11-13 17:07:35,858 (server:353) INFO: Server: Starting evaluation at the end of round 2.
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2024-11-13 17:07:35,858 (server:359) INFO: ----------- Starting a new training round (Round #3) -------------
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2024-11-13 17:09:13,420 (client:354) INFO: {'Role': 'Client #1', 'Round': 3, 'Results_raw': {'train_loss': 18.345324, 'val_loss': 16.891763, 'test_loss': 15.833295}}
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2024-11-13 17:09:50,528 (client:354) INFO: {'Role': 'Client #8', 'Round': 3, 'Results_raw': {'train_loss': 18.076583, 'val_loss': 15.778445, 'test_loss': 15.090305}}
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2024-11-13 17:10:27,947 (client:354) INFO: {'Role': 'Client #6', 'Round': 3, 'Results_raw': {'train_loss': 9.849533, 'val_loss': 8.564662, 'test_loss': 8.60042}}
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2024-11-13 17:11:05,011 (client:354) INFO: {'Role': 'Client #3', 'Round': 3, 'Results_raw': {'train_loss': 16.321828, 'val_loss': 14.282993, 'test_loss': 14.049011}}
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2024-11-13 17:11:41,947 (client:354) INFO: {'Role': 'Client #4', 'Round': 3, 'Results_raw': {'train_loss': 17.919046, 'val_loss': 15.356112, 'test_loss': 15.493095}}
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2024-11-13 17:12:19,170 (client:354) INFO: {'Role': 'Client #10', 'Round': 3, 'Results_raw': {'train_loss': 18.704378, 'val_loss': 16.315565, 'test_loss': 16.210919}}
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2024-11-13 17:12:57,175 (client:354) INFO: {'Role': 'Client #7', 'Round': 3, 'Results_raw': {'train_loss': 13.583609, 'val_loss': 11.393568, 'test_loss': 11.457143}}
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2024-11-13 17:13:34,043 (client:354) INFO: {'Role': 'Client #5', 'Round': 3, 'Results_raw': {'train_loss': 15.169999, 'val_loss': 13.393946, 'test_loss': 13.680643}}
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2024-11-13 17:14:11,129 (client:354) INFO: {'Role': 'Client #9', 'Round': 3, 'Results_raw': {'train_loss': 16.344812, 'val_loss': 15.637693, 'test_loss': 15.109491}}
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2024-11-13 17:14:48,078 (client:354) INFO: {'Role': 'Client #2', 'Round': 3, 'Results_raw': {'train_loss': 17.511916, 'val_loss': 15.347818, 'test_loss': 15.579546}}
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2024-11-13 17:14:48,082 (server:615) INFO: {'Role': 'Server #', 'Round': 2, 'Results_weighted_avg': {'test_loss': np.float64(60478.679413), 'test_avg_loss': np.float64(17.829799), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62540.035471), 'val_avg_loss': np.float64(18.43751), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60478.679413), 'test_avg_loss': np.float64(17.829799), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62540.035471), 'val_avg_loss': np.float64(18.43751), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8672.726258), 'test_loss_bottom_decile': np.float64(51993.343567), 'test_loss_top_decile': np.float64(68921.658813), 'test_loss_min': np.float64(38778.476746), 'test_loss_max': np.float64(68921.658813), 'test_loss_bottom10%': np.float64(38778.476746), 'test_loss_top10%': np.float64(68921.658813), 'test_loss_cos1': np.float64(0.989874), 'test_loss_entropy': np.float64(2.291407), 'test_avg_loss_std': np.float64(2.556818), 'test_avg_loss_bottom_decile': np.float64(15.328226), 'test_avg_loss_top_decile': np.float64(20.318885), 'test_avg_loss_min': np.float64(11.432334), 'test_avg_loss_max': np.float64(20.318885), 'test_avg_loss_bottom10%': np.float64(11.432334), 'test_avg_loss_top10%': np.float64(20.318885), 'test_avg_loss_cos1': np.float64(0.989874), 'test_avg_loss_entropy': np.float64(2.291407), 'val_loss_std': np.float64(9333.382706), 'val_loss_bottom_decile': np.float64(53834.933472), 'val_loss_top_decile': np.float64(72188.842712), 'val_loss_min': np.float64(39114.493805), 'val_loss_max': np.float64(72188.842712), 'val_loss_bottom10%': np.float64(39114.493805), 'val_loss_top10%': np.float64(72188.842712), 'val_loss_cos1': np.float64(0.989047), 'val_loss_entropy': np.float64(2.290432), 'val_avg_loss_std': np.float64(2.751587), 'val_avg_loss_bottom_decile': np.float64(15.871148), 'val_avg_loss_top_decile': np.float64(21.282088), 'val_avg_loss_min': np.float64(11.531396), 'val_avg_loss_max': np.float64(21.282088), 'val_avg_loss_bottom10%': np.float64(11.531396), 'val_avg_loss_top10%': np.float64(21.282088), 'val_avg_loss_cos1': np.float64(0.989047), 'val_avg_loss_entropy': np.float64(2.290432)}}
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2024-11-13 17:14:48,112 (server:353) INFO: Server: Starting evaluation at the end of round 3.
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2024-11-13 17:14:48,113 (server:359) INFO: ----------- Starting a new training round (Round #4) -------------
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2024-11-13 17:16:26,097 (client:354) INFO: {'Role': 'Client #3', 'Round': 4, 'Results_raw': {'train_loss': 16.100292, 'val_loss': 14.061161, 'test_loss': 13.910026}}
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2024-11-13 17:17:01,085 (client:354) INFO: {'Role': 'Client #1', 'Round': 4, 'Results_raw': {'train_loss': 17.789154, 'val_loss': 17.135729, 'test_loss': 16.054605}}
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2024-11-13 17:17:36,492 (client:354) INFO: {'Role': 'Client #10', 'Round': 4, 'Results_raw': {'train_loss': 18.336905, 'val_loss': 16.029196, 'test_loss': 15.953174}}
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2024-11-13 17:18:09,661 (client:354) INFO: {'Role': 'Client #6', 'Round': 4, 'Results_raw': {'train_loss': 9.613619, 'val_loss': 8.36345, 'test_loss': 8.38078}}
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2024-11-13 17:18:41,212 (client:354) INFO: {'Role': 'Client #5', 'Round': 4, 'Results_raw': {'train_loss': 14.679316, 'val_loss': 13.409065, 'test_loss': 13.84304}}
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2024-11-13 17:19:12,670 (client:354) INFO: {'Role': 'Client #2', 'Round': 4, 'Results_raw': {'train_loss': 16.852045, 'val_loss': 15.715068, 'test_loss': 15.965541}}
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2024-11-13 17:19:43,707 (client:354) INFO: {'Role': 'Client #8', 'Round': 4, 'Results_raw': {'train_loss': 17.795312, 'val_loss': 15.347831, 'test_loss': 14.732709}}
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2024-11-13 17:20:17,528 (client:354) INFO: {'Role': 'Client #9', 'Round': 4, 'Results_raw': {'train_loss': 16.032253, 'val_loss': 14.70649, 'test_loss': 14.247379}}
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2024-11-13 17:20:49,390 (client:354) INFO: {'Role': 'Client #7', 'Round': 4, 'Results_raw': {'train_loss': 12.778594, 'val_loss': 10.620873, 'test_loss': 10.646246}}
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2024-11-13 17:21:21,348 (client:354) INFO: {'Role': 'Client #4', 'Round': 4, 'Results_raw': {'train_loss': 17.693269, 'val_loss': 15.029215, 'test_loss': 15.130403}}
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2024-11-13 17:21:21,351 (server:615) INFO: {'Role': 'Server #', 'Round': 3, 'Results_weighted_avg': {'test_loss': np.float64(58993.809625), 'test_avg_loss': np.float64(17.392043), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60991.772214), 'val_avg_loss': np.float64(17.981065), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58993.809625), 'test_avg_loss': np.float64(17.392043), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60991.772214), 'val_avg_loss': np.float64(17.981065), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8241.820243), 'test_loss_bottom_decile': np.float64(49739.580078), 'test_loss_top_decile': np.float64(66543.555481), 'test_loss_min': np.float64(38831.369995), 'test_loss_max': np.float64(66543.555481), 'test_loss_bottom10%': np.float64(38831.369995), 'test_loss_top10%': np.float64(66543.555481), 'test_loss_cos1': np.float64(0.990382), 'test_loss_entropy': np.float64(2.292039), 'test_avg_loss_std': np.float64(2.429782), 'test_avg_loss_bottom_decile': np.float64(14.663791), 'test_avg_loss_top_decile': np.float64(19.617793), 'test_avg_loss_min': np.float64(11.447927), 'test_avg_loss_max': np.float64(19.617793), 'test_avg_loss_bottom10%': np.float64(11.447927), 'test_avg_loss_top10%': np.float64(19.617793), 'test_avg_loss_cos1': np.float64(0.990382), 'test_avg_loss_entropy': np.float64(2.292039), 'val_loss_std': np.float64(8783.044936), 'val_loss_bottom_decile': np.float64(51642.864014), 'val_loss_top_decile': np.float64(70106.523132), 'val_loss_min': np.float64(39316.236298), 'val_loss_max': np.float64(70106.523132), 'val_loss_bottom10%': np.float64(39316.236298), 'val_loss_top10%': np.float64(70106.523132), 'val_loss_cos1': np.float64(0.98979), 'val_loss_entropy': np.float64(2.291338), 'val_avg_loss_std': np.float64(2.589341), 'val_avg_loss_bottom_decile': np.float64(15.224901), 'val_avg_loss_top_decile': np.float64(20.668197), 'val_avg_loss_min': np.float64(11.590872), 'val_avg_loss_max': np.float64(20.668197), 'val_avg_loss_bottom10%': np.float64(11.590872), 'val_avg_loss_top10%': np.float64(20.668197), 'val_avg_loss_cos1': np.float64(0.98979), 'val_avg_loss_entropy': np.float64(2.291338)}}
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2024-11-13 17:21:21,385 (server:353) INFO: Server: Starting evaluation at the end of round 4.
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2024-11-13 17:21:21,385 (server:359) INFO: ----------- Starting a new training round (Round #5) -------------
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2024-11-13 17:22:49,845 (client:354) INFO: {'Role': 'Client #2', 'Round': 5, 'Results_raw': {'train_loss': 16.744316, 'val_loss': 15.076553, 'test_loss': 15.298259}}
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2024-11-13 17:23:22,235 (client:354) INFO: {'Role': 'Client #5', 'Round': 5, 'Results_raw': {'train_loss': 14.432023, 'val_loss': 13.089597, 'test_loss': 13.464347}}
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2024-11-13 17:23:54,440 (client:354) INFO: {'Role': 'Client #10', 'Round': 5, 'Results_raw': {'train_loss': 17.856366, 'val_loss': 15.870832, 'test_loss': 15.940441}}
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2024-11-13 17:24:25,751 (client:354) INFO: {'Role': 'Client #6', 'Round': 5, 'Results_raw': {'train_loss': 9.418936, 'val_loss': 8.407101, 'test_loss': 8.463528}}
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2024-11-13 17:24:56,098 (client:354) INFO: {'Role': 'Client #4', 'Round': 5, 'Results_raw': {'train_loss': 17.148276, 'val_loss': 14.958971, 'test_loss': 15.017648}}
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2024-11-13 17:25:26,972 (client:354) INFO: {'Role': 'Client #1', 'Round': 5, 'Results_raw': {'train_loss': 17.380807, 'val_loss': 16.176932, 'test_loss': 15.286505}}
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2024-11-13 17:25:59,113 (client:354) INFO: {'Role': 'Client #9', 'Round': 5, 'Results_raw': {'train_loss': 15.639488, 'val_loss': 14.747316, 'test_loss': 14.241704}}
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2024-11-13 17:26:31,196 (client:354) INFO: {'Role': 'Client #8', 'Round': 5, 'Results_raw': {'train_loss': 17.099892, 'val_loss': 15.09718, 'test_loss': 14.472948}}
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2024-11-13 17:27:03,389 (client:354) INFO: {'Role': 'Client #7', 'Round': 5, 'Results_raw': {'train_loss': 12.398995, 'val_loss': 10.677704, 'test_loss': 10.66221}}
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2024-11-13 17:27:35,151 (client:354) INFO: {'Role': 'Client #3', 'Round': 5, 'Results_raw': {'train_loss': 15.869717, 'val_loss': 13.962367, 'test_loss': 13.838108}}
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2024-11-13 17:27:35,154 (server:615) INFO: {'Role': 'Server #', 'Round': 4, 'Results_weighted_avg': {'test_loss': np.float64(59269.586945), 'test_avg_loss': np.float64(17.473345), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61404.051431), 'val_avg_loss': np.float64(18.10261), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59269.586945), 'test_avg_loss': np.float64(17.473345), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61404.051431), 'val_avg_loss': np.float64(18.10261), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8631.994615), 'test_loss_bottom_decile': np.float64(49961.993347), 'test_loss_top_decile': np.float64(66985.775513), 'test_loss_min': np.float64(37942.120972), 'test_loss_max': np.float64(66985.775513), 'test_loss_bottom10%': np.float64(37942.120972), 'test_loss_top10%': np.float64(66985.775513), 'test_loss_cos1': np.float64(0.98956), 'test_loss_entropy': np.float64(2.291057), 'test_avg_loss_std': np.float64(2.54481), 'test_avg_loss_bottom_decile': np.float64(14.729361), 'test_avg_loss_top_decile': np.float64(19.748165), 'test_avg_loss_min': np.float64(11.185767), 'test_avg_loss_max': np.float64(19.748165), 'test_avg_loss_bottom10%': np.float64(11.185767), 'test_avg_loss_top10%': np.float64(19.748165), 'test_avg_loss_cos1': np.float64(0.98956), 'test_avg_loss_entropy': np.float64(2.291057), 'val_loss_std': np.float64(9167.867645), 'val_loss_bottom_decile': np.float64(52059.722656), 'val_loss_top_decile': np.float64(71446.745911), 'val_loss_min': np.float64(38675.57193), 'val_loss_max': np.float64(71446.745911), 'val_loss_bottom10%': np.float64(38675.57193), 'val_loss_top10%': np.float64(71446.745911), 'val_loss_cos1': np.float64(0.989037), 'val_loss_entropy': np.float64(2.290453), 'val_avg_loss_std': np.float64(2.702791), 'val_avg_loss_bottom_decile': np.float64(15.347796), 'val_avg_loss_top_decile': np.float64(21.06331), 'val_avg_loss_min': np.float64(11.401996), 'val_avg_loss_max': np.float64(21.06331), 'val_avg_loss_bottom10%': np.float64(11.401996), 'val_avg_loss_top10%': np.float64(21.06331), 'val_avg_loss_cos1': np.float64(0.989037), 'val_avg_loss_entropy': np.float64(2.290453)}}
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2024-11-13 17:27:35,180 (server:353) INFO: Server: Starting evaluation at the end of round 5.
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2024-11-13 17:27:35,181 (server:359) INFO: ----------- Starting a new training round (Round #6) -------------
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2024-11-13 17:29:02,103 (client:354) INFO: {'Role': 'Client #6', 'Round': 6, 'Results_raw': {'train_loss': 9.222858, 'val_loss': 8.098892, 'test_loss': 8.128721}}
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2024-11-13 17:29:34,411 (client:354) INFO: {'Role': 'Client #5', 'Round': 6, 'Results_raw': {'train_loss': 14.20997, 'val_loss': 13.004488, 'test_loss': 13.334625}}
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2024-11-13 17:30:06,310 (client:354) INFO: {'Role': 'Client #9', 'Round': 6, 'Results_raw': {'train_loss': 15.584704, 'val_loss': 14.510476, 'test_loss': 14.052706}}
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2024-11-13 17:30:37,766 (client:354) INFO: {'Role': 'Client #2', 'Round': 6, 'Results_raw': {'train_loss': 16.410219, 'val_loss': 14.567516, 'test_loss': 14.831237}}
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2024-11-13 17:31:09,357 (client:354) INFO: {'Role': 'Client #4', 'Round': 6, 'Results_raw': {'train_loss': 16.911054, 'val_loss': 15.077275, 'test_loss': 15.320676}}
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2024-11-13 17:31:41,399 (client:354) INFO: {'Role': 'Client #8', 'Round': 6, 'Results_raw': {'train_loss': 17.014612, 'val_loss': 14.842014, 'test_loss': 14.176693}}
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2024-11-13 17:32:14,770 (client:354) INFO: {'Role': 'Client #7', 'Round': 6, 'Results_raw': {'train_loss': 12.138073, 'val_loss': 10.670417, 'test_loss': 10.911111}}
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2024-11-13 17:32:49,215 (client:354) INFO: {'Role': 'Client #1', 'Round': 6, 'Results_raw': {'train_loss': 17.159615, 'val_loss': 16.302889, 'test_loss': 15.395617}}
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2024-11-13 17:33:21,607 (client:354) INFO: {'Role': 'Client #3', 'Round': 6, 'Results_raw': {'train_loss': 15.574081, 'val_loss': 13.856066, 'test_loss': 13.76622}}
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2024-11-13 17:34:00,752 (client:354) INFO: {'Role': 'Client #10', 'Round': 6, 'Results_raw': {'train_loss': 17.522324, 'val_loss': 15.61689, 'test_loss': 15.773812}}
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2024-11-13 17:34:00,756 (server:615) INFO: {'Role': 'Server #', 'Round': 5, 'Results_weighted_avg': {'test_loss': np.float64(59515.439786), 'test_avg_loss': np.float64(17.545825), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61641.935001), 'val_avg_loss': np.float64(18.17274), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59515.439786), 'test_avg_loss': np.float64(17.545825), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61641.935001), 'val_avg_loss': np.float64(18.17274), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8518.860789), 'test_loss_bottom_decile': np.float64(50513.032074), 'test_loss_top_decile': np.float64(67036.87616), 'test_loss_min': np.float64(38422.832764), 'test_loss_max': np.float64(67036.87616), 'test_loss_bottom10%': np.float64(38422.832764), 'test_loss_top10%': np.float64(67036.87616), 'test_loss_cos1': np.float64(0.989911), 'test_loss_entropy': np.float64(2.291468), 'test_avg_loss_std': np.float64(2.511457), 'test_avg_loss_bottom_decile': np.float64(14.891814), 'test_avg_loss_top_decile': np.float64(19.76323), 'test_avg_loss_min': np.float64(11.327486), 'test_avg_loss_max': np.float64(19.76323), 'test_avg_loss_bottom10%': np.float64(11.327486), 'test_avg_loss_top10%': np.float64(19.76323), 'test_avg_loss_cos1': np.float64(0.989911), 'test_avg_loss_entropy': np.float64(2.291468), 'val_loss_std': np.float64(8969.780516), 'val_loss_bottom_decile': np.float64(52821.498596), 'val_loss_top_decile': np.float64(71461.075928), 'val_loss_min': np.float64(39320.759491), 'val_loss_max': np.float64(71461.075928), 'val_loss_bottom10%': np.float64(39320.759491), 'val_loss_top10%': np.float64(71461.075928), 'val_loss_cos1': np.float64(0.989578), 'val_loss_entropy': np.float64(2.291088), 'val_avg_loss_std': np.float64(2.644393), 'val_avg_loss_bottom_decile': np.float64(15.572376), 'val_avg_loss_top_decile': np.float64(21.067534), 'val_avg_loss_min': np.float64(11.592205), 'val_avg_loss_max': np.float64(21.067534), 'val_avg_loss_bottom10%': np.float64(11.592205), 'val_avg_loss_top10%': np.float64(21.067534), 'val_avg_loss_cos1': np.float64(0.989578), 'val_avg_loss_entropy': np.float64(2.291088)}}
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2024-11-13 17:34:00,800 (server:353) INFO: Server: Starting evaluation at the end of round 6.
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2024-11-13 17:34:00,801 (server:359) INFO: ----------- Starting a new training round (Round #7) -------------
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2024-11-13 17:35:32,409 (client:354) INFO: {'Role': 'Client #8', 'Round': 7, 'Results_raw': {'train_loss': 16.563468, 'val_loss': 15.269223, 'test_loss': 14.601843}}
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2024-11-13 17:36:05,200 (client:354) INFO: {'Role': 'Client #4', 'Round': 7, 'Results_raw': {'train_loss': 16.795132, 'val_loss': 14.442249, 'test_loss': 14.758297}}
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2024-11-13 17:36:37,600 (client:354) INFO: {'Role': 'Client #6', 'Round': 7, 'Results_raw': {'train_loss': 9.069971, 'val_loss': 7.980666, 'test_loss': 8.043807}}
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2024-11-13 17:37:10,613 (client:354) INFO: {'Role': 'Client #10', 'Round': 7, 'Results_raw': {'train_loss': 17.38111, 'val_loss': 15.554932, 'test_loss': 15.665864}}
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2024-11-13 17:37:44,568 (client:354) INFO: {'Role': 'Client #9', 'Round': 7, 'Results_raw': {'train_loss': 15.463686, 'val_loss': 14.50283, 'test_loss': 14.123946}}
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2024-11-13 17:38:17,636 (client:354) INFO: {'Role': 'Client #7', 'Round': 7, 'Results_raw': {'train_loss': 12.229018, 'val_loss': 10.206877, 'test_loss': 10.230994}}
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2024-11-13 17:38:50,525 (client:354) INFO: {'Role': 'Client #1', 'Round': 7, 'Results_raw': {'train_loss': 16.78094, 'val_loss': 15.770201, 'test_loss': 15.077742}}
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2024-11-13 17:39:25,358 (client:354) INFO: {'Role': 'Client #3', 'Round': 7, 'Results_raw': {'train_loss': 15.473847, 'val_loss': 13.634775, 'test_loss': 13.536583}}
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2024-11-13 17:40:01,277 (client:354) INFO: {'Role': 'Client #5', 'Round': 7, 'Results_raw': {'train_loss': 13.95138, 'val_loss': 12.656518, 'test_loss': 13.129518}}
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2024-11-13 17:40:34,635 (client:354) INFO: {'Role': 'Client #2', 'Round': 7, 'Results_raw': {'train_loss': 16.171692, 'val_loss': 14.565393, 'test_loss': 14.820774}}
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2024-11-13 17:40:34,640 (server:615) INFO: {'Role': 'Server #', 'Round': 6, 'Results_weighted_avg': {'test_loss': np.float64(59464.191992), 'test_avg_loss': np.float64(17.530717), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61524.566193), 'val_avg_loss': np.float64(18.138139), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59464.191992), 'test_avg_loss': np.float64(17.530717), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61524.566193), 'val_avg_loss': np.float64(18.138139), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8586.585188), 'test_loss_bottom_decile': np.float64(49656.346008), 'test_loss_top_decile': np.float64(67244.053467), 'test_loss_min': np.float64(38389.860962), 'test_loss_max': np.float64(67244.053467), 'test_loss_bottom10%': np.float64(38389.860962), 'test_loss_top10%': np.float64(67244.053467), 'test_loss_cos1': np.float64(0.989735), 'test_loss_entropy': np.float64(2.291267), 'test_avg_loss_std': np.float64(2.531423), 'test_avg_loss_bottom_decile': np.float64(14.639253), 'test_avg_loss_top_decile': np.float64(19.824308), 'test_avg_loss_min': np.float64(11.317766), 'test_avg_loss_max': np.float64(19.824308), 'test_avg_loss_bottom10%': np.float64(11.317766), 'test_avg_loss_top10%': np.float64(19.824308), 'test_avg_loss_cos1': np.float64(0.989735), 'test_avg_loss_entropy': np.float64(2.291267), 'val_loss_std': np.float64(8907.388567), 'val_loss_bottom_decile': np.float64(51977.549622), 'val_loss_top_decile': np.float64(70245.462463), 'val_loss_min': np.float64(39325.996033), 'val_loss_max': np.float64(70245.462463), 'val_loss_bottom10%': np.float64(39325.996033), 'val_loss_top10%': np.float64(70245.462463), 'val_loss_cos1': np.float64(0.989682), 'val_loss_entropy': np.float64(2.291174), 'val_avg_loss_std': np.float64(2.625999), 'val_avg_loss_bottom_decile': np.float64(15.32357), 'val_avg_loss_top_decile': np.float64(20.709158), 'val_avg_loss_min': np.float64(11.593749), 'val_avg_loss_max': np.float64(20.709158), 'val_avg_loss_bottom10%': np.float64(11.593749), 'val_avg_loss_top10%': np.float64(20.709158), 'val_avg_loss_cos1': np.float64(0.989682), 'val_avg_loss_entropy': np.float64(2.291174)}}
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2024-11-13 17:40:34,683 (server:353) INFO: Server: Starting evaluation at the end of round 7.
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2024-11-13 17:40:34,684 (server:359) INFO: ----------- Starting a new training round (Round #8) -------------
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2024-11-13 17:42:02,635 (client:354) INFO: {'Role': 'Client #6', 'Round': 8, 'Results_raw': {'train_loss': 9.053816, 'val_loss': 8.099754, 'test_loss': 8.154828}}
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2024-11-13 17:42:34,340 (client:354) INFO: {'Role': 'Client #7', 'Round': 8, 'Results_raw': {'train_loss': 11.969874, 'val_loss': 10.047707, 'test_loss': 10.310971}}
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2024-11-13 17:43:06,559 (client:354) INFO: {'Role': 'Client #2', 'Round': 8, 'Results_raw': {'train_loss': 15.999868, 'val_loss': 14.310933, 'test_loss': 14.576132}}
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2024-11-13 17:43:39,200 (client:354) INFO: {'Role': 'Client #4', 'Round': 8, 'Results_raw': {'train_loss': 16.697271, 'val_loss': 14.304638, 'test_loss': 14.602109}}
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2024-11-13 17:44:11,781 (client:354) INFO: {'Role': 'Client #1', 'Round': 8, 'Results_raw': {'train_loss': 16.627265, 'val_loss': 15.996223, 'test_loss': 15.083575}}
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2024-11-13 17:44:43,982 (client:354) INFO: {'Role': 'Client #8', 'Round': 8, 'Results_raw': {'train_loss': 16.44423, 'val_loss': 14.568907, 'test_loss': 13.849099}}
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2024-11-13 17:45:14,505 (client:354) INFO: {'Role': 'Client #5', 'Round': 8, 'Results_raw': {'train_loss': 13.895619, 'val_loss': 12.768417, 'test_loss': 13.238846}}
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2024-11-13 17:45:45,637 (client:354) INFO: {'Role': 'Client #3', 'Round': 8, 'Results_raw': {'train_loss': 15.230887, 'val_loss': 13.678209, 'test_loss': 13.53734}}
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2024-11-13 17:46:21,605 (client:354) INFO: {'Role': 'Client #10', 'Round': 8, 'Results_raw': {'train_loss': 17.256613, 'val_loss': 15.449304, 'test_loss': 15.627398}}
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2024-11-13 17:46:57,035 (client:354) INFO: {'Role': 'Client #9', 'Round': 8, 'Results_raw': {'train_loss': 15.211581, 'val_loss': 14.797352, 'test_loss': 14.325333}}
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2024-11-13 17:46:57,038 (server:615) INFO: {'Role': 'Server #', 'Round': 7, 'Results_weighted_avg': {'test_loss': np.float64(59115.40993), 'test_avg_loss': np.float64(17.427892), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61139.165945), 'val_avg_loss': np.float64(18.024518), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59115.40993), 'test_avg_loss': np.float64(17.427892), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61139.165945), 'val_avg_loss': np.float64(18.024518), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8365.567699), 'test_loss_bottom_decile': np.float64(49084.993774), 'test_loss_top_decile': np.float64(66713.420776), 'test_loss_min': np.float64(38865.913086), 'test_loss_max': np.float64(66713.420776), 'test_loss_bottom10%': np.float64(38865.913086), 'test_loss_top10%': np.float64(66713.420776), 'test_loss_cos1': np.float64(0.990135), 'test_loss_entropy': np.float64(2.291763), 'test_avg_loss_std': np.float64(2.466264), 'test_avg_loss_bottom_decile': np.float64(14.470812), 'test_avg_loss_top_decile': np.float64(19.667872), 'test_avg_loss_min': np.float64(11.458111), 'test_avg_loss_max': np.float64(19.667872), 'test_avg_loss_bottom10%': np.float64(11.458111), 'test_avg_loss_top10%': np.float64(19.667872), 'test_avg_loss_cos1': np.float64(0.990135), 'test_avg_loss_entropy': np.float64(2.291763), 'val_loss_std': np.float64(8651.084784), 'val_loss_bottom_decile': np.float64(51380.470184), 'val_loss_top_decile': np.float64(69299.355286), 'val_loss_min': np.float64(39792.188965), 'val_loss_max': np.float64(69299.355286), 'val_loss_bottom10%': np.float64(39792.188965), 'val_loss_top10%': np.float64(69299.355286), 'val_loss_cos1': np.float64(0.990137), 'val_loss_entropy': np.float64(2.291727), 'val_avg_loss_std': np.float64(2.550438), 'val_avg_loss_bottom_decile': np.float64(15.147544), 'val_avg_loss_top_decile': np.float64(20.430234), 'val_avg_loss_min': np.float64(11.731188), 'val_avg_loss_max': np.float64(20.430234), 'val_avg_loss_bottom10%': np.float64(11.731188), 'val_avg_loss_top10%': np.float64(20.430234), 'val_avg_loss_cos1': np.float64(0.990137), 'val_avg_loss_entropy': np.float64(2.291727)}}
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2024-11-13 17:46:57,072 (server:353) INFO: Server: Starting evaluation at the end of round 8.
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2024-11-13 17:46:57,072 (server:359) INFO: ----------- Starting a new training round (Round #9) -------------
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2024-11-13 17:48:28,430 (client:354) INFO: {'Role': 'Client #2', 'Round': 9, 'Results_raw': {'train_loss': 15.779971, 'val_loss': 14.371766, 'test_loss': 14.586316}}
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2024-11-13 17:49:03,445 (client:354) INFO: {'Role': 'Client #9', 'Round': 9, 'Results_raw': {'train_loss': 15.04073, 'val_loss': 13.913913, 'test_loss': 13.584073}}
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2024-11-13 17:49:38,453 (client:354) INFO: {'Role': 'Client #8', 'Round': 9, 'Results_raw': {'train_loss': 16.379022, 'val_loss': 14.646657, 'test_loss': 14.003342}}
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2024-11-13 17:50:13,960 (client:354) INFO: {'Role': 'Client #5', 'Round': 9, 'Results_raw': {'train_loss': 13.858816, 'val_loss': 12.718025, 'test_loss': 13.215536}}
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2024-11-13 17:50:49,205 (client:354) INFO: {'Role': 'Client #1', 'Round': 9, 'Results_raw': {'train_loss': 16.615898, 'val_loss': 15.94305, 'test_loss': 15.152937}}
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2024-11-13 17:51:24,530 (client:354) INFO: {'Role': 'Client #4', 'Round': 9, 'Results_raw': {'train_loss': 16.63042, 'val_loss': 14.244602, 'test_loss': 14.543859}}
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2024-11-13 17:52:00,510 (client:354) INFO: {'Role': 'Client #3', 'Round': 9, 'Results_raw': {'train_loss': 15.145712, 'val_loss': 13.500551, 'test_loss': 13.391295}}
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2024-11-13 17:52:33,868 (client:354) INFO: {'Role': 'Client #6', 'Round': 9, 'Results_raw': {'train_loss': 8.937352, 'val_loss': 7.928945, 'test_loss': 7.974031}}
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2024-11-13 17:53:07,222 (client:354) INFO: {'Role': 'Client #10', 'Round': 9, 'Results_raw': {'train_loss': 17.037972, 'val_loss': 15.439676, 'test_loss': 15.662244}}
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2024-11-13 17:53:40,249 (client:354) INFO: {'Role': 'Client #7', 'Round': 9, 'Results_raw': {'train_loss': 11.767367, 'val_loss': 10.355668, 'test_loss': 10.396644}}
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2024-11-13 17:53:40,253 (server:615) INFO: {'Role': 'Server #', 'Round': 8, 'Results_weighted_avg': {'test_loss': np.float64(59259.577292), 'test_avg_loss': np.float64(17.470394), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61257.37829), 'val_avg_loss': np.float64(18.059369), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59259.577292), 'test_avg_loss': np.float64(17.470394), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61257.37829), 'val_avg_loss': np.float64(18.059369), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8351.648035), 'test_loss_bottom_decile': np.float64(49749.040771), 'test_loss_top_decile': np.float64(66825.17218), 'test_loss_min': np.float64(38776.478119), 'test_loss_max': np.float64(66825.17218), 'test_loss_bottom10%': np.float64(38776.478119), 'test_loss_top10%': np.float64(66825.17218), 'test_loss_cos1': np.float64(0.990214), 'test_loss_entropy': np.float64(2.291832), 'test_avg_loss_std': np.float64(2.46216), 'test_avg_loss_bottom_decile': np.float64(14.66658), 'test_avg_loss_top_decile': np.float64(19.700817), 'test_avg_loss_min': np.float64(11.431745), 'test_avg_loss_max': np.float64(19.700817), 'test_avg_loss_bottom10%': np.float64(11.431745), 'test_avg_loss_top10%': np.float64(19.700817), 'test_avg_loss_cos1': np.float64(0.990214), 'test_avg_loss_entropy': np.float64(2.291832), 'val_loss_std': np.float64(8637.773118), 'val_loss_bottom_decile': np.float64(51990.884949), 'val_loss_top_decile': np.float64(69329.740112), 'val_loss_min': np.float64(39645.203918), 'val_loss_max': np.float64(69329.740112), 'val_loss_bottom10%': np.float64(39645.203918), 'val_loss_top10%': np.float64(69329.740112), 'val_loss_cos1': np.float64(0.990204), 'val_loss_entropy': np.float64(2.291774), 'val_avg_loss_std': np.float64(2.546513), 'val_avg_loss_bottom_decile': np.float64(15.327501), 'val_avg_loss_top_decile': np.float64(20.439192), 'val_avg_loss_min': np.float64(11.687855), 'val_avg_loss_max': np.float64(20.439192), 'val_avg_loss_bottom10%': np.float64(11.687855), 'val_avg_loss_top10%': np.float64(20.439192), 'val_avg_loss_cos1': np.float64(0.990204), 'val_avg_loss_entropy': np.float64(2.291774)}}
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2024-11-13 17:53:40,288 (server:353) INFO: Server: Starting evaluation at the end of round 9.
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2024-11-13 17:53:40,288 (server:359) INFO: ----------- Starting a new training round (Round #10) -------------
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2024-11-13 17:55:06,934 (client:354) INFO: {'Role': 'Client #6', 'Round': 10, 'Results_raw': {'train_loss': 8.844604, 'val_loss': 8.318289, 'test_loss': 8.376493}}
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2024-11-13 17:55:40,014 (client:354) INFO: {'Role': 'Client #10', 'Round': 10, 'Results_raw': {'train_loss': 16.95461, 'val_loss': 15.276593, 'test_loss': 15.376157}}
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2024-11-13 17:56:11,631 (client:354) INFO: {'Role': 'Client #4', 'Round': 10, 'Results_raw': {'train_loss': 16.527719, 'val_loss': 14.330759, 'test_loss': 14.675083}}
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2024-11-13 17:56:43,689 (client:354) INFO: {'Role': 'Client #9', 'Round': 10, 'Results_raw': {'train_loss': 14.936992, 'val_loss': 14.63994, 'test_loss': 14.260004}}
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2024-11-13 17:57:17,775 (client:354) INFO: {'Role': 'Client #3', 'Round': 10, 'Results_raw': {'train_loss': 15.09006, 'val_loss': 13.395597, 'test_loss': 13.42033}}
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2024-11-13 17:57:53,791 (client:354) INFO: {'Role': 'Client #1', 'Round': 10, 'Results_raw': {'train_loss': 16.376854, 'val_loss': 15.802422, 'test_loss': 14.997359}}
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2024-11-13 17:58:27,137 (client:354) INFO: {'Role': 'Client #5', 'Round': 10, 'Results_raw': {'train_loss': 13.671353, 'val_loss': 12.756275, 'test_loss': 13.213762}}
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2024-11-13 17:59:00,351 (client:354) INFO: {'Role': 'Client #8', 'Round': 10, 'Results_raw': {'train_loss': 16.211059, 'val_loss': 14.432302, 'test_loss': 13.82659}}
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2024-11-13 17:59:34,180 (client:354) INFO: {'Role': 'Client #2', 'Round': 10, 'Results_raw': {'train_loss': 15.705459, 'val_loss': 14.398777, 'test_loss': 14.627616}}
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2024-11-13 18:00:09,591 (client:354) INFO: {'Role': 'Client #7', 'Round': 10, 'Results_raw': {'train_loss': 11.606993, 'val_loss': 10.124443, 'test_loss': 10.334608}}
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2024-11-13 18:00:09,594 (server:615) INFO: {'Role': 'Server #', 'Round': 9, 'Results_weighted_avg': {'test_loss': np.float64(59058.183057), 'test_avg_loss': np.float64(17.411021), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60965.561343), 'val_avg_loss': np.float64(17.973338), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59058.183057), 'test_avg_loss': np.float64(17.411021), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60965.561343), 'val_avg_loss': np.float64(17.973338), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8240.24661), 'test_loss_bottom_decile': np.float64(49401.183594), 'test_loss_top_decile': np.float64(66582.997986), 'test_loss_min': np.float64(38823.119934), 'test_loss_max': np.float64(66582.997986), 'test_loss_bottom10%': np.float64(38823.119934), 'test_loss_top10%': np.float64(66582.997986), 'test_loss_cos1': np.float64(0.990406), 'test_loss_entropy': np.float64(2.292042), 'test_avg_loss_std': np.float64(2.429318), 'test_avg_loss_bottom_decile': np.float64(14.564028), 'test_avg_loss_top_decile': np.float64(19.629422), 'test_avg_loss_min': np.float64(11.445495), 'test_avg_loss_max': np.float64(19.629422), 'test_avg_loss_bottom10%': np.float64(11.445495), 'test_avg_loss_top10%': np.float64(19.629422), 'test_avg_loss_cos1': np.float64(0.990406), 'test_avg_loss_entropy': np.float64(2.292042), 'val_loss_std': np.float64(8455.375167), 'val_loss_bottom_decile': np.float64(51612.153198), 'val_loss_top_decile': np.float64(68443.937012), 'val_loss_min': np.float64(39689.804291), 'val_loss_max': np.float64(68443.937012), 'val_loss_bottom10%': np.float64(39689.804291), 'val_loss_top10%': np.float64(68443.937012), 'val_loss_cos1': np.float64(0.990519), 'val_loss_entropy': np.float64(2.292114), 'val_avg_loss_std': np.float64(2.49274), 'val_avg_loss_bottom_decile': np.float64(15.215847), 'val_avg_loss_top_decile': np.float64(20.178047), 'val_avg_loss_min': np.float64(11.701004), 'val_avg_loss_max': np.float64(20.178047), 'val_avg_loss_bottom10%': np.float64(11.701004), 'val_avg_loss_top10%': np.float64(20.178047), 'val_avg_loss_cos1': np.float64(0.990519), 'val_avg_loss_entropy': np.float64(2.292114)}}
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2024-11-13 18:00:09,631 (server:353) INFO: Server: Starting evaluation at the end of round 10.
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2024-11-13 18:00:09,632 (server:359) INFO: ----------- Starting a new training round (Round #11) -------------
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2024-11-13 18:01:43,293 (client:354) INFO: {'Role': 'Client #4', 'Round': 11, 'Results_raw': {'train_loss': 16.332751, 'val_loss': 14.135396, 'test_loss': 14.450692}}
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2024-11-13 18:02:18,342 (client:354) INFO: {'Role': 'Client #9', 'Round': 11, 'Results_raw': {'train_loss': 14.758139, 'val_loss': 14.15267, 'test_loss': 13.744951}}
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2024-11-13 18:02:55,097 (client:354) INFO: {'Role': 'Client #7', 'Round': 11, 'Results_raw': {'train_loss': 11.407206, 'val_loss': 9.858557, 'test_loss': 9.988735}}
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2024-11-13 18:03:30,390 (client:354) INFO: {'Role': 'Client #5', 'Round': 11, 'Results_raw': {'train_loss': 13.564306, 'val_loss': 12.434884, 'test_loss': 13.035306}}
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2024-11-13 18:04:07,137 (client:354) INFO: {'Role': 'Client #8', 'Round': 11, 'Results_raw': {'train_loss': 16.029205, 'val_loss': 14.605904, 'test_loss': 13.940912}}
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2024-11-13 18:04:42,604 (client:354) INFO: {'Role': 'Client #10', 'Round': 11, 'Results_raw': {'train_loss': 16.828199, 'val_loss': 15.233847, 'test_loss': 15.454852}}
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2024-11-13 18:05:19,777 (client:354) INFO: {'Role': 'Client #1', 'Round': 11, 'Results_raw': {'train_loss': 16.220439, 'val_loss': 15.706237, 'test_loss': 15.077962}}
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2024-11-13 18:05:57,839 (client:354) INFO: {'Role': 'Client #6', 'Round': 11, 'Results_raw': {'train_loss': 8.764516, 'val_loss': 7.790753, 'test_loss': 7.904092}}
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2024-11-13 18:06:34,997 (client:354) INFO: {'Role': 'Client #3', 'Round': 11, 'Results_raw': {'train_loss': 14.881816, 'val_loss': 13.321895, 'test_loss': 13.316753}}
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2024-11-13 18:07:15,285 (client:354) INFO: {'Role': 'Client #2', 'Round': 11, 'Results_raw': {'train_loss': 15.535219, 'val_loss': 14.143899, 'test_loss': 14.320172}}
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2024-11-13 18:07:15,290 (server:615) INFO: {'Role': 'Server #', 'Round': 10, 'Results_weighted_avg': {'test_loss': np.float64(59463.601111), 'test_avg_loss': np.float64(17.530543), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61362.858676), 'val_avg_loss': np.float64(18.090465), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59463.601111), 'test_avg_loss': np.float64(17.530543), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61362.858676), 'val_avg_loss': np.float64(18.090465), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8437.761146), 'test_loss_bottom_decile': np.float64(50201.400146), 'test_loss_top_decile': np.float64(67297.409546), 'test_loss_min': np.float64(38495.46759), 'test_loss_max': np.float64(67297.409546), 'test_loss_bottom10%': np.float64(38495.46759), 'test_loss_top10%': np.float64(67297.409546), 'test_loss_cos1': np.float64(0.990082), 'test_loss_entropy': np.float64(2.291645), 'test_avg_loss_std': np.float64(2.487548), 'test_avg_loss_bottom_decile': np.float64(14.799941), 'test_avg_loss_top_decile': np.float64(19.840038), 'test_avg_loss_min': np.float64(11.3489), 'test_avg_loss_max': np.float64(19.840038), 'test_avg_loss_bottom10%': np.float64(11.3489), 'test_avg_loss_top10%': np.float64(19.840038), 'test_avg_loss_cos1': np.float64(0.990082), 'test_avg_loss_entropy': np.float64(2.291645), 'val_loss_std': np.float64(8649.217256), 'val_loss_bottom_decile': np.float64(52442.055908), 'val_loss_top_decile': np.float64(68981.978943), 'val_loss_min': np.float64(39376.008636), 'val_loss_max': np.float64(68981.978943), 'val_loss_bottom10%': np.float64(39376.008636), 'val_loss_top10%': np.float64(68981.978943), 'val_loss_cos1': np.float64(0.990212), 'val_loss_entropy': np.float64(2.291736), 'val_avg_loss_std': np.float64(2.549887), 'val_avg_loss_bottom_decile': np.float64(15.460512), 'val_avg_loss_top_decile': np.float64(20.336668), 'val_avg_loss_min': np.float64(11.608493), 'val_avg_loss_max': np.float64(20.336668), 'val_avg_loss_bottom10%': np.float64(11.608493), 'val_avg_loss_top10%': np.float64(20.336668), 'val_avg_loss_cos1': np.float64(0.990212), 'val_avg_loss_entropy': np.float64(2.291736)}}
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2024-11-13 18:07:15,334 (server:353) INFO: Server: Starting evaluation at the end of round 11.
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2024-11-13 18:07:15,334 (server:359) INFO: ----------- Starting a new training round (Round #12) -------------
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2024-11-13 18:08:51,749 (client:354) INFO: {'Role': 'Client #9', 'Round': 12, 'Results_raw': {'train_loss': 14.652149, 'val_loss': 14.30514, 'test_loss': 13.896787}}
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2024-11-13 18:09:27,999 (client:354) INFO: {'Role': 'Client #2', 'Round': 12, 'Results_raw': {'train_loss': 15.56463, 'val_loss': 14.185131, 'test_loss': 14.444776}}
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2024-11-13 18:10:03,631 (client:354) INFO: {'Role': 'Client #10', 'Round': 12, 'Results_raw': {'train_loss': 16.766348, 'val_loss': 14.891724, 'test_loss': 15.12419}}
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2024-11-13 18:10:38,226 (client:354) INFO: {'Role': 'Client #7', 'Round': 12, 'Results_raw': {'train_loss': 11.281255, 'val_loss': 9.769226, 'test_loss': 9.999585}}
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2024-11-13 18:11:12,807 (client:354) INFO: {'Role': 'Client #3', 'Round': 12, 'Results_raw': {'train_loss': 14.866012, 'val_loss': 13.230634, 'test_loss': 13.216783}}
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2024-11-13 18:11:50,967 (client:354) INFO: {'Role': 'Client #5', 'Round': 12, 'Results_raw': {'train_loss': 13.406448, 'val_loss': 12.559647, 'test_loss': 13.245424}}
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2024-11-13 18:12:30,585 (client:354) INFO: {'Role': 'Client #1', 'Round': 12, 'Results_raw': {'train_loss': 16.063074, 'val_loss': 15.405339, 'test_loss': 14.703788}}
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2024-11-13 18:13:06,001 (client:354) INFO: {'Role': 'Client #6', 'Round': 12, 'Results_raw': {'train_loss': 8.744088, 'val_loss': 7.765585, 'test_loss': 7.82997}}
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2024-11-13 18:13:44,018 (client:354) INFO: {'Role': 'Client #4', 'Round': 12, 'Results_raw': {'train_loss': 16.201656, 'val_loss': 14.433699, 'test_loss': 14.812071}}
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2024-11-13 18:14:21,758 (client:354) INFO: {'Role': 'Client #8', 'Round': 12, 'Results_raw': {'train_loss': 16.016524, 'val_loss': 14.609322, 'test_loss': 14.006602}}
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2024-11-13 18:14:21,762 (server:615) INFO: {'Role': 'Server #', 'Round': 11, 'Results_weighted_avg': {'test_loss': np.float64(60039.013748), 'test_avg_loss': np.float64(17.700181), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61914.301981), 'val_avg_loss': np.float64(18.253037), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60039.013748), 'test_avg_loss': np.float64(17.700181), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61914.301981), 'val_avg_loss': np.float64(18.253037), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8451.885557), 'test_loss_bottom_decile': np.float64(50823.35321), 'test_loss_top_decile': np.float64(68215.652222), 'test_loss_min': np.float64(39022.042206), 'test_loss_max': np.float64(68215.652222), 'test_loss_bottom10%': np.float64(39022.042206), 'test_loss_top10%': np.float64(68215.652222), 'test_loss_cos1': np.float64(0.990236), 'test_loss_entropy': np.float64(2.291829), 'test_avg_loss_std': np.float64(2.491712), 'test_avg_loss_bottom_decile': np.float64(14.9833), 'test_avg_loss_top_decile': np.float64(20.110747), 'test_avg_loss_min': np.float64(11.50414), 'test_avg_loss_max': np.float64(20.110747), 'test_avg_loss_bottom10%': np.float64(11.50414), 'test_avg_loss_top10%': np.float64(20.110747), 'test_avg_loss_cos1': np.float64(0.990236), 'test_avg_loss_entropy': np.float64(2.291829), 'val_loss_std': np.float64(8587.517847), 'val_loss_bottom_decile': np.float64(53167.659607), 'val_loss_top_decile': np.float64(69277.088745), 'val_loss_min': np.float64(39936.054291), 'val_loss_max': np.float64(69277.088745), 'val_loss_bottom10%': np.float64(39936.054291), 'val_loss_top10%': np.float64(69277.088745), 'val_loss_cos1': np.float64(0.990518), 'val_loss_entropy': np.float64(2.292077), 'val_avg_loss_std': np.float64(2.531697), 'val_avg_loss_bottom_decile': np.float64(15.674428), 'val_avg_loss_top_decile': np.float64(20.42367), 'val_avg_loss_min': np.float64(11.773601), 'val_avg_loss_max': np.float64(20.42367), 'val_avg_loss_bottom10%': np.float64(11.773601), 'val_avg_loss_top10%': np.float64(20.42367), 'val_avg_loss_cos1': np.float64(0.990518), 'val_avg_loss_entropy': np.float64(2.292077)}}
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2024-11-13 18:14:21,809 (server:353) INFO: Server: Starting evaluation at the end of round 12.
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2024-11-13 18:14:21,810 (server:359) INFO: ----------- Starting a new training round (Round #13) -------------
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2024-11-13 18:16:03,500 (client:354) INFO: {'Role': 'Client #10', 'Round': 13, 'Results_raw': {'train_loss': 16.746639, 'val_loss': 15.120591, 'test_loss': 15.369516}}
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2024-11-13 18:16:41,752 (client:354) INFO: {'Role': 'Client #5', 'Round': 13, 'Results_raw': {'train_loss': 13.32691, 'val_loss': 12.231117, 'test_loss': 12.824095}}
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2024-11-13 18:17:19,908 (client:354) INFO: {'Role': 'Client #7', 'Round': 13, 'Results_raw': {'train_loss': 11.165566, 'val_loss': 9.591328, 'test_loss': 9.756672}}
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2024-11-13 18:18:13,375 (client:354) INFO: {'Role': 'Client #8', 'Round': 13, 'Results_raw': {'train_loss': 15.861569, 'val_loss': 14.634007, 'test_loss': 13.960705}}
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2024-11-13 18:19:14,149 (client:354) INFO: {'Role': 'Client #1', 'Round': 13, 'Results_raw': {'train_loss': 15.983807, 'val_loss': 15.209967, 'test_loss': 14.525642}}
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2024-11-13 18:20:11,635 (client:354) INFO: {'Role': 'Client #9', 'Round': 13, 'Results_raw': {'train_loss': 14.695331, 'val_loss': 13.746203, 'test_loss': 13.457526}}
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2024-11-13 18:21:08,215 (client:354) INFO: {'Role': 'Client #6', 'Round': 13, 'Results_raw': {'train_loss': 8.631729, 'val_loss': 7.769668, 'test_loss': 7.853201}}
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2024-11-13 18:22:06,644 (client:354) INFO: {'Role': 'Client #3', 'Round': 13, 'Results_raw': {'train_loss': 14.785795, 'val_loss': 13.258225, 'test_loss': 13.187381}}
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2024-11-13 18:23:03,834 (client:354) INFO: {'Role': 'Client #2', 'Round': 13, 'Results_raw': {'train_loss': 15.440433, 'val_loss': 14.043094, 'test_loss': 14.409996}}
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2024-11-13 18:24:04,390 (client:354) INFO: {'Role': 'Client #4', 'Round': 13, 'Results_raw': {'train_loss': 16.181058, 'val_loss': 14.129516, 'test_loss': 14.494269}}
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2024-11-13 18:24:04,394 (server:615) INFO: {'Role': 'Server #', 'Round': 12, 'Results_weighted_avg': {'test_loss': np.float64(59982.164761), 'test_avg_loss': np.float64(17.683421), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61737.530643), 'val_avg_loss': np.float64(18.200923), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59982.164761), 'test_avg_loss': np.float64(17.683421), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61737.530643), 'val_avg_loss': np.float64(18.200923), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8325.26029), 'test_loss_bottom_decile': np.float64(50482.589844), 'test_loss_top_decile': np.float64(68309.945862), 'test_loss_min': np.float64(39622.182281), 'test_loss_max': np.float64(68309.945862), 'test_loss_bottom10%': np.float64(39622.182281), 'test_loss_top10%': np.float64(68309.945862), 'test_loss_cos1': np.float64(0.990505), 'test_loss_entropy': np.float64(2.292183), 'test_avg_loss_std': np.float64(2.454381), 'test_avg_loss_bottom_decile': np.float64(14.882839), 'test_avg_loss_top_decile': np.float64(20.138545), 'test_avg_loss_min': np.float64(11.681068), 'test_avg_loss_max': np.float64(20.138545), 'test_avg_loss_bottom10%': np.float64(11.681068), 'test_avg_loss_top10%': np.float64(20.138545), 'test_avg_loss_cos1': np.float64(0.990505), 'test_avg_loss_entropy': np.float64(2.292183), 'val_loss_std': np.float64(8399.518068), 'val_loss_bottom_decile': np.float64(52859.003357), 'val_loss_top_decile': np.float64(69108.760254), 'val_loss_min': np.float64(40510.47287), 'val_loss_max': np.float64(69108.760254), 'val_loss_bottom10%': np.float64(40510.47287), 'val_loss_top10%': np.float64(69108.760254), 'val_loss_cos1': np.float64(0.990871), 'val_loss_entropy': np.float64(2.292527), 'val_avg_loss_std': np.float64(2.476273), 'val_avg_loss_bottom_decile': np.float64(15.583433), 'val_avg_loss_top_decile': np.float64(20.374045), 'val_avg_loss_min': np.float64(11.942946), 'val_avg_loss_max': np.float64(20.374045), 'val_avg_loss_bottom10%': np.float64(11.942946), 'val_avg_loss_top10%': np.float64(20.374045), 'val_avg_loss_cos1': np.float64(0.990871), 'val_avg_loss_entropy': np.float64(2.292527)}}
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2024-11-13 18:24:04,442 (server:353) INFO: Server: Starting evaluation at the end of round 13.
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2024-11-13 18:24:04,443 (server:359) INFO: ----------- Starting a new training round (Round #14) -------------
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2024-11-13 18:27:07,761 (client:354) INFO: {'Role': 'Client #9', 'Round': 14, 'Results_raw': {'train_loss': 14.581482, 'val_loss': 13.902569, 'test_loss': 13.568201}}
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2024-11-13 18:28:03,611 (client:354) INFO: {'Role': 'Client #4', 'Round': 14, 'Results_raw': {'train_loss': 16.06688, 'val_loss': 14.042477, 'test_loss': 14.388266}}
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2024-11-13 18:29:01,578 (client:354) INFO: {'Role': 'Client #8', 'Round': 14, 'Results_raw': {'train_loss': 15.81905, 'val_loss': 14.672916, 'test_loss': 13.965258}}
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2024-11-13 18:29:59,467 (client:354) INFO: {'Role': 'Client #6', 'Round': 14, 'Results_raw': {'train_loss': 8.595376, 'val_loss': 7.830262, 'test_loss': 7.907828}}
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2024-11-13 18:30:59,917 (client:354) INFO: {'Role': 'Client #7', 'Round': 14, 'Results_raw': {'train_loss': 11.0231, 'val_loss': 9.611591, 'test_loss': 9.782088}}
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2024-11-13 18:31:59,007 (client:354) INFO: {'Role': 'Client #5', 'Round': 14, 'Results_raw': {'train_loss': 13.270335, 'val_loss': 12.368869, 'test_loss': 12.948589}}
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2024-11-13 18:32:58,931 (client:354) INFO: {'Role': 'Client #3', 'Round': 14, 'Results_raw': {'train_loss': 14.632293, 'val_loss': 13.454963, 'test_loss': 13.424592}}
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2024-11-13 18:33:57,322 (client:354) INFO: {'Role': 'Client #10', 'Round': 14, 'Results_raw': {'train_loss': 16.661484, 'val_loss': 15.106148, 'test_loss': 15.302061}}
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2024-11-13 18:34:54,546 (client:354) INFO: {'Role': 'Client #1', 'Round': 14, 'Results_raw': {'train_loss': 15.878499, 'val_loss': 15.383103, 'test_loss': 14.723026}}
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2024-11-13 18:35:53,888 (client:354) INFO: {'Role': 'Client #2', 'Round': 14, 'Results_raw': {'train_loss': 15.317593, 'val_loss': 14.033518, 'test_loss': 14.331535}}
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2024-11-13 18:35:53,892 (server:615) INFO: {'Role': 'Server #', 'Round': 13, 'Results_weighted_avg': {'test_loss': np.float64(60653.54501), 'test_avg_loss': np.float64(17.881352), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62539.575003), 'val_avg_loss': np.float64(18.437375), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60653.54501), 'test_avg_loss': np.float64(17.881352), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62539.575003), 'val_avg_loss': np.float64(18.437375), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8564.517306), 'test_loss_bottom_decile': np.float64(51863.185852), 'test_loss_top_decile': np.float64(69382.618835), 'test_loss_min': np.float64(39223.592743), 'test_loss_max': np.float64(69382.618835), 'test_loss_bottom10%': np.float64(39223.592743), 'test_loss_top10%': np.float64(69382.618835), 'test_loss_cos1': np.float64(0.990177), 'test_loss_entropy': np.float64(2.291757), 'test_avg_loss_std': np.float64(2.524917), 'test_avg_loss_bottom_decile': np.float64(15.289854), 'test_avg_loss_top_decile': np.float64(20.454781), 'test_avg_loss_min': np.float64(11.563559), 'test_avg_loss_max': np.float64(20.454781), 'test_avg_loss_bottom10%': np.float64(11.563559), 'test_avg_loss_top10%': np.float64(20.454781), 'test_avg_loss_cos1': np.float64(0.990177), 'test_avg_loss_entropy': np.float64(2.291757), 'val_loss_std': np.float64(8650.909209), 'val_loss_bottom_decile': np.float64(54368.453979), 'val_loss_top_decile': np.float64(70049.902527), 'val_loss_min': np.float64(40150.809357), 'val_loss_max': np.float64(70049.902527), 'val_loss_bottom10%': np.float64(40150.809357), 'val_loss_top10%': np.float64(70049.902527), 'val_loss_cos1': np.float64(0.990568), 'val_loss_entropy': np.float64(2.292114), 'val_avg_loss_std': np.float64(2.550386), 'val_avg_loss_bottom_decile': np.float64(16.028436), 'val_avg_loss_top_decile': np.float64(20.651504), 'val_avg_loss_min': np.float64(11.836913), 'val_avg_loss_max': np.float64(20.651504), 'val_avg_loss_bottom10%': np.float64(11.836913), 'val_avg_loss_top10%': np.float64(20.651504), 'val_avg_loss_cos1': np.float64(0.990568), 'val_avg_loss_entropy': np.float64(2.292114)}}
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2024-11-13 18:35:53,933 (server:353) INFO: Server: Starting evaluation at the end of round 14.
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2024-11-13 18:35:53,933 (server:359) INFO: ----------- Starting a new training round (Round #15) -------------
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2024-11-13 18:38:58,197 (client:354) INFO: {'Role': 'Client #7', 'Round': 15, 'Results_raw': {'train_loss': 10.964189, 'val_loss': 9.48557, 'test_loss': 9.706703}}
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2024-11-13 18:39:56,048 (client:354) INFO: {'Role': 'Client #3', 'Round': 15, 'Results_raw': {'train_loss': 14.66742, 'val_loss': 13.354563, 'test_loss': 13.260668}}
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2024-11-13 18:40:55,219 (client:354) INFO: {'Role': 'Client #4', 'Round': 15, 'Results_raw': {'train_loss': 16.02542, 'val_loss': 14.281184, 'test_loss': 14.702021}}
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2024-11-13 18:41:51,898 (client:354) INFO: {'Role': 'Client #5', 'Round': 15, 'Results_raw': {'train_loss': 13.239828, 'val_loss': 12.299014, 'test_loss': 12.925325}}
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2024-11-13 18:42:50,451 (client:354) INFO: {'Role': 'Client #9', 'Round': 15, 'Results_raw': {'train_loss': 14.499803, 'val_loss': 14.214461, 'test_loss': 13.798811}}
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2024-11-13 18:43:48,428 (client:354) INFO: {'Role': 'Client #2', 'Round': 15, 'Results_raw': {'train_loss': 15.249924, 'val_loss': 13.843132, 'test_loss': 14.18825}}
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2024-11-13 18:44:45,884 (client:354) INFO: {'Role': 'Client #6', 'Round': 15, 'Results_raw': {'train_loss': 8.506414, 'val_loss': 7.732921, 'test_loss': 7.81299}}
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2024-11-13 18:45:42,720 (client:354) INFO: {'Role': 'Client #8', 'Round': 15, 'Results_raw': {'train_loss': 15.566646, 'val_loss': 14.219797, 'test_loss': 13.59025}}
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2024-11-13 18:46:39,227 (client:354) INFO: {'Role': 'Client #10', 'Round': 15, 'Results_raw': {'train_loss': 16.504313, 'val_loss': 14.935191, 'test_loss': 15.179289}}
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2024-11-13 18:47:36,604 (client:354) INFO: {'Role': 'Client #1', 'Round': 15, 'Results_raw': {'train_loss': 15.88007, 'val_loss': 15.341065, 'test_loss': 14.63}}
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2024-11-13 18:47:36,608 (server:615) INFO: {'Role': 'Server #', 'Round': 14, 'Results_weighted_avg': {'test_loss': np.float64(60497.18793), 'test_avg_loss': np.float64(17.835256), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62359.769537), 'val_avg_loss': np.float64(18.384366), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60497.18793), 'test_avg_loss': np.float64(17.835256), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62359.769537), 'val_avg_loss': np.float64(18.384366), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8482.388224), 'test_loss_bottom_decile': np.float64(51628.98407), 'test_loss_top_decile': np.float64(68789.669495), 'test_loss_min': np.float64(39206.251251), 'test_loss_max': np.float64(68789.669495), 'test_loss_bottom10%': np.float64(39206.251251), 'test_loss_top10%': np.float64(68789.669495), 'test_loss_cos1': np.float64(0.990313), 'test_loss_entropy': np.float64(2.291898), 'test_avg_loss_std': np.float64(2.500704), 'test_avg_loss_bottom_decile': np.float64(15.220809), 'test_avg_loss_top_decile': np.float64(20.279973), 'test_avg_loss_min': np.float64(11.558447), 'test_avg_loss_max': np.float64(20.279973), 'test_avg_loss_bottom10%': np.float64(11.558447), 'test_avg_loss_top10%': np.float64(20.279973), 'test_avg_loss_cos1': np.float64(0.990313), 'test_avg_loss_entropy': np.float64(2.291898), 'val_loss_std': np.float64(8574.969597), 'val_loss_bottom_decile': np.float64(54138.036926), 'val_loss_top_decile': np.float64(69810.099731), 'val_loss_min': np.float64(40147.497009), 'val_loss_max': np.float64(69810.099731), 'val_loss_bottom10%': np.float64(40147.497009), 'val_loss_top10%': np.float64(69810.099731), 'val_loss_cos1': np.float64(0.990678), 'val_loss_entropy': np.float64(2.292238), 'val_avg_loss_std': np.float64(2.527998), 'val_avg_loss_bottom_decile': np.float64(15.960506), 'val_avg_loss_top_decile': np.float64(20.580808), 'val_avg_loss_min': np.float64(11.835937), 'val_avg_loss_max': np.float64(20.580808), 'val_avg_loss_bottom10%': np.float64(11.835937), 'val_avg_loss_top10%': np.float64(20.580808), 'val_avg_loss_cos1': np.float64(0.990678), 'val_avg_loss_entropy': np.float64(2.292238)}}
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2024-11-13 18:47:36,646 (server:353) INFO: Server: Starting evaluation at the end of round 15.
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2024-11-13 18:47:36,647 (server:359) INFO: ----------- Starting a new training round (Round #16) -------------
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2024-11-13 18:50:40,099 (client:354) INFO: {'Role': 'Client #2', 'Round': 16, 'Results_raw': {'train_loss': 15.194507, 'val_loss': 14.00751, 'test_loss': 14.239741}}
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2024-11-13 18:51:38,104 (client:354) INFO: {'Role': 'Client #9', 'Round': 16, 'Results_raw': {'train_loss': 14.416825, 'val_loss': 13.782995, 'test_loss': 13.387912}}
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2024-11-13 18:52:37,246 (client:354) INFO: {'Role': 'Client #5', 'Round': 16, 'Results_raw': {'train_loss': 13.151361, 'val_loss': 12.171831, 'test_loss': 12.792502}}
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2024-11-13 18:53:33,011 (client:354) INFO: {'Role': 'Client #8', 'Round': 16, 'Results_raw': {'train_loss': 15.578161, 'val_loss': 14.872854, 'test_loss': 14.153944}}
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2024-11-13 18:54:30,971 (client:354) INFO: {'Role': 'Client #6', 'Round': 16, 'Results_raw': {'train_loss': 8.518965, 'val_loss': 7.65797, 'test_loss': 7.739843}}
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2024-11-13 18:55:31,276 (client:354) INFO: {'Role': 'Client #10', 'Round': 16, 'Results_raw': {'train_loss': 16.43828, 'val_loss': 14.803889, 'test_loss': 15.106868}}
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2024-11-13 18:56:29,681 (client:354) INFO: {'Role': 'Client #4', 'Round': 16, 'Results_raw': {'train_loss': 15.874809, 'val_loss': 13.904741, 'test_loss': 14.334659}}
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2024-11-13 18:57:27,718 (client:354) INFO: {'Role': 'Client #3', 'Round': 16, 'Results_raw': {'train_loss': 14.612601, 'val_loss': 13.206851, 'test_loss': 13.271001}}
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2024-11-13 18:58:23,948 (client:354) INFO: {'Role': 'Client #1', 'Round': 16, 'Results_raw': {'train_loss': 15.85587, 'val_loss': 15.143779, 'test_loss': 14.486741}}
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2024-11-13 18:59:21,380 (client:354) INFO: {'Role': 'Client #7', 'Round': 16, 'Results_raw': {'train_loss': 11.11432, 'val_loss': 9.642963, 'test_loss': 9.925441}}
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2024-11-13 18:59:21,384 (server:615) INFO: {'Role': 'Server #', 'Round': 15, 'Results_weighted_avg': {'test_loss': np.float64(59454.617584), 'test_avg_loss': np.float64(17.527894), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61214.014703), 'val_avg_loss': np.float64(18.046585), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59454.617584), 'test_avg_loss': np.float64(17.527894), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61214.014703), 'val_avg_loss': np.float64(18.046585), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8012.002147), 'test_loss_bottom_decile': np.float64(50623.868591), 'test_loss_top_decile': np.float64(67072.698975), 'test_loss_min': np.float64(39598.426208), 'test_loss_max': np.float64(67072.698975), 'test_loss_bottom10%': np.float64(39598.426208), 'test_loss_top10%': np.float64(67072.698975), 'test_loss_cos1': np.float64(0.991042), 'test_loss_entropy': np.float64(2.292776), 'test_avg_loss_std': np.float64(2.362029), 'test_avg_loss_bottom_decile': np.float64(14.92449), 'test_avg_loss_top_decile': np.float64(19.773791), 'test_avg_loss_min': np.float64(11.674064), 'test_avg_loss_max': np.float64(19.773791), 'test_avg_loss_bottom10%': np.float64(11.674064), 'test_avg_loss_top10%': np.float64(19.773791), 'test_avg_loss_cos1': np.float64(0.991042), 'test_avg_loss_entropy': np.float64(2.292776), 'val_loss_std': np.float64(8097.263347), 'val_loss_bottom_decile': np.float64(52945.160095), 'val_loss_top_decile': np.float64(68367.591553), 'val_loss_min': np.float64(40517.506958), 'val_loss_max': np.float64(68367.591553), 'val_loss_bottom10%': np.float64(40517.506958), 'val_loss_top10%': np.float64(68367.591553), 'val_loss_cos1': np.float64(0.991364), 'val_loss_entropy': np.float64(2.293078), 'val_avg_loss_std': np.float64(2.387165), 'val_avg_loss_bottom_decile': np.float64(15.608833), 'val_avg_loss_top_decile': np.float64(20.15554), 'val_avg_loss_min': np.float64(11.94502), 'val_avg_loss_max': np.float64(20.15554), 'val_avg_loss_bottom10%': np.float64(11.94502), 'val_avg_loss_top10%': np.float64(20.15554), 'val_avg_loss_cos1': np.float64(0.991364), 'val_avg_loss_entropy': np.float64(2.293078)}}
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2024-11-13 18:59:21,424 (server:353) INFO: Server: Starting evaluation at the end of round 16.
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2024-11-13 18:59:21,425 (server:359) INFO: ----------- Starting a new training round (Round #17) -------------
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2024-11-13 19:02:27,577 (client:354) INFO: {'Role': 'Client #10', 'Round': 17, 'Results_raw': {'train_loss': 16.450717, 'val_loss': 14.789544, 'test_loss': 15.02974}}
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2024-11-13 19:03:25,442 (client:354) INFO: {'Role': 'Client #5', 'Round': 17, 'Results_raw': {'train_loss': 13.125924, 'val_loss': 12.285564, 'test_loss': 12.834689}}
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2024-11-13 19:04:24,629 (client:354) INFO: {'Role': 'Client #3', 'Round': 17, 'Results_raw': {'train_loss': 14.434723, 'val_loss': 13.491197, 'test_loss': 13.475586}}
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2024-11-13 19:05:23,825 (client:354) INFO: {'Role': 'Client #7', 'Round': 17, 'Results_raw': {'train_loss': 11.055547, 'val_loss': 9.342424, 'test_loss': 9.533214}}
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2024-11-13 19:06:21,752 (client:354) INFO: {'Role': 'Client #2', 'Round': 17, 'Results_raw': {'train_loss': 15.017509, 'val_loss': 13.709677, 'test_loss': 14.006933}}
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2024-11-13 19:07:18,534 (client:354) INFO: {'Role': 'Client #4', 'Round': 17, 'Results_raw': {'train_loss': 15.893601, 'val_loss': 14.001044, 'test_loss': 14.398496}}
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2024-11-13 19:08:16,872 (client:354) INFO: {'Role': 'Client #8', 'Round': 17, 'Results_raw': {'train_loss': 15.453473, 'val_loss': 14.639609, 'test_loss': 13.927719}}
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2024-11-13 19:09:15,700 (client:354) INFO: {'Role': 'Client #9', 'Round': 17, 'Results_raw': {'train_loss': 14.437107, 'val_loss': 14.024462, 'test_loss': 13.617148}}
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2024-11-13 19:10:14,452 (client:354) INFO: {'Role': 'Client #6', 'Round': 17, 'Results_raw': {'train_loss': 8.510048, 'val_loss': 7.65461, 'test_loss': 7.77297}}
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2024-11-13 19:11:13,242 (client:354) INFO: {'Role': 'Client #1', 'Round': 17, 'Results_raw': {'train_loss': 15.759724, 'val_loss': 14.911938, 'test_loss': 14.365867}}
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2024-11-13 19:11:13,247 (server:615) INFO: {'Role': 'Server #', 'Round': 16, 'Results_weighted_avg': {'test_loss': np.float64(60160.686554), 'test_avg_loss': np.float64(17.736051), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61937.984171), 'val_avg_loss': np.float64(18.260019), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60160.686554), 'test_avg_loss': np.float64(17.736051), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61937.984171), 'val_avg_loss': np.float64(18.260019), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8271.269924), 'test_loss_bottom_decile': np.float64(51307.898254), 'test_loss_top_decile': np.float64(67821.623596), 'test_loss_min': np.float64(39414.994263), 'test_loss_max': np.float64(67821.623596), 'test_loss_bottom10%': np.float64(39414.994263), 'test_loss_top10%': np.float64(67821.623596), 'test_loss_cos1': np.float64(0.990681), 'test_loss_entropy': np.float64(2.292325), 'test_avg_loss_std': np.float64(2.438464), 'test_avg_loss_bottom_decile': np.float64(15.126149), 'test_avg_loss_top_decile': np.float64(19.994582), 'test_avg_loss_min': np.float64(11.619987), 'test_avg_loss_max': np.float64(19.994582), 'test_avg_loss_bottom10%': np.float64(11.619987), 'test_avg_loss_top10%': np.float64(19.994582), 'test_avg_loss_cos1': np.float64(0.990681), 'test_avg_loss_entropy': np.float64(2.292325), 'val_loss_std': np.float64(8379.41078), 'val_loss_bottom_decile': np.float64(53691.868408), 'val_loss_top_decile': np.float64(69232.970947), 'val_loss_min': np.float64(40323.787201), 'val_loss_max': np.float64(69232.970947), 'val_loss_bottom10%': np.float64(40323.787201), 'val_loss_top10%': np.float64(69232.970947), 'val_loss_cos1': np.float64(0.990972), 'val_loss_entropy': np.float64(2.292597), 'val_avg_loss_std': np.float64(2.470345), 'val_avg_loss_bottom_decile': np.float64(15.828971), 'val_avg_loss_top_decile': np.float64(20.410664), 'val_avg_loss_min': np.float64(11.887909), 'val_avg_loss_max': np.float64(20.410664), 'val_avg_loss_bottom10%': np.float64(11.887909), 'val_avg_loss_top10%': np.float64(20.410664), 'val_avg_loss_cos1': np.float64(0.990972), 'val_avg_loss_entropy': np.float64(2.292597)}}
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2024-11-13 19:11:13,289 (server:353) INFO: Server: Starting evaluation at the end of round 17.
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2024-11-13 19:11:13,289 (server:359) INFO: ----------- Starting a new training round (Round #18) -------------
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2024-11-13 19:13:50,396 (client:354) INFO: {'Role': 'Client #3', 'Round': 18, 'Results_raw': {'train_loss': 14.384479, 'val_loss': 13.10821, 'test_loss': 13.184899}}
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2024-11-13 19:14:26,111 (client:354) INFO: {'Role': 'Client #5', 'Round': 18, 'Results_raw': {'train_loss': 13.055677, 'val_loss': 12.25645, 'test_loss': 12.903309}}
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2024-11-13 19:15:04,171 (client:354) INFO: {'Role': 'Client #7', 'Round': 18, 'Results_raw': {'train_loss': 10.82114, 'val_loss': 9.34428, 'test_loss': 9.581294}}
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2024-11-13 19:15:39,394 (client:354) INFO: {'Role': 'Client #9', 'Round': 18, 'Results_raw': {'train_loss': 14.264831, 'val_loss': 13.805784, 'test_loss': 13.387256}}
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2024-11-13 19:16:14,361 (client:354) INFO: {'Role': 'Client #10', 'Round': 18, 'Results_raw': {'train_loss': 16.372037, 'val_loss': 14.817302, 'test_loss': 14.995467}}
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2024-11-13 19:16:52,646 (client:354) INFO: {'Role': 'Client #1', 'Round': 18, 'Results_raw': {'train_loss': 15.707471, 'val_loss': 15.073223, 'test_loss': 14.496129}}
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2024-11-13 19:17:31,697 (client:354) INFO: {'Role': 'Client #4', 'Round': 18, 'Results_raw': {'train_loss': 15.825358, 'val_loss': 13.914211, 'test_loss': 14.296348}}
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2024-11-13 19:18:10,479 (client:354) INFO: {'Role': 'Client #8', 'Round': 18, 'Results_raw': {'train_loss': 15.441463, 'val_loss': 14.03229, 'test_loss': 13.356361}}
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2024-11-13 19:18:45,498 (client:354) INFO: {'Role': 'Client #2', 'Round': 18, 'Results_raw': {'train_loss': 14.999939, 'val_loss': 13.59899, 'test_loss': 14.039787}}
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2024-11-13 19:19:19,890 (client:354) INFO: {'Role': 'Client #6', 'Round': 18, 'Results_raw': {'train_loss': 8.525068, 'val_loss': 7.535335, 'test_loss': 7.667049}}
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2024-11-13 19:19:19,901 (server:615) INFO: {'Role': 'Server #', 'Round': 17, 'Results_weighted_avg': {'test_loss': np.float64(59823.198685), 'test_avg_loss': np.float64(17.636556), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61579.155255), 'val_avg_loss': np.float64(18.154232), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59823.198685), 'test_avg_loss': np.float64(17.636556), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61579.155255), 'val_avg_loss': np.float64(18.154232), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8083.240258), 'test_loss_bottom_decile': np.float64(50925.6922), 'test_loss_top_decile': np.float64(67349.677917), 'test_loss_min': np.float64(39637.620026), 'test_loss_max': np.float64(67349.677917), 'test_loss_bottom10%': np.float64(39637.620026), 'test_loss_top10%': np.float64(67349.677917), 'test_loss_cos1': np.float64(0.990995), 'test_loss_entropy': np.float64(2.292697), 'test_avg_loss_std': np.float64(2.383031), 'test_avg_loss_bottom_decile': np.float64(15.013471), 'test_avg_loss_top_decile': np.float64(19.855447), 'test_avg_loss_min': np.float64(11.685619), 'test_avg_loss_max': np.float64(19.855447), 'test_avg_loss_bottom10%': np.float64(11.685619), 'test_avg_loss_top10%': np.float64(19.855447), 'test_avg_loss_cos1': np.float64(0.990995), 'test_avg_loss_entropy': np.float64(2.292697), 'val_loss_std': np.float64(8158.099351), 'val_loss_bottom_decile': np.float64(53243.494568), 'val_loss_top_decile': np.float64(68600.836121), 'val_loss_min': np.float64(40579.776123), 'val_loss_max': np.float64(68600.836121), 'val_loss_bottom10%': np.float64(40579.776123), 'val_loss_top10%': np.float64(68600.836121), 'val_loss_cos1': np.float64(0.991338), 'val_loss_entropy': np.float64(2.293024), 'val_avg_loss_std': np.float64(2.4051), 'val_avg_loss_bottom_decile': np.float64(15.696785), 'val_avg_loss_top_decile': np.float64(20.224303), 'val_avg_loss_min': np.float64(11.963377), 'val_avg_loss_max': np.float64(20.224303), 'val_avg_loss_bottom10%': np.float64(11.963377), 'val_avg_loss_top10%': np.float64(20.224303), 'val_avg_loss_cos1': np.float64(0.991338), 'val_avg_loss_entropy': np.float64(2.293024)}}
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2024-11-13 19:19:19,934 (server:353) INFO: Server: Starting evaluation at the end of round 18.
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2024-11-13 19:19:19,934 (server:359) INFO: ----------- Starting a new training round (Round #19) -------------
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2024-11-13 19:20:49,216 (client:354) INFO: {'Role': 'Client #3', 'Round': 19, 'Results_raw': {'train_loss': 14.433758, 'val_loss': 13.162704, 'test_loss': 13.179182}}
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2024-11-13 19:21:21,344 (client:354) INFO: {'Role': 'Client #7', 'Round': 19, 'Results_raw': {'train_loss': 10.655392, 'val_loss': 9.649544, 'test_loss': 9.868978}}
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2024-11-13 19:21:56,079 (client:354) INFO: {'Role': 'Client #2', 'Round': 19, 'Results_raw': {'train_loss': 15.029788, 'val_loss': 13.638876, 'test_loss': 13.99524}}
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2024-11-13 19:22:27,967 (client:354) INFO: {'Role': 'Client #4', 'Round': 19, 'Results_raw': {'train_loss': 15.747287, 'val_loss': 13.924475, 'test_loss': 14.352978}}
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2024-11-13 19:22:59,863 (client:354) INFO: {'Role': 'Client #5', 'Round': 19, 'Results_raw': {'train_loss': 13.049154, 'val_loss': 12.163838, 'test_loss': 12.806781}}
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2024-11-13 19:23:32,764 (client:354) INFO: {'Role': 'Client #9', 'Round': 19, 'Results_raw': {'train_loss': 14.301257, 'val_loss': 13.860398, 'test_loss': 13.448203}}
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2024-11-13 19:24:05,357 (client:354) INFO: {'Role': 'Client #10', 'Round': 19, 'Results_raw': {'train_loss': 16.247601, 'val_loss': 14.852907, 'test_loss': 15.11995}}
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2024-11-13 19:24:38,300 (client:354) INFO: {'Role': 'Client #1', 'Round': 19, 'Results_raw': {'train_loss': 15.838533, 'val_loss': 15.148975, 'test_loss': 14.531424}}
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2024-11-13 19:25:12,210 (client:354) INFO: {'Role': 'Client #8', 'Round': 19, 'Results_raw': {'train_loss': 15.334929, 'val_loss': 14.296802, 'test_loss': 13.691978}}
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2024-11-13 19:25:45,128 (client:354) INFO: {'Role': 'Client #6', 'Round': 19, 'Results_raw': {'train_loss': 8.42415, 'val_loss': 7.584399, 'test_loss': 7.681841}}
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2024-11-13 19:25:45,132 (server:615) INFO: {'Role': 'Server #', 'Round': 18, 'Results_weighted_avg': {'test_loss': np.float64(60444.26615), 'test_avg_loss': np.float64(17.819654), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62257.005276), 'val_avg_loss': np.float64(18.35407), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60444.26615), 'test_avg_loss': np.float64(17.819654), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62257.005276), 'val_avg_loss': np.float64(18.35407), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8376.931404), 'test_loss_bottom_decile': np.float64(51814.675171), 'test_loss_top_decile': np.float64(67942.117249), 'test_loss_min': np.float64(39291.254333), 'test_loss_max': np.float64(67942.117249), 'test_loss_bottom10%': np.float64(39291.254333), 'test_loss_top10%': np.float64(67942.117249), 'test_loss_cos1': np.float64(0.990533), 'test_loss_entropy': np.float64(2.292138), 'test_avg_loss_std': np.float64(2.469614), 'test_avg_loss_bottom_decile': np.float64(15.275553), 'test_avg_loss_top_decile': np.float64(20.030105), 'test_avg_loss_min': np.float64(11.583507), 'test_avg_loss_max': np.float64(20.030105), 'test_avg_loss_bottom10%': np.float64(11.583507), 'test_avg_loss_top10%': np.float64(20.030105), 'test_avg_loss_cos1': np.float64(0.990533), 'test_avg_loss_entropy': np.float64(2.292138), 'val_loss_std': np.float64(8502.674733), 'val_loss_bottom_decile': np.float64(54400.270813), 'val_loss_top_decile': np.float64(69950.118042), 'val_loss_min': np.float64(40191.489227), 'val_loss_max': np.float64(69950.118042), 'val_loss_bottom10%': np.float64(40191.489227), 'val_loss_top10%': np.float64(69950.118042), 'val_loss_cos1': np.float64(0.990802), 'val_loss_entropy': np.float64(2.292389), 'val_avg_loss_std': np.float64(2.506685), 'val_avg_loss_bottom_decile': np.float64(16.037816), 'val_avg_loss_top_decile': np.float64(20.622087), 'val_avg_loss_min': np.float64(11.848906), 'val_avg_loss_max': np.float64(20.622087), 'val_avg_loss_bottom10%': np.float64(11.848906), 'val_avg_loss_top10%': np.float64(20.622087), 'val_avg_loss_cos1': np.float64(0.990802), 'val_avg_loss_entropy': np.float64(2.292389)}}
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2024-11-13 19:25:45,167 (server:353) INFO: Server: Starting evaluation at the end of round 19.
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2024-11-13 19:25:45,168 (server:359) INFO: ----------- Starting a new training round (Round #20) -------------
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2024-11-13 19:27:15,315 (client:354) INFO: {'Role': 'Client #3', 'Round': 20, 'Results_raw': {'train_loss': 14.277352, 'val_loss': 13.099904, 'test_loss': 13.139081}}
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2024-11-13 19:27:54,962 (client:354) INFO: {'Role': 'Client #4', 'Round': 20, 'Results_raw': {'train_loss': 15.695129, 'val_loss': 14.021453, 'test_loss': 14.330964}}
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2024-11-13 19:28:43,430 (client:354) INFO: {'Role': 'Client #1', 'Round': 20, 'Results_raw': {'train_loss': 15.552226, 'val_loss': 15.231765, 'test_loss': 14.689526}}
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2024-11-13 19:29:45,348 (client:354) INFO: {'Role': 'Client #5', 'Round': 20, 'Results_raw': {'train_loss': 12.928225, 'val_loss': 12.157451, 'test_loss': 12.72805}}
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2024-11-13 19:30:47,844 (client:354) INFO: {'Role': 'Client #9', 'Round': 20, 'Results_raw': {'train_loss': 14.229104, 'val_loss': 13.682528, 'test_loss': 13.279858}}
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2024-11-13 19:31:48,552 (client:354) INFO: {'Role': 'Client #8', 'Round': 20, 'Results_raw': {'train_loss': 15.320462, 'val_loss': 14.374873, 'test_loss': 13.725709}}
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2024-11-13 19:32:48,110 (client:354) INFO: {'Role': 'Client #7', 'Round': 20, 'Results_raw': {'train_loss': 10.650862, 'val_loss': 9.729783, 'test_loss': 9.830627}}
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2024-11-13 19:33:47,605 (client:354) INFO: {'Role': 'Client #6', 'Round': 20, 'Results_raw': {'train_loss': 8.387532, 'val_loss': 7.479094, 'test_loss': 7.621349}}
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2024-11-13 19:34:45,318 (client:354) INFO: {'Role': 'Client #2', 'Round': 20, 'Results_raw': {'train_loss': 15.038188, 'val_loss': 13.60114, 'test_loss': 13.920768}}
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2024-11-13 19:35:45,124 (client:354) INFO: {'Role': 'Client #10', 'Round': 20, 'Results_raw': {'train_loss': 16.202791, 'val_loss': 14.927766, 'test_loss': 15.17291}}
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2024-11-13 19:35:45,128 (server:615) INFO: {'Role': 'Server #', 'Round': 19, 'Results_weighted_avg': {'test_loss': np.float64(60470.799207), 'test_avg_loss': np.float64(17.827476), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62292.053616), 'val_avg_loss': np.float64(18.364403), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60470.799207), 'test_avg_loss': np.float64(17.827476), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62292.053616), 'val_avg_loss': np.float64(18.364403), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8362.406448), 'test_loss_bottom_decile': np.float64(51375.15918), 'test_loss_top_decile': np.float64(67918.88031), 'test_loss_min': np.float64(39558.015564), 'test_loss_max': np.float64(67918.88031), 'test_loss_bottom10%': np.float64(39558.015564), 'test_loss_top10%': np.float64(67918.88031), 'test_loss_cos1': np.float64(0.990573), 'test_loss_entropy': np.float64(2.292202), 'test_avg_loss_std': np.float64(2.465332), 'test_avg_loss_bottom_decile': np.float64(15.145979), 'test_avg_loss_top_decile': np.float64(20.023255), 'test_avg_loss_min': np.float64(11.662151), 'test_avg_loss_max': np.float64(20.023255), 'test_avg_loss_bottom10%': np.float64(11.662151), 'test_avg_loss_top10%': np.float64(20.023255), 'test_avg_loss_cos1': np.float64(0.990573), 'test_avg_loss_entropy': np.float64(2.292202), 'val_loss_std': np.float64(8497.048016), 'val_loss_bottom_decile': np.float64(53885.088806), 'val_loss_top_decile': np.float64(69980.191162), 'val_loss_min': np.float64(40436.067047), 'val_loss_max': np.float64(69980.191162), 'val_loss_bottom10%': np.float64(40436.067047), 'val_loss_top10%': np.float64(69980.191162), 'val_loss_cos1': np.float64(0.990824), 'val_loss_entropy': np.float64(2.292431), 'val_avg_loss_std': np.float64(2.505026), 'val_avg_loss_bottom_decile': np.float64(15.885934), 'val_avg_loss_top_decile': np.float64(20.630953), 'val_avg_loss_min': np.float64(11.92101), 'val_avg_loss_max': np.float64(20.630953), 'val_avg_loss_bottom10%': np.float64(11.92101), 'val_avg_loss_top10%': np.float64(20.630953), 'val_avg_loss_cos1': np.float64(0.990824), 'val_avg_loss_entropy': np.float64(2.292431)}}
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2024-11-13 19:35:45,167 (server:353) INFO: Server: Starting evaluation at the end of round 20.
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2024-11-13 19:35:45,167 (server:359) INFO: ----------- Starting a new training round (Round #21) -------------
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2024-11-13 19:38:58,386 (client:354) INFO: {'Role': 'Client #5', 'Round': 21, 'Results_raw': {'train_loss': 12.959956, 'val_loss': 12.099263, 'test_loss': 12.783917}}
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2024-11-13 19:39:56,932 (client:354) INFO: {'Role': 'Client #3', 'Round': 21, 'Results_raw': {'train_loss': 14.154833, 'val_loss': 12.913684, 'test_loss': 13.011596}}
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2024-11-13 19:40:55,580 (client:354) INFO: {'Role': 'Client #6', 'Round': 21, 'Results_raw': {'train_loss': 8.375103, 'val_loss': 7.66356, 'test_loss': 7.77629}}
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2024-11-13 19:41:55,217 (client:354) INFO: {'Role': 'Client #9', 'Round': 21, 'Results_raw': {'train_loss': 14.163849, 'val_loss': 13.755122, 'test_loss': 13.375347}}
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2024-11-13 19:42:54,735 (client:354) INFO: {'Role': 'Client #4', 'Round': 21, 'Results_raw': {'train_loss': 15.711673, 'val_loss': 13.89732, 'test_loss': 14.292064}}
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2024-11-13 19:43:53,549 (client:354) INFO: {'Role': 'Client #1', 'Round': 21, 'Results_raw': {'train_loss': 15.543006, 'val_loss': 14.940811, 'test_loss': 14.422258}}
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2024-11-13 19:44:51,917 (client:354) INFO: {'Role': 'Client #7', 'Round': 21, 'Results_raw': {'train_loss': 10.679037, 'val_loss': 9.154819, 'test_loss': 9.482813}}
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2024-11-13 19:45:51,669 (client:354) INFO: {'Role': 'Client #2', 'Round': 21, 'Results_raw': {'train_loss': 14.879176, 'val_loss': 13.409718, 'test_loss': 13.719017}}
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2024-11-13 19:46:51,045 (client:354) INFO: {'Role': 'Client #10', 'Round': 21, 'Results_raw': {'train_loss': 16.085005, 'val_loss': 14.880622, 'test_loss': 15.093659}}
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2024-11-13 19:47:49,289 (client:354) INFO: {'Role': 'Client #8', 'Round': 21, 'Results_raw': {'train_loss': 15.351934, 'val_loss': 14.361101, 'test_loss': 13.717666}}
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2024-11-13 19:47:49,294 (server:615) INFO: {'Role': 'Server #', 'Round': 20, 'Results_weighted_avg': {'test_loss': np.float64(59648.166986), 'test_avg_loss': np.float64(17.584955), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61422.929324), 'val_avg_loss': np.float64(18.108175), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59648.166986), 'test_avg_loss': np.float64(17.584955), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61422.929324), 'val_avg_loss': np.float64(18.108175), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8089.516037), 'test_loss_bottom_decile': np.float64(51047.526855), 'test_loss_top_decile': np.float64(67209.03009), 'test_loss_min': np.float64(39426.04126), 'test_loss_max': np.float64(67209.03009), 'test_loss_bottom10%': np.float64(39426.04126), 'test_loss_top10%': np.float64(67209.03009), 'test_loss_cos1': np.float64(0.990928), 'test_loss_entropy': np.float64(2.292626), 'test_avg_loss_std': np.float64(2.384881), 'test_avg_loss_bottom_decile': np.float64(15.049389), 'test_avg_loss_top_decile': np.float64(19.813983), 'test_avg_loss_min': np.float64(11.623243), 'test_avg_loss_max': np.float64(19.813983), 'test_avg_loss_bottom10%': np.float64(11.623243), 'test_avg_loss_top10%': np.float64(19.813983), 'test_avg_loss_cos1': np.float64(0.990928), 'test_avg_loss_entropy': np.float64(2.292626), 'val_loss_std': np.float64(8184.702382), 'val_loss_bottom_decile': np.float64(53454.087097), 'val_loss_top_decile': np.float64(68670.810181), 'val_loss_min': np.float64(40318.921417), 'val_loss_max': np.float64(68670.810181), 'val_loss_bottom10%': np.float64(40318.921417), 'val_loss_top10%': np.float64(68670.810181), 'val_loss_cos1': np.float64(0.991239), 'val_loss_entropy': np.float64(2.292913), 'val_avg_loss_std': np.float64(2.412943), 'val_avg_loss_bottom_decile': np.float64(15.75887), 'val_avg_loss_top_decile': np.float64(20.244932), 'val_avg_loss_min': np.float64(11.886474), 'val_avg_loss_max': np.float64(20.244932), 'val_avg_loss_bottom10%': np.float64(11.886474), 'val_avg_loss_top10%': np.float64(20.244932), 'val_avg_loss_cos1': np.float64(0.991239), 'val_avg_loss_entropy': np.float64(2.292913)}}
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2024-11-13 19:47:49,335 (server:353) INFO: Server: Starting evaluation at the end of round 21.
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2024-11-13 19:47:49,336 (server:359) INFO: ----------- Starting a new training round (Round #22) -------------
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2024-11-13 19:51:02,872 (client:354) INFO: {'Role': 'Client #4', 'Round': 22, 'Results_raw': {'train_loss': 15.581954, 'val_loss': 13.905294, 'test_loss': 14.368986}}
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2024-11-13 19:52:02,742 (client:354) INFO: {'Role': 'Client #8', 'Round': 22, 'Results_raw': {'train_loss': 15.186046, 'val_loss': 13.878938, 'test_loss': 13.265473}}
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2024-11-13 19:53:01,069 (client:354) INFO: {'Role': 'Client #2', 'Round': 22, 'Results_raw': {'train_loss': 14.816006, 'val_loss': 13.947877, 'test_loss': 14.221504}}
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2024-11-13 19:53:58,656 (client:354) INFO: {'Role': 'Client #6', 'Round': 22, 'Results_raw': {'train_loss': 8.378361, 'val_loss': 7.667451, 'test_loss': 7.765617}}
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2024-11-13 19:54:56,041 (client:354) INFO: {'Role': 'Client #3', 'Round': 22, 'Results_raw': {'train_loss': 14.172567, 'val_loss': 12.787409, 'test_loss': 12.851467}}
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2024-11-13 19:55:54,479 (client:354) INFO: {'Role': 'Client #10', 'Round': 22, 'Results_raw': {'train_loss': 16.110026, 'val_loss': 14.567293, 'test_loss': 14.791688}}
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2024-11-13 19:56:51,288 (client:354) INFO: {'Role': 'Client #1', 'Round': 22, 'Results_raw': {'train_loss': 15.51708, 'val_loss': 14.935491, 'test_loss': 14.351853}}
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2024-11-13 19:57:49,986 (client:354) INFO: {'Role': 'Client #9', 'Round': 22, 'Results_raw': {'train_loss': 14.102866, 'val_loss': 13.552477, 'test_loss': 13.225274}}
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2024-11-13 19:58:48,149 (client:354) INFO: {'Role': 'Client #7', 'Round': 22, 'Results_raw': {'train_loss': 10.509759, 'val_loss': 9.428015, 'test_loss': 9.593779}}
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2024-11-13 19:59:46,146 (client:354) INFO: {'Role': 'Client #5', 'Round': 22, 'Results_raw': {'train_loss': 12.904219, 'val_loss': 11.957855, 'test_loss': 12.664215}}
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2024-11-13 19:59:46,150 (server:615) INFO: {'Role': 'Server #', 'Round': 21, 'Results_weighted_avg': {'test_loss': np.float64(59718.385678), 'test_avg_loss': np.float64(17.605656), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61428.824197), 'val_avg_loss': np.float64(18.109913), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59718.385678), 'test_avg_loss': np.float64(17.605656), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61428.824197), 'val_avg_loss': np.float64(18.109913), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8096.538999), 'test_loss_bottom_decile': np.float64(50551.499023), 'test_loss_top_decile': np.float64(67068.494019), 'test_loss_min': np.float64(39642.283173), 'test_loss_max': np.float64(67068.494019), 'test_loss_bottom10%': np.float64(39642.283173), 'test_loss_top10%': np.float64(67068.494019), 'test_loss_cos1': np.float64(0.990934), 'test_loss_entropy': np.float64(2.292638), 'test_avg_loss_std': np.float64(2.386951), 'test_avg_loss_bottom_decile': np.float64(14.903154), 'test_avg_loss_top_decile': np.float64(19.772551), 'test_avg_loss_min': np.float64(11.686994), 'test_avg_loss_max': np.float64(19.772551), 'test_avg_loss_bottom10%': np.float64(11.686994), 'test_avg_loss_top10%': np.float64(19.772551), 'test_avg_loss_cos1': np.float64(0.990934), 'test_avg_loss_entropy': np.float64(2.292638), 'val_loss_std': np.float64(8213.131409), 'val_loss_bottom_decile': np.float64(52935.479797), 'val_loss_top_decile': np.float64(68859.631714), 'val_loss_min': np.float64(40464.663727), 'val_loss_max': np.float64(68859.631714), 'val_loss_bottom10%': np.float64(40464.663727), 'val_loss_top10%': np.float64(68859.631714), 'val_loss_cos1': np.float64(0.99118), 'val_loss_entropy': np.float64(2.292864), 'val_avg_loss_std': np.float64(2.421324), 'val_avg_loss_bottom_decile': np.float64(15.605979), 'val_avg_loss_top_decile': np.float64(20.300599), 'val_avg_loss_min': np.float64(11.929441), 'val_avg_loss_max': np.float64(20.300599), 'val_avg_loss_bottom10%': np.float64(11.929441), 'val_avg_loss_top10%': np.float64(20.300599), 'val_avg_loss_cos1': np.float64(0.99118), 'val_avg_loss_entropy': np.float64(2.292864)}}
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2024-11-13 19:59:46,188 (server:353) INFO: Server: Starting evaluation at the end of round 22.
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2024-11-13 19:59:46,189 (server:359) INFO: ----------- Starting a new training round (Round #23) -------------
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2024-11-13 20:02:57,732 (client:354) INFO: {'Role': 'Client #5', 'Round': 23, 'Results_raw': {'train_loss': 12.900157, 'val_loss': 12.252882, 'test_loss': 12.950227}}
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2024-11-13 20:03:57,873 (client:354) INFO: {'Role': 'Client #6', 'Round': 23, 'Results_raw': {'train_loss': 8.286932, 'val_loss': 7.446857, 'test_loss': 7.570938}}
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2024-11-13 20:04:56,429 (client:354) INFO: {'Role': 'Client #9', 'Round': 23, 'Results_raw': {'train_loss': 13.977101, 'val_loss': 13.449343, 'test_loss': 13.063938}}
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2024-11-13 20:05:55,741 (client:354) INFO: {'Role': 'Client #1', 'Round': 23, 'Results_raw': {'train_loss': 15.406682, 'val_loss': 14.923711, 'test_loss': 14.355604}}
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2024-11-13 20:06:54,783 (client:354) INFO: {'Role': 'Client #8', 'Round': 23, 'Results_raw': {'train_loss': 15.082723, 'val_loss': 14.010302, 'test_loss': 13.391845}}
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2024-11-13 20:07:54,480 (client:354) INFO: {'Role': 'Client #3', 'Round': 23, 'Results_raw': {'train_loss': 14.187679, 'val_loss': 13.017882, 'test_loss': 13.111745}}
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2024-11-13 20:08:53,583 (client:354) INFO: {'Role': 'Client #4', 'Round': 23, 'Results_raw': {'train_loss': 15.541076, 'val_loss': 13.660587, 'test_loss': 14.118029}}
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2024-11-13 20:09:53,312 (client:354) INFO: {'Role': 'Client #7', 'Round': 23, 'Results_raw': {'train_loss': 10.446093, 'val_loss': 9.295167, 'test_loss': 9.48653}}
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2024-11-13 20:10:52,110 (client:354) INFO: {'Role': 'Client #10', 'Round': 23, 'Results_raw': {'train_loss': 16.003451, 'val_loss': 14.583306, 'test_loss': 14.870991}}
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2024-11-13 20:11:52,313 (client:354) INFO: {'Role': 'Client #2', 'Round': 23, 'Results_raw': {'train_loss': 14.737035, 'val_loss': 13.675797, 'test_loss': 14.057506}}
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2024-11-13 20:11:52,319 (server:615) INFO: {'Role': 'Server #', 'Round': 22, 'Results_weighted_avg': {'test_loss': np.float64(60182.545584), 'test_avg_loss': np.float64(17.742496), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62004.700699), 'val_avg_loss': np.float64(18.279688), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60182.545584), 'test_avg_loss': np.float64(17.742496), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62004.700699), 'val_avg_loss': np.float64(18.279688), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8215.032242), 'test_loss_bottom_decile': np.float64(51439.543884), 'test_loss_top_decile': np.float64(67932.012329), 'test_loss_min': np.float64(39510.798187), 'test_loss_max': np.float64(67932.012329), 'test_loss_bottom10%': np.float64(39510.798187), 'test_loss_top10%': np.float64(67932.012329), 'test_loss_cos1': np.float64(0.990812), 'test_loss_entropy': np.float64(2.292468), 'test_avg_loss_std': np.float64(2.421885), 'test_avg_loss_bottom_decile': np.float64(15.16496), 'test_avg_loss_top_decile': np.float64(20.027126), 'test_avg_loss_min': np.float64(11.648231), 'test_avg_loss_max': np.float64(20.027126), 'test_avg_loss_bottom10%': np.float64(11.648231), 'test_avg_loss_top10%': np.float64(20.027126), 'test_avg_loss_cos1': np.float64(0.990812), 'test_avg_loss_entropy': np.float64(2.292468), 'val_loss_std': np.float64(8346.576058), 'val_loss_bottom_decile': np.float64(53908.711975), 'val_loss_top_decile': np.float64(69432.361755), 'val_loss_min': np.float64(40419.645966), 'val_loss_max': np.float64(69432.361755), 'val_loss_bottom10%': np.float64(40419.645966), 'val_loss_top10%': np.float64(69432.361755), 'val_loss_cos1': np.float64(0.991061), 'val_loss_entropy': np.float64(2.292696), 'val_avg_loss_std': np.float64(2.460665), 'val_avg_loss_bottom_decile': np.float64(15.892899), 'val_avg_loss_top_decile': np.float64(20.469446), 'val_avg_loss_min': np.float64(11.916169), 'val_avg_loss_max': np.float64(20.469446), 'val_avg_loss_bottom10%': np.float64(11.916169), 'val_avg_loss_top10%': np.float64(20.469446), 'val_avg_loss_cos1': np.float64(0.991061), 'val_avg_loss_entropy': np.float64(2.292696)}}
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2024-11-13 20:11:52,363 (server:353) INFO: Server: Starting evaluation at the end of round 23.
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2024-11-13 20:11:52,364 (server:359) INFO: ----------- Starting a new training round (Round #24) -------------
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2024-11-13 20:14:14,347 (client:354) INFO: {'Role': 'Client #4', 'Round': 24, 'Results_raw': {'train_loss': 15.50313, 'val_loss': 13.942802, 'test_loss': 14.370971}}
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2024-11-13 20:14:57,724 (client:354) INFO: {'Role': 'Client #2', 'Round': 24, 'Results_raw': {'train_loss': 14.69183, 'val_loss': 13.566963, 'test_loss': 13.884405}}
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2024-11-13 20:15:42,214 (client:354) INFO: {'Role': 'Client #10', 'Round': 24, 'Results_raw': {'train_loss': 15.973143, 'val_loss': 14.619928, 'test_loss': 14.904395}}
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2024-11-13 20:16:27,650 (client:354) INFO: {'Role': 'Client #6', 'Round': 24, 'Results_raw': {'train_loss': 8.282384, 'val_loss': 7.544152, 'test_loss': 7.668402}}
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2024-11-13 20:17:12,793 (client:354) INFO: {'Role': 'Client #9', 'Round': 24, 'Results_raw': {'train_loss': 14.035728, 'val_loss': 13.504291, 'test_loss': 13.173408}}
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2024-11-13 20:18:01,871 (client:354) INFO: {'Role': 'Client #8', 'Round': 24, 'Results_raw': {'train_loss': 15.066673, 'val_loss': 14.065183, 'test_loss': 13.500775}}
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2024-11-13 20:18:48,998 (client:354) INFO: {'Role': 'Client #3', 'Round': 24, 'Results_raw': {'train_loss': 14.135139, 'val_loss': 12.765705, 'test_loss': 12.844067}}
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2024-11-13 20:19:32,079 (client:354) INFO: {'Role': 'Client #7', 'Round': 24, 'Results_raw': {'train_loss': 10.453436, 'val_loss': 9.274852, 'test_loss': 9.504955}}
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2024-11-13 20:20:04,239 (client:354) INFO: {'Role': 'Client #1', 'Round': 24, 'Results_raw': {'train_loss': 15.436852, 'val_loss': 14.838441, 'test_loss': 14.33347}}
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2024-11-13 20:20:36,018 (client:354) INFO: {'Role': 'Client #5', 'Round': 24, 'Results_raw': {'train_loss': 12.88528, 'val_loss': 12.020509, 'test_loss': 12.646268}}
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2024-11-13 20:20:36,021 (server:615) INFO: {'Role': 'Server #', 'Round': 23, 'Results_weighted_avg': {'test_loss': np.float64(60390.032495), 'test_avg_loss': np.float64(17.803665), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62208.668027), 'val_avg_loss': np.float64(18.33982), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60390.032495), 'test_avg_loss': np.float64(17.803665), 'test_total': np.float64(3392.0), 'val_loss': np.float64(62208.668027), 'val_avg_loss': np.float64(18.33982), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8309.494775), 'test_loss_bottom_decile': np.float64(51322.427551), 'test_loss_top_decile': np.float64(68074.43219), 'test_loss_min': np.float64(39595.917175), 'test_loss_max': np.float64(68074.43219), 'test_loss_bottom10%': np.float64(39595.917175), 'test_loss_top10%': np.float64(68074.43219), 'test_loss_cos1': np.float64(0.990666), 'test_loss_entropy': np.float64(2.292306), 'test_avg_loss_std': np.float64(2.449733), 'test_avg_loss_bottom_decile': np.float64(15.130433), 'test_avg_loss_top_decile': np.float64(20.069113), 'test_avg_loss_min': np.float64(11.673325), 'test_avg_loss_max': np.float64(20.069113), 'test_avg_loss_bottom10%': np.float64(11.673325), 'test_avg_loss_top10%': np.float64(20.069113), 'test_avg_loss_cos1': np.float64(0.990666), 'test_avg_loss_entropy': np.float64(2.292306), 'val_loss_std': np.float64(8456.591166), 'val_loss_bottom_decile': np.float64(53865.334167), 'val_loss_top_decile': np.float64(69797.40979), 'val_loss_min': np.float64(40440.196198), 'val_loss_max': np.float64(69797.40979), 'val_loss_bottom10%': np.float64(40440.196198), 'val_loss_top10%': np.float64(69797.40979), 'val_loss_cos1': np.float64(0.990886), 'val_loss_entropy': np.float64(2.292503), 'val_avg_loss_std': np.float64(2.493099), 'val_avg_loss_bottom_decile': np.float64(15.88011), 'val_avg_loss_top_decile': np.float64(20.577067), 'val_avg_loss_min': np.float64(11.922228), 'val_avg_loss_max': np.float64(20.577067), 'val_avg_loss_bottom10%': np.float64(11.922228), 'val_avg_loss_top10%': np.float64(20.577067), 'val_avg_loss_cos1': np.float64(0.990886), 'val_avg_loss_entropy': np.float64(2.292503)}}
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2024-11-13 20:20:36,056 (server:353) INFO: Server: Starting evaluation at the end of round 24.
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2024-11-13 20:20:36,057 (server:359) INFO: ----------- Starting a new training round (Round #25) -------------
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2024-11-13 20:22:03,809 (client:354) INFO: {'Role': 'Client #5', 'Round': 25, 'Results_raw': {'train_loss': 12.785605, 'val_loss': 11.897638, 'test_loss': 12.623371}}
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2024-11-13 20:22:35,782 (client:354) INFO: {'Role': 'Client #2', 'Round': 25, 'Results_raw': {'train_loss': 14.728569, 'val_loss': 13.359662, 'test_loss': 13.687806}}
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2024-11-13 20:23:07,699 (client:354) INFO: {'Role': 'Client #4', 'Round': 25, 'Results_raw': {'train_loss': 15.467938, 'val_loss': 14.315194, 'test_loss': 14.731738}}
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2024-11-13 20:23:39,768 (client:354) INFO: {'Role': 'Client #8', 'Round': 25, 'Results_raw': {'train_loss': 14.993143, 'val_loss': 13.736174, 'test_loss': 13.143453}}
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2024-11-13 20:24:11,854 (client:354) INFO: {'Role': 'Client #10', 'Round': 25, 'Results_raw': {'train_loss': 15.965238, 'val_loss': 14.43404, 'test_loss': 14.749406}}
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2024-11-13 20:24:48,880 (client:354) INFO: {'Role': 'Client #3', 'Round': 25, 'Results_raw': {'train_loss': 14.102016, 'val_loss': 12.852897, 'test_loss': 12.972077}}
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2024-11-13 20:25:21,896 (client:354) INFO: {'Role': 'Client #9', 'Round': 25, 'Results_raw': {'train_loss': 13.911786, 'val_loss': 13.47881, 'test_loss': 13.072026}}
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2024-11-13 20:25:54,755 (client:354) INFO: {'Role': 'Client #1', 'Round': 25, 'Results_raw': {'train_loss': 15.278498, 'val_loss': 14.85346, 'test_loss': 14.290347}}
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2024-11-13 20:26:28,250 (client:354) INFO: {'Role': 'Client #6', 'Round': 25, 'Results_raw': {'train_loss': 8.258911, 'val_loss': 7.620679, 'test_loss': 7.763945}}
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2024-11-13 20:27:04,889 (client:354) INFO: {'Role': 'Client #7', 'Round': 25, 'Results_raw': {'train_loss': 10.455429, 'val_loss': 8.988994, 'test_loss': 9.25636}}
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2024-11-13 20:27:04,893 (server:615) INFO: {'Role': 'Server #', 'Round': 24, 'Results_weighted_avg': {'test_loss': np.float64(59823.642081), 'test_avg_loss': np.float64(17.636687), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61622.156287), 'val_avg_loss': np.float64(18.166909), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59823.642081), 'test_avg_loss': np.float64(17.636687), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61622.156287), 'val_avg_loss': np.float64(18.166909), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8237.998541), 'test_loss_bottom_decile': np.float64(50684.650635), 'test_loss_top_decile': np.float64(67117.120178), 'test_loss_min': np.float64(39212.0784), 'test_loss_max': np.float64(67117.120178), 'test_loss_bottom10%': np.float64(39212.0784), 'test_loss_top10%': np.float64(67117.120178), 'test_loss_cos1': np.float64(0.990651), 'test_loss_entropy': np.float64(2.292284), 'test_avg_loss_std': np.float64(2.428655), 'test_avg_loss_bottom_decile': np.float64(14.942409), 'test_avg_loss_top_decile': np.float64(19.786887), 'test_avg_loss_min': np.float64(11.560165), 'test_avg_loss_max': np.float64(19.786887), 'test_avg_loss_bottom10%': np.float64(11.560165), 'test_avg_loss_top10%': np.float64(19.786887), 'test_avg_loss_cos1': np.float64(0.990651), 'test_avg_loss_entropy': np.float64(2.292284), 'val_loss_std': np.float64(8389.824227), 'val_loss_bottom_decile': np.float64(53063.579285), 'val_loss_top_decile': np.float64(69074.19104), 'val_loss_min': np.float64(40069.758362), 'val_loss_max': np.float64(69074.19104), 'val_loss_bottom10%': np.float64(40069.758362), 'val_loss_top10%': np.float64(69074.19104), 'val_loss_cos1': np.float64(0.990859), 'val_loss_entropy': np.float64(2.292468), 'val_avg_loss_std': np.float64(2.473415), 'val_avg_loss_bottom_decile': np.float64(15.643744), 'val_avg_loss_top_decile': np.float64(20.363853), 'val_avg_loss_min': np.float64(11.813018), 'val_avg_loss_max': np.float64(20.363853), 'val_avg_loss_bottom10%': np.float64(11.813018), 'val_avg_loss_top10%': np.float64(20.363853), 'val_avg_loss_cos1': np.float64(0.990859), 'val_avg_loss_entropy': np.float64(2.292468)}}
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2024-11-13 20:27:04,927 (server:353) INFO: Server: Starting evaluation at the end of round 25.
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2024-11-13 20:27:04,928 (server:359) INFO: ----------- Starting a new training round (Round #26) -------------
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2024-11-13 20:28:39,917 (client:354) INFO: {'Role': 'Client #8', 'Round': 26, 'Results_raw': {'train_loss': 14.945519, 'val_loss': 14.083734, 'test_loss': 13.546645}}
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2024-11-13 20:29:15,174 (client:354) INFO: {'Role': 'Client #9', 'Round': 26, 'Results_raw': {'train_loss': 13.926322, 'val_loss': 13.324506, 'test_loss': 12.958725}}
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2024-11-13 20:29:48,517 (client:354) INFO: {'Role': 'Client #7', 'Round': 26, 'Results_raw': {'train_loss': 10.358946, 'val_loss': 9.142457, 'test_loss': 9.46184}}
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2024-11-13 20:30:20,393 (client:354) INFO: {'Role': 'Client #10', 'Round': 26, 'Results_raw': {'train_loss': 15.979052, 'val_loss': 14.415056, 'test_loss': 14.683981}}
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2024-11-13 20:30:51,491 (client:354) INFO: {'Role': 'Client #2', 'Round': 26, 'Results_raw': {'train_loss': 14.640109, 'val_loss': 13.394633, 'test_loss': 13.773786}}
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2024-11-13 20:31:23,448 (client:354) INFO: {'Role': 'Client #5', 'Round': 26, 'Results_raw': {'train_loss': 12.765633, 'val_loss': 11.973565, 'test_loss': 12.597832}}
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2024-11-13 20:31:55,907 (client:354) INFO: {'Role': 'Client #1', 'Round': 26, 'Results_raw': {'train_loss': 15.359035, 'val_loss': 14.755012, 'test_loss': 14.266636}}
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2024-11-13 20:32:27,808 (client:354) INFO: {'Role': 'Client #6', 'Round': 26, 'Results_raw': {'train_loss': 8.258507, 'val_loss': 7.551717, 'test_loss': 7.678001}}
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2024-11-13 20:32:59,170 (client:354) INFO: {'Role': 'Client #4', 'Round': 26, 'Results_raw': {'train_loss': 15.399665, 'val_loss': 13.713021, 'test_loss': 14.143415}}
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2024-11-13 20:33:30,245 (client:354) INFO: {'Role': 'Client #3', 'Round': 26, 'Results_raw': {'train_loss': 13.918028, 'val_loss': 12.649367, 'test_loss': 12.821185}}
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2024-11-13 20:33:30,248 (server:615) INFO: {'Role': 'Server #', 'Round': 25, 'Results_weighted_avg': {'test_loss': np.float64(59571.403024), 'test_avg_loss': np.float64(17.562324), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61349.191922), 'val_avg_loss': np.float64(18.086436), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59571.403024), 'test_avg_loss': np.float64(17.562324), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61349.191922), 'val_avg_loss': np.float64(18.086436), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8092.500602), 'test_loss_bottom_decile': np.float64(50678.932373), 'test_loss_top_decile': np.float64(66996.548157), 'test_loss_min': np.float64(39333.62735), 'test_loss_max': np.float64(66996.548157), 'test_loss_bottom10%': np.float64(39333.62735), 'test_loss_top10%': np.float64(66996.548157), 'test_loss_cos1': np.float64(0.990899), 'test_loss_entropy': np.float64(2.292579), 'test_avg_loss_std': np.float64(2.385761), 'test_avg_loss_bottom_decile': np.float64(14.940723), 'test_avg_loss_top_decile': np.float64(19.751341), 'test_avg_loss_min': np.float64(11.595999), 'test_avg_loss_max': np.float64(19.751341), 'test_avg_loss_bottom10%': np.float64(11.595999), 'test_avg_loss_top10%': np.float64(19.751341), 'test_avg_loss_cos1': np.float64(0.990899), 'test_avg_loss_entropy': np.float64(2.292579), 'val_loss_std': np.float64(8220.330753), 'val_loss_bottom_decile': np.float64(53034.109497), 'val_loss_top_decile': np.float64(68761.620544), 'val_loss_min': np.float64(40179.082001), 'val_loss_max': np.float64(68761.620544), 'val_loss_bottom10%': np.float64(40179.082001), 'val_loss_top10%': np.float64(68761.620544), 'val_loss_cos1': np.float64(0.991142), 'val_loss_entropy': np.float64(2.292795), 'val_avg_loss_std': np.float64(2.423447), 'val_avg_loss_bottom_decile': np.float64(15.635056), 'val_avg_loss_top_decile': np.float64(20.271704), 'val_avg_loss_min': np.float64(11.845248), 'val_avg_loss_max': np.float64(20.271704), 'val_avg_loss_bottom10%': np.float64(11.845248), 'val_avg_loss_top10%': np.float64(20.271704), 'val_avg_loss_cos1': np.float64(0.991142), 'val_avg_loss_entropy': np.float64(2.292795)}}
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2024-11-13 20:33:30,278 (server:353) INFO: Server: Starting evaluation at the end of round 26.
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2024-11-13 20:33:30,278 (server:359) INFO: ----------- Starting a new training round (Round #27) -------------
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2024-11-13 20:34:56,479 (client:354) INFO: {'Role': 'Client #9', 'Round': 27, 'Results_raw': {'train_loss': 13.927623, 'val_loss': 13.566043, 'test_loss': 13.137176}}
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2024-11-13 20:35:27,560 (client:354) INFO: {'Role': 'Client #8', 'Round': 27, 'Results_raw': {'train_loss': 14.887812, 'val_loss': 13.91774, 'test_loss': 13.320327}}
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2024-11-13 20:35:58,536 (client:354) INFO: {'Role': 'Client #4', 'Round': 27, 'Results_raw': {'train_loss': 15.363731, 'val_loss': 13.661523, 'test_loss': 14.125389}}
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2024-11-13 20:36:29,926 (client:354) INFO: {'Role': 'Client #1', 'Round': 27, 'Results_raw': {'train_loss': 15.142263, 'val_loss': 14.937497, 'test_loss': 14.432416}}
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2024-11-13 20:37:01,100 (client:354) INFO: {'Role': 'Client #2', 'Round': 27, 'Results_raw': {'train_loss': 14.636698, 'val_loss': 13.495918, 'test_loss': 13.834182}}
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2024-11-13 20:37:32,232 (client:354) INFO: {'Role': 'Client #3', 'Round': 27, 'Results_raw': {'train_loss': 14.012025, 'val_loss': 12.691784, 'test_loss': 12.833866}}
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2024-11-13 20:38:03,282 (client:354) INFO: {'Role': 'Client #7', 'Round': 27, 'Results_raw': {'train_loss': 10.303892, 'val_loss': 9.164113, 'test_loss': 9.508056}}
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2024-11-13 20:38:34,157 (client:354) INFO: {'Role': 'Client #10', 'Round': 27, 'Results_raw': {'train_loss': 15.838565, 'val_loss': 14.470154, 'test_loss': 14.78789}}
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2024-11-13 20:39:04,826 (client:354) INFO: {'Role': 'Client #6', 'Round': 27, 'Results_raw': {'train_loss': 8.20313, 'val_loss': 7.431422, 'test_loss': 7.583276}}
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2024-11-13 20:39:35,614 (client:354) INFO: {'Role': 'Client #5', 'Round': 27, 'Results_raw': {'train_loss': 12.722819, 'val_loss': 12.013768, 'test_loss': 12.724482}}
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2024-11-13 20:39:35,616 (server:615) INFO: {'Role': 'Server #', 'Round': 26, 'Results_weighted_avg': {'test_loss': np.float64(60039.686243), 'test_avg_loss': np.float64(17.700379), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61845.203088), 'val_avg_loss': np.float64(18.232666), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60039.686243), 'test_avg_loss': np.float64(17.700379), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61845.203088), 'val_avg_loss': np.float64(18.232666), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8233.240184), 'test_loss_bottom_decile': np.float64(51482.793396), 'test_loss_top_decile': np.float64(67666.365173), 'test_loss_min': np.float64(39179.079041), 'test_loss_max': np.float64(67666.365173), 'test_loss_bottom10%': np.float64(39179.079041), 'test_loss_top10%': np.float64(67666.365173), 'test_loss_cos1': np.float64(0.990728), 'test_loss_entropy': np.float64(2.292351), 'test_avg_loss_std': np.float64(2.427252), 'test_avg_loss_bottom_decile': np.float64(15.17771), 'test_avg_loss_top_decile': np.float64(19.94881), 'test_avg_loss_min': np.float64(11.550436), 'test_avg_loss_max': np.float64(19.94881), 'test_avg_loss_bottom10%': np.float64(11.550436), 'test_avg_loss_top10%': np.float64(19.94881), 'test_avg_loss_cos1': np.float64(0.990728), 'test_avg_loss_entropy': np.float64(2.292351), 'val_loss_std': np.float64(8370.472402), 'val_loss_bottom_decile': np.float64(53948.859802), 'val_loss_top_decile': np.float64(69384.967285), 'val_loss_min': np.float64(40049.389465), 'val_loss_max': np.float64(69384.967285), 'val_loss_bottom10%': np.float64(40049.389465), 'val_loss_top10%': np.float64(69384.967285), 'val_loss_cos1': np.float64(0.990965), 'val_loss_entropy': np.float64(2.292564), 'val_avg_loss_std': np.float64(2.46771), 'val_avg_loss_bottom_decile': np.float64(15.904735), 'val_avg_loss_top_decile': np.float64(20.455474), 'val_avg_loss_min': np.float64(11.807013), 'val_avg_loss_max': np.float64(20.455474), 'val_avg_loss_bottom10%': np.float64(11.807013), 'val_avg_loss_top10%': np.float64(20.455474), 'val_avg_loss_cos1': np.float64(0.990965), 'val_avg_loss_entropy': np.float64(2.292564)}}
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2024-11-13 20:39:35,643 (server:353) INFO: Server: Starting evaluation at the end of round 27.
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2024-11-13 20:39:35,644 (server:359) INFO: ----------- Starting a new training round (Round #28) -------------
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2024-11-13 20:41:02,379 (client:354) INFO: {'Role': 'Client #8', 'Round': 28, 'Results_raw': {'train_loss': 14.858509, 'val_loss': 13.870521, 'test_loss': 13.321651}}
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2024-11-13 20:41:33,410 (client:354) INFO: {'Role': 'Client #9', 'Round': 28, 'Results_raw': {'train_loss': 13.856339, 'val_loss': 13.411007, 'test_loss': 13.069972}}
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2024-11-13 20:42:04,936 (client:354) INFO: {'Role': 'Client #3', 'Round': 28, 'Results_raw': {'train_loss': 13.94008, 'val_loss': 12.825368, 'test_loss': 12.990371}}
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2024-11-13 20:42:38,017 (client:354) INFO: {'Role': 'Client #4', 'Round': 28, 'Results_raw': {'train_loss': 15.293152, 'val_loss': 13.727866, 'test_loss': 14.260563}}
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2024-11-13 20:43:11,078 (client:354) INFO: {'Role': 'Client #6', 'Round': 28, 'Results_raw': {'train_loss': 8.114173, 'val_loss': 7.279436, 'test_loss': 7.413441}}
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2024-11-13 20:43:43,987 (client:354) INFO: {'Role': 'Client #1', 'Round': 28, 'Results_raw': {'train_loss': 15.161677, 'val_loss': 15.280982, 'test_loss': 14.581876}}
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2024-11-13 20:44:16,423 (client:354) INFO: {'Role': 'Client #2', 'Round': 28, 'Results_raw': {'train_loss': 14.516369, 'val_loss': 13.480601, 'test_loss': 13.818442}}
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2024-11-13 20:44:48,770 (client:354) INFO: {'Role': 'Client #10', 'Round': 28, 'Results_raw': {'train_loss': 15.835523, 'val_loss': 14.673837, 'test_loss': 14.891847}}
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2024-11-13 20:45:20,817 (client:354) INFO: {'Role': 'Client #7', 'Round': 28, 'Results_raw': {'train_loss': 10.245846, 'val_loss': 9.49629, 'test_loss': 9.880576}}
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2024-11-13 20:45:53,332 (client:354) INFO: {'Role': 'Client #5', 'Round': 28, 'Results_raw': {'train_loss': 12.669433, 'val_loss': 12.07932, 'test_loss': 12.778154}}
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2024-11-13 20:45:53,334 (server:615) INFO: {'Role': 'Server #', 'Round': 27, 'Results_weighted_avg': {'test_loss': np.float64(59757.849701), 'test_avg_loss': np.float64(17.617291), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61521.083276), 'val_avg_loss': np.float64(18.137112), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59757.849701), 'test_avg_loss': np.float64(17.617291), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61521.083276), 'val_avg_loss': np.float64(18.137112), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8098.218752), 'test_loss_bottom_decile': np.float64(50418.982056), 'test_loss_top_decile': np.float64(66945.384705), 'test_loss_min': np.float64(39565.028015), 'test_loss_max': np.float64(66945.384705), 'test_loss_bottom10%': np.float64(39565.028015), 'test_loss_top10%': np.float64(66945.384705), 'test_loss_cos1': np.float64(0.990942), 'test_loss_entropy': np.float64(2.292622), 'test_avg_loss_std': np.float64(2.387447), 'test_avg_loss_bottom_decile': np.float64(14.864087), 'test_avg_loss_top_decile': np.float64(19.736257), 'test_avg_loss_min': np.float64(11.664218), 'test_avg_loss_max': np.float64(19.736257), 'test_avg_loss_bottom10%': np.float64(11.664218), 'test_avg_loss_top10%': np.float64(19.736257), 'test_avg_loss_cos1': np.float64(0.990942), 'test_avg_loss_entropy': np.float64(2.292622), 'val_loss_std': np.float64(8255.760689), 'val_loss_bottom_decile': np.float64(52720.547058), 'val_loss_top_decile': np.float64(68886.067444), 'val_loss_min': np.float64(40417.250244), 'val_loss_max': np.float64(68886.067444), 'val_loss_bottom10%': np.float64(40417.250244), 'val_loss_top10%': np.float64(68886.067444), 'val_loss_cos1': np.float64(0.991116), 'val_loss_entropy': np.float64(2.292775), 'val_avg_loss_std': np.float64(2.433892), 'val_avg_loss_bottom_decile': np.float64(15.542614), 'val_avg_loss_top_decile': np.float64(20.308393), 'val_avg_loss_min': np.float64(11.915463), 'val_avg_loss_max': np.float64(20.308393), 'val_avg_loss_bottom10%': np.float64(11.915463), 'val_avg_loss_top10%': np.float64(20.308393), 'val_avg_loss_cos1': np.float64(0.991116), 'val_avg_loss_entropy': np.float64(2.292775)}}
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2024-11-13 20:45:53,365 (server:353) INFO: Server: Starting evaluation at the end of round 28.
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2024-11-13 20:45:53,366 (server:359) INFO: ----------- Starting a new training round (Round #29) -------------
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2024-11-13 20:47:21,892 (client:354) INFO: {'Role': 'Client #1', 'Round': 29, 'Results_raw': {'train_loss': 15.20532, 'val_loss': 15.12194, 'test_loss': 14.43881}}
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2024-11-13 20:47:54,428 (client:354) INFO: {'Role': 'Client #5', 'Round': 29, 'Results_raw': {'train_loss': 12.634669, 'val_loss': 12.021707, 'test_loss': 12.570862}}
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2024-11-13 20:48:27,140 (client:354) INFO: {'Role': 'Client #7', 'Round': 29, 'Results_raw': {'train_loss': 10.296681, 'val_loss': 9.0515, 'test_loss': 9.295545}}
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2024-11-13 20:49:00,199 (client:354) INFO: {'Role': 'Client #6', 'Round': 29, 'Results_raw': {'train_loss': 8.105236, 'val_loss': 7.369004, 'test_loss': 7.511595}}
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2024-11-13 20:49:32,662 (client:354) INFO: {'Role': 'Client #3', 'Round': 29, 'Results_raw': {'train_loss': 13.89105, 'val_loss': 12.629409, 'test_loss': 12.787324}}
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2024-11-13 20:50:05,332 (client:354) INFO: {'Role': 'Client #4', 'Round': 29, 'Results_raw': {'train_loss': 15.346002, 'val_loss': 13.725404, 'test_loss': 14.186189}}
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2024-11-13 20:50:38,048 (client:354) INFO: {'Role': 'Client #9', 'Round': 29, 'Results_raw': {'train_loss': 13.805222, 'val_loss': 13.291991, 'test_loss': 12.930052}}
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2024-11-13 20:51:10,774 (client:354) INFO: {'Role': 'Client #10', 'Round': 29, 'Results_raw': {'train_loss': 15.745553, 'val_loss': 14.473858, 'test_loss': 14.77563}}
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2024-11-13 20:51:43,633 (client:354) INFO: {'Role': 'Client #2', 'Round': 29, 'Results_raw': {'train_loss': 14.566556, 'val_loss': 13.387791, 'test_loss': 13.743087}}
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2024-11-13 20:52:18,154 (client:354) INFO: {'Role': 'Client #8', 'Round': 29, 'Results_raw': {'train_loss': 14.881312, 'val_loss': 13.755223, 'test_loss': 13.179425}}
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2024-11-13 20:52:18,156 (server:615) INFO: {'Role': 'Server #', 'Round': 28, 'Results_weighted_avg': {'test_loss': np.float64(59983.840259), 'test_avg_loss': np.float64(17.683915), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61824.152707), 'val_avg_loss': np.float64(18.22646), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59983.840259), 'test_avg_loss': np.float64(17.683915), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61824.152707), 'val_avg_loss': np.float64(18.22646), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8252.725945), 'test_loss_bottom_decile': np.float64(50879.19928), 'test_loss_top_decile': np.float64(67529.546326), 'test_loss_min': np.float64(39194.439026), 'test_loss_max': np.float64(67529.546326), 'test_loss_bottom10%': np.float64(39194.439026), 'test_loss_top10%': np.float64(67529.546326), 'test_loss_cos1': np.float64(0.990668), 'test_loss_entropy': np.float64(2.292282), 'test_avg_loss_std': np.float64(2.432997), 'test_avg_loss_bottom_decile': np.float64(14.999764), 'test_avg_loss_top_decile': np.float64(19.908475), 'test_avg_loss_min': np.float64(11.554964), 'test_avg_loss_max': np.float64(19.908475), 'test_avg_loss_bottom10%': np.float64(11.554964), 'test_avg_loss_top10%': np.float64(19.908475), 'test_avg_loss_cos1': np.float64(0.990668), 'test_avg_loss_entropy': np.float64(2.292282), 'val_loss_std': np.float64(8463.005596), 'val_loss_bottom_decile': np.float64(53237.918457), 'val_loss_top_decile': np.float64(69605.960327), 'val_loss_min': np.float64(40061.857758), 'val_loss_max': np.float64(69605.960327), 'val_loss_bottom10%': np.float64(40061.857758), 'val_loss_top10%': np.float64(69605.960327), 'val_loss_cos1': np.float64(0.99076), 'val_loss_entropy': np.float64(2.292351), 'val_avg_loss_std': np.float64(2.49499), 'val_avg_loss_bottom_decile': np.float64(15.695141), 'val_avg_loss_top_decile': np.float64(20.520625), 'val_avg_loss_min': np.float64(11.810689), 'val_avg_loss_max': np.float64(20.520625), 'val_avg_loss_bottom10%': np.float64(11.810689), 'val_avg_loss_top10%': np.float64(20.520625), 'val_avg_loss_cos1': np.float64(0.99076), 'val_avg_loss_entropy': np.float64(2.292351)}}
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2024-11-13 20:52:18,185 (server:353) INFO: Server: Starting evaluation at the end of round 29.
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2024-11-13 20:52:18,186 (server:359) INFO: ----------- Starting a new training round (Round #30) -------------
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2024-11-13 20:53:46,838 (client:354) INFO: {'Role': 'Client #7', 'Round': 30, 'Results_raw': {'train_loss': 10.114541, 'val_loss': 8.90591, 'test_loss': 9.165456}}
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2024-11-13 20:54:18,405 (client:354) INFO: {'Role': 'Client #6', 'Round': 30, 'Results_raw': {'train_loss': 8.119681, 'val_loss': 7.304958, 'test_loss': 7.43954}}
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2024-11-13 20:54:49,197 (client:354) INFO: {'Role': 'Client #10', 'Round': 30, 'Results_raw': {'train_loss': 15.771788, 'val_loss': 14.894538, 'test_loss': 15.2355}}
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2024-11-13 20:55:20,128 (client:354) INFO: {'Role': 'Client #8', 'Round': 30, 'Results_raw': {'train_loss': 14.844866, 'val_loss': 13.866715, 'test_loss': 13.294417}}
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2024-11-13 20:55:50,895 (client:354) INFO: {'Role': 'Client #3', 'Round': 30, 'Results_raw': {'train_loss': 13.792677, 'val_loss': 12.61142, 'test_loss': 12.786488}}
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2024-11-13 20:56:23,020 (client:354) INFO: {'Role': 'Client #2', 'Round': 30, 'Results_raw': {'train_loss': 14.518616, 'val_loss': 13.227707, 'test_loss': 13.602731}}
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2024-11-13 20:56:54,488 (client:354) INFO: {'Role': 'Client #5', 'Round': 30, 'Results_raw': {'train_loss': 12.570506, 'val_loss': 12.030359, 'test_loss': 12.790353}}
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2024-11-13 20:57:26,455 (client:354) INFO: {'Role': 'Client #4', 'Round': 30, 'Results_raw': {'train_loss': 15.336349, 'val_loss': 13.527671, 'test_loss': 13.964017}}
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2024-11-13 20:57:57,299 (client:354) INFO: {'Role': 'Client #9', 'Round': 30, 'Results_raw': {'train_loss': 13.821193, 'val_loss': 13.422093, 'test_loss': 13.103706}}
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2024-11-13 20:58:28,060 (client:354) INFO: {'Role': 'Client #1', 'Round': 30, 'Results_raw': {'train_loss': 14.997879, 'val_loss': 14.980169, 'test_loss': 14.42123}}
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2024-11-13 20:58:28,063 (server:615) INFO: {'Role': 'Server #', 'Round': 29, 'Results_weighted_avg': {'test_loss': np.float64(60074.580252), 'test_avg_loss': np.float64(17.710666), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61952.746616), 'val_avg_loss': np.float64(18.264371), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60074.580252), 'test_avg_loss': np.float64(17.710666), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61952.746616), 'val_avg_loss': np.float64(18.264371), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8406.005103), 'test_loss_bottom_decile': np.float64(51331.768616), 'test_loss_top_decile': np.float64(67893.838745), 'test_loss_min': np.float64(38620.548492), 'test_loss_max': np.float64(67893.838745), 'test_loss_bottom10%': np.float64(38620.548492), 'test_loss_top10%': np.float64(67893.838745), 'test_loss_cos1': np.float64(0.990352), 'test_loss_entropy': np.float64(2.291881), 'test_avg_loss_std': np.float64(2.478185), 'test_avg_loss_bottom_decile': np.float64(15.133187), 'test_avg_loss_top_decile': np.float64(20.015872), 'test_avg_loss_min': np.float64(11.385775), 'test_avg_loss_max': np.float64(20.015872), 'test_avg_loss_bottom10%': np.float64(11.385775), 'test_avg_loss_top10%': np.float64(20.015872), 'test_avg_loss_cos1': np.float64(0.990352), 'test_avg_loss_entropy': np.float64(2.291881), 'val_loss_std': np.float64(8635.546337), 'val_loss_bottom_decile': np.float64(53837.147522), 'val_loss_top_decile': np.float64(69974.715942), 'val_loss_min': np.float64(39464.791168), 'val_loss_max': np.float64(69974.715942), 'val_loss_bottom10%': np.float64(39464.791168), 'val_loss_top10%': np.float64(69974.715942), 'val_loss_cos1': np.float64(0.990425), 'val_loss_entropy': np.float64(2.291924), 'val_avg_loss_std': np.float64(2.545857), 'val_avg_loss_bottom_decile': np.float64(15.871801), 'val_avg_loss_top_decile': np.float64(20.629338), 'val_avg_loss_min': np.float64(11.634667), 'val_avg_loss_max': np.float64(20.629338), 'val_avg_loss_bottom10%': np.float64(11.634667), 'val_avg_loss_top10%': np.float64(20.629338), 'val_avg_loss_cos1': np.float64(0.990425), 'val_avg_loss_entropy': np.float64(2.291924)}}
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2024-11-13 20:58:28,091 (server:353) INFO: Server: Starting evaluation at the end of round 30.
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2024-11-13 20:58:28,091 (server:359) INFO: ----------- Starting a new training round (Round #31) -------------
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2024-11-13 20:59:54,821 (client:354) INFO: {'Role': 'Client #4', 'Round': 31, 'Results_raw': {'train_loss': 15.259545, 'val_loss': 13.743957, 'test_loss': 14.21452}}
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2024-11-13 21:00:26,739 (client:354) INFO: {'Role': 'Client #2', 'Round': 31, 'Results_raw': {'train_loss': 14.407489, 'val_loss': 13.416577, 'test_loss': 13.788219}}
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2024-11-13 21:00:57,946 (client:354) INFO: {'Role': 'Client #5', 'Round': 31, 'Results_raw': {'train_loss': 12.596724, 'val_loss': 11.845922, 'test_loss': 12.568526}}
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2024-11-13 21:01:29,579 (client:354) INFO: {'Role': 'Client #10', 'Round': 31, 'Results_raw': {'train_loss': 15.72977, 'val_loss': 14.404508, 'test_loss': 14.703155}}
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2024-11-13 21:02:01,171 (client:354) INFO: {'Role': 'Client #3', 'Round': 31, 'Results_raw': {'train_loss': 13.813785, 'val_loss': 12.866432, 'test_loss': 12.887422}}
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2024-11-13 21:02:33,363 (client:354) INFO: {'Role': 'Client #9', 'Round': 31, 'Results_raw': {'train_loss': 13.767786, 'val_loss': 13.350724, 'test_loss': 12.98792}}
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2024-11-13 21:03:07,617 (client:354) INFO: {'Role': 'Client #1', 'Round': 31, 'Results_raw': {'train_loss': 15.083289, 'val_loss': 14.790324, 'test_loss': 14.260864}}
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2024-11-13 21:03:43,602 (client:354) INFO: {'Role': 'Client #6', 'Round': 31, 'Results_raw': {'train_loss': 8.052354, 'val_loss': 7.309942, 'test_loss': 7.468931}}
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2024-11-13 21:04:19,440 (client:354) INFO: {'Role': 'Client #7', 'Round': 31, 'Results_raw': {'train_loss': 10.156892, 'val_loss': 8.89032, 'test_loss': 9.20106}}
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2024-11-13 21:04:56,206 (client:354) INFO: {'Role': 'Client #8', 'Round': 31, 'Results_raw': {'train_loss': 14.731434, 'val_loss': 13.946524, 'test_loss': 13.367795}}
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2024-11-13 21:04:56,209 (server:615) INFO: {'Role': 'Server #', 'Round': 30, 'Results_weighted_avg': {'test_loss': np.float64(59831.305627), 'test_avg_loss': np.float64(17.638946), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61612.635339), 'val_avg_loss': np.float64(18.164102), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59831.305627), 'test_avg_loss': np.float64(17.638946), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61612.635339), 'val_avg_loss': np.float64(18.164102), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8223.413614), 'test_loss_bottom_decile': np.float64(50492.595398), 'test_loss_top_decile': np.float64(67352.186157), 'test_loss_min': np.float64(39283.070068), 'test_loss_max': np.float64(67352.186157), 'test_loss_bottom10%': np.float64(39283.070068), 'test_loss_top10%': np.float64(67352.186157), 'test_loss_cos1': np.float64(0.990686), 'test_loss_entropy': np.float64(2.292321), 'test_avg_loss_std': np.float64(2.424355), 'test_avg_loss_bottom_decile': np.float64(14.885789), 'test_avg_loss_top_decile': np.float64(19.856187), 'test_avg_loss_min': np.float64(11.581094), 'test_avg_loss_max': np.float64(19.856187), 'test_avg_loss_bottom10%': np.float64(11.581094), 'test_avg_loss_top10%': np.float64(19.856187), 'test_avg_loss_cos1': np.float64(0.990686), 'test_avg_loss_entropy': np.float64(2.292321), 'val_loss_std': np.float64(8427.607312), 'val_loss_bottom_decile': np.float64(52811.236694), 'val_loss_top_decile': np.float64(69463.624451), 'val_loss_min': np.float64(40064.81366), 'val_loss_max': np.float64(69463.624451), 'val_loss_bottom10%': np.float64(40064.81366), 'val_loss_top10%': np.float64(69463.624451), 'val_loss_cos1': np.float64(0.990774), 'val_loss_entropy': np.float64(2.29238), 'val_avg_loss_std': np.float64(2.484554), 'val_avg_loss_bottom_decile': np.float64(15.56935), 'val_avg_loss_top_decile': np.float64(20.478663), 'val_avg_loss_min': np.float64(11.811561), 'val_avg_loss_max': np.float64(20.478663), 'val_avg_loss_bottom10%': np.float64(11.811561), 'val_avg_loss_top10%': np.float64(20.478663), 'val_avg_loss_cos1': np.float64(0.990774), 'val_avg_loss_entropy': np.float64(2.29238)}}
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2024-11-13 21:04:56,249 (server:353) INFO: Server: Starting evaluation at the end of round 31.
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2024-11-13 21:04:56,250 (server:359) INFO: ----------- Starting a new training round (Round #32) -------------
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2024-11-13 21:06:45,073 (client:354) INFO: {'Role': 'Client #5', 'Round': 32, 'Results_raw': {'train_loss': 12.469319, 'val_loss': 11.808971, 'test_loss': 12.506188}}
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2024-11-13 21:07:20,229 (client:354) INFO: {'Role': 'Client #10', 'Round': 32, 'Results_raw': {'train_loss': 15.683098, 'val_loss': 14.325149, 'test_loss': 14.638494}}
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2024-11-13 21:07:55,682 (client:354) INFO: {'Role': 'Client #3', 'Round': 32, 'Results_raw': {'train_loss': 13.723469, 'val_loss': 12.745627, 'test_loss': 12.841823}}
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2024-11-13 21:08:31,425 (client:354) INFO: {'Role': 'Client #2', 'Round': 32, 'Results_raw': {'train_loss': 14.449584, 'val_loss': 13.328693, 'test_loss': 13.711081}}
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2024-11-13 21:09:07,529 (client:354) INFO: {'Role': 'Client #7', 'Round': 32, 'Results_raw': {'train_loss': 10.162205, 'val_loss': 8.977412, 'test_loss': 9.257562}}
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2024-11-13 21:09:42,953 (client:354) INFO: {'Role': 'Client #8', 'Round': 32, 'Results_raw': {'train_loss': 14.812284, 'val_loss': 13.713108, 'test_loss': 13.196678}}
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2024-11-13 21:10:18,183 (client:354) INFO: {'Role': 'Client #9', 'Round': 32, 'Results_raw': {'train_loss': 13.679551, 'val_loss': 13.501145, 'test_loss': 13.065206}}
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2024-11-13 21:10:53,373 (client:354) INFO: {'Role': 'Client #1', 'Round': 32, 'Results_raw': {'train_loss': 15.114185, 'val_loss': 14.962933, 'test_loss': 14.312543}}
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2024-11-13 21:11:29,628 (client:354) INFO: {'Role': 'Client #4', 'Round': 32, 'Results_raw': {'train_loss': 15.202026, 'val_loss': 13.587676, 'test_loss': 14.050735}}
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2024-11-13 21:12:04,541 (client:354) INFO: {'Role': 'Client #6', 'Round': 32, 'Results_raw': {'train_loss': 8.052407, 'val_loss': 7.282108, 'test_loss': 7.430065}}
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2024-11-13 21:12:04,545 (server:615) INFO: {'Role': 'Server #', 'Round': 31, 'Results_weighted_avg': {'test_loss': np.float64(59979.372736), 'test_avg_loss': np.float64(17.682598), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61836.995172), 'val_avg_loss': np.float64(18.230246), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59979.372736), 'test_avg_loss': np.float64(17.682598), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61836.995172), 'val_avg_loss': np.float64(18.230246), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8255.385821), 'test_loss_bottom_decile': np.float64(50925.515991), 'test_loss_top_decile': np.float64(67458.442932), 'test_loss_min': np.float64(39117.471863), 'test_loss_max': np.float64(67458.442932), 'test_loss_bottom10%': np.float64(39117.471863), 'test_loss_top10%': np.float64(67458.442932), 'test_loss_cos1': np.float64(0.990661), 'test_loss_entropy': np.float64(2.292264), 'test_avg_loss_std': np.float64(2.433781), 'test_avg_loss_bottom_decile': np.float64(15.013419), 'test_avg_loss_top_decile': np.float64(19.887513), 'test_avg_loss_min': np.float64(11.532274), 'test_avg_loss_max': np.float64(19.887513), 'test_avg_loss_bottom10%': np.float64(11.532274), 'test_avg_loss_top10%': np.float64(19.887513), 'test_avg_loss_cos1': np.float64(0.990661), 'test_avg_loss_entropy': np.float64(2.292264), 'val_loss_std': np.float64(8465.598175), 'val_loss_bottom_decile': np.float64(53325.018982), 'val_loss_top_decile': np.float64(69286.556763), 'val_loss_min': np.float64(39931.322754), 'val_loss_max': np.float64(69286.556763), 'val_loss_bottom10%': np.float64(39931.322754), 'val_loss_top10%': np.float64(69286.556763), 'val_loss_cos1': np.float64(0.990759), 'val_loss_entropy': np.float64(2.292327), 'val_avg_loss_std': np.float64(2.495754), 'val_avg_loss_bottom_decile': np.float64(15.720819), 'val_avg_loss_top_decile': np.float64(20.426461), 'val_avg_loss_min': np.float64(11.772206), 'val_avg_loss_max': np.float64(20.426461), 'val_avg_loss_bottom10%': np.float64(11.772206), 'val_avg_loss_top10%': np.float64(20.426461), 'val_avg_loss_cos1': np.float64(0.990759), 'val_avg_loss_entropy': np.float64(2.292327)}}
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2024-11-13 21:12:04,578 (server:353) INFO: Server: Starting evaluation at the end of round 32.
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2024-11-13 21:12:04,578 (server:359) INFO: ----------- Starting a new training round (Round #33) -------------
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2024-11-13 21:13:46,872 (client:354) INFO: {'Role': 'Client #9', 'Round': 33, 'Results_raw': {'train_loss': 13.771832, 'val_loss': 13.319839, 'test_loss': 12.993294}}
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2024-11-13 21:14:24,561 (client:354) INFO: {'Role': 'Client #8', 'Round': 33, 'Results_raw': {'train_loss': 14.67328, 'val_loss': 13.863575, 'test_loss': 13.362019}}
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2024-11-13 21:15:02,397 (client:354) INFO: {'Role': 'Client #7', 'Round': 33, 'Results_raw': {'train_loss': 10.093078, 'val_loss': 8.991348, 'test_loss': 9.333934}}
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2024-11-13 21:15:40,411 (client:354) INFO: {'Role': 'Client #10', 'Round': 33, 'Results_raw': {'train_loss': 15.651483, 'val_loss': 14.464011, 'test_loss': 14.732622}}
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2024-11-13 21:16:18,571 (client:354) INFO: {'Role': 'Client #1', 'Round': 33, 'Results_raw': {'train_loss': 15.087744, 'val_loss': 14.664772, 'test_loss': 14.151505}}
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2024-11-13 21:16:56,770 (client:354) INFO: {'Role': 'Client #5', 'Round': 33, 'Results_raw': {'train_loss': 12.474293, 'val_loss': 11.844811, 'test_loss': 12.577216}}
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2024-11-13 21:17:35,835 (client:354) INFO: {'Role': 'Client #3', 'Round': 33, 'Results_raw': {'train_loss': 13.75024, 'val_loss': 12.522696, 'test_loss': 12.683083}}
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2024-11-13 21:18:18,005 (client:354) INFO: {'Role': 'Client #6', 'Round': 33, 'Results_raw': {'train_loss': 8.045123, 'val_loss': 7.211711, 'test_loss': 7.357502}}
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2024-11-13 21:18:59,216 (client:354) INFO: {'Role': 'Client #2', 'Round': 33, 'Results_raw': {'train_loss': 14.466731, 'val_loss': 13.201113, 'test_loss': 13.586934}}
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2024-11-13 21:19:37,839 (client:354) INFO: {'Role': 'Client #4', 'Round': 33, 'Results_raw': {'train_loss': 15.216575, 'val_loss': 13.53661, 'test_loss': 14.018722}}
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2024-11-13 21:19:37,842 (server:615) INFO: {'Role': 'Server #', 'Round': 32, 'Results_weighted_avg': {'test_loss': np.float64(59597.176691), 'test_avg_loss': np.float64(17.569922), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61328.368274), 'val_avg_loss': np.float64(18.080297), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59597.176691), 'test_avg_loss': np.float64(17.569922), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61328.368274), 'val_avg_loss': np.float64(18.080297), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8203.758304), 'test_loss_bottom_decile': np.float64(50182.465363), 'test_loss_top_decile': np.float64(67152.808472), 'test_loss_min': np.float64(39093.45694), 'test_loss_max': np.float64(67152.808472), 'test_loss_bottom10%': np.float64(39093.45694), 'test_loss_top10%': np.float64(67152.808472), 'test_loss_cos1': np.float64(0.990658), 'test_loss_entropy': np.float64(2.292284), 'test_avg_loss_std': np.float64(2.418561), 'test_avg_loss_bottom_decile': np.float64(14.794359), 'test_avg_loss_top_decile': np.float64(19.797408), 'test_avg_loss_min': np.float64(11.525194), 'test_avg_loss_max': np.float64(19.797408), 'test_avg_loss_bottom10%': np.float64(11.525194), 'test_avg_loss_top10%': np.float64(19.797408), 'test_avg_loss_cos1': np.float64(0.990658), 'test_avg_loss_entropy': np.float64(2.292284), 'val_loss_std': np.float64(8351.468951), 'val_loss_bottom_decile': np.float64(52413.155029), 'val_loss_top_decile': np.float64(68792.977478), 'val_loss_min': np.float64(39936.173401), 'val_loss_max': np.float64(68792.977478), 'val_loss_bottom10%': np.float64(39936.173401), 'val_loss_top10%': np.float64(68792.977478), 'val_loss_cos1': np.float64(0.990855), 'val_loss_entropy': np.float64(2.292461), 'val_avg_loss_std': np.float64(2.462108), 'val_avg_loss_bottom_decile': np.float64(15.451991), 'val_avg_loss_top_decile': np.float64(20.280949), 'val_avg_loss_min': np.float64(11.773636), 'val_avg_loss_max': np.float64(20.280949), 'val_avg_loss_bottom10%': np.float64(11.773636), 'val_avg_loss_top10%': np.float64(20.280949), 'val_avg_loss_cos1': np.float64(0.990855), 'val_avg_loss_entropy': np.float64(2.292461)}}
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2024-11-13 21:19:37,876 (server:353) INFO: Server: Starting evaluation at the end of round 33.
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2024-11-13 21:19:37,876 (server:359) INFO: ----------- Starting a new training round (Round #34) -------------
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2024-11-13 21:21:26,027 (client:354) INFO: {'Role': 'Client #4', 'Round': 34, 'Results_raw': {'train_loss': 15.155718, 'val_loss': 13.600476, 'test_loss': 14.007336}}
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2024-11-13 21:22:04,346 (client:354) INFO: {'Role': 'Client #7', 'Round': 34, 'Results_raw': {'train_loss': 9.989411, 'val_loss': 9.059143, 'test_loss': 9.319273}}
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2024-11-13 21:22:42,771 (client:354) INFO: {'Role': 'Client #8', 'Round': 34, 'Results_raw': {'train_loss': 14.643066, 'val_loss': 13.870077, 'test_loss': 13.329649}}
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2024-11-13 21:23:23,839 (client:354) INFO: {'Role': 'Client #10', 'Round': 34, 'Results_raw': {'train_loss': 15.644985, 'val_loss': 14.372738, 'test_loss': 14.663461}}
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2024-11-13 21:24:02,345 (client:354) INFO: {'Role': 'Client #5', 'Round': 34, 'Results_raw': {'train_loss': 12.502033, 'val_loss': 11.814116, 'test_loss': 12.535571}}
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2024-11-13 21:24:40,967 (client:354) INFO: {'Role': 'Client #2', 'Round': 34, 'Results_raw': {'train_loss': 14.356089, 'val_loss': 13.365809, 'test_loss': 13.744829}}
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2024-11-13 21:25:17,675 (client:354) INFO: {'Role': 'Client #9', 'Round': 34, 'Results_raw': {'train_loss': 13.670456, 'val_loss': 13.396746, 'test_loss': 13.108867}}
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2024-11-13 21:25:55,135 (client:354) INFO: {'Role': 'Client #3', 'Round': 34, 'Results_raw': {'train_loss': 13.660199, 'val_loss': 12.588429, 'test_loss': 12.764274}}
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2024-11-13 21:26:35,697 (client:354) INFO: {'Role': 'Client #1', 'Round': 34, 'Results_raw': {'train_loss': 15.02338, 'val_loss': 14.869987, 'test_loss': 14.322647}}
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2024-11-13 21:27:12,386 (client:354) INFO: {'Role': 'Client #6', 'Round': 34, 'Results_raw': {'train_loss': 8.025623, 'val_loss': 7.262879, 'test_loss': 7.407759}}
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2024-11-13 21:27:12,390 (server:615) INFO: {'Role': 'Server #', 'Round': 33, 'Results_weighted_avg': {'test_loss': np.float64(60162.847934), 'test_avg_loss': np.float64(17.736689), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61947.686279), 'val_avg_loss': np.float64(18.262879), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60162.847934), 'test_avg_loss': np.float64(17.736689), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61947.686279), 'val_avg_loss': np.float64(18.262879), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8345.07152), 'test_loss_bottom_decile': np.float64(51024.306091), 'test_loss_top_decile': np.float64(67843.133728), 'test_loss_min': np.float64(39050.462433), 'test_loss_max': np.float64(67843.133728), 'test_loss_bottom10%': np.float64(39050.462433), 'test_loss_top10%': np.float64(67843.133728), 'test_loss_cos1': np.float64(0.990517), 'test_loss_entropy': np.float64(2.292092), 'test_avg_loss_std': np.float64(2.460222), 'test_avg_loss_bottom_decile': np.float64(15.042543), 'test_avg_loss_top_decile': np.float64(20.000924), 'test_avg_loss_min': np.float64(11.512518), 'test_avg_loss_max': np.float64(20.000924), 'test_avg_loss_bottom10%': np.float64(11.512518), 'test_avg_loss_top10%': np.float64(20.000924), 'test_avg_loss_cos1': np.float64(0.990517), 'test_avg_loss_entropy': np.float64(2.292092), 'val_loss_std': np.float64(8531.33678), 'val_loss_bottom_decile': np.float64(53375.546448), 'val_loss_top_decile': np.float64(69714.011047), 'val_loss_min': np.float64(39921.900818), 'val_loss_max': np.float64(69714.011047), 'val_loss_bottom10%': np.float64(39921.900818), 'val_loss_top10%': np.float64(69714.011047), 'val_loss_cos1': np.float64(0.99065), 'val_loss_entropy': np.float64(2.292207), 'val_avg_loss_std': np.float64(2.515135), 'val_avg_loss_bottom_decile': np.float64(15.735715), 'val_avg_loss_top_decile': np.float64(20.55248), 'val_avg_loss_min': np.float64(11.769428), 'val_avg_loss_max': np.float64(20.55248), 'val_avg_loss_bottom10%': np.float64(11.769428), 'val_avg_loss_top10%': np.float64(20.55248), 'val_avg_loss_cos1': np.float64(0.99065), 'val_avg_loss_entropy': np.float64(2.292207)}}
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2024-11-13 21:27:12,432 (server:353) INFO: Server: Starting evaluation at the end of round 34.
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2024-11-13 21:27:12,432 (server:359) INFO: ----------- Starting a new training round (Round #35) -------------
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2024-11-13 21:28:55,923 (client:354) INFO: {'Role': 'Client #3', 'Round': 35, 'Results_raw': {'train_loss': 13.739336, 'val_loss': 12.607298, 'test_loss': 12.739888}}
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2024-11-13 21:29:36,776 (client:354) INFO: {'Role': 'Client #9', 'Round': 35, 'Results_raw': {'train_loss': 13.669473, 'val_loss': 13.286868, 'test_loss': 12.887884}}
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2024-11-13 21:30:17,183 (client:354) INFO: {'Role': 'Client #2', 'Round': 35, 'Results_raw': {'train_loss': 14.339519, 'val_loss': 13.410275, 'test_loss': 13.829181}}
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2024-11-13 21:30:55,461 (client:354) INFO: {'Role': 'Client #8', 'Round': 35, 'Results_raw': {'train_loss': 14.626759, 'val_loss': 13.776075, 'test_loss': 13.231727}}
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2024-11-13 21:31:31,988 (client:354) INFO: {'Role': 'Client #5', 'Round': 35, 'Results_raw': {'train_loss': 12.413904, 'val_loss': 11.779437, 'test_loss': 12.541076}}
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2024-11-13 21:32:09,475 (client:354) INFO: {'Role': 'Client #4', 'Round': 35, 'Results_raw': {'train_loss': 15.201204, 'val_loss': 13.487198, 'test_loss': 13.970689}}
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2024-11-13 21:32:49,706 (client:354) INFO: {'Role': 'Client #6', 'Round': 35, 'Results_raw': {'train_loss': 8.063726, 'val_loss': 7.343186, 'test_loss': 7.49259}}
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2024-11-13 21:33:28,993 (client:354) INFO: {'Role': 'Client #7', 'Round': 35, 'Results_raw': {'train_loss': 9.97305, 'val_loss': 9.153538, 'test_loss': 9.27128}}
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2024-11-13 21:34:07,133 (client:354) INFO: {'Role': 'Client #10', 'Round': 35, 'Results_raw': {'train_loss': 15.592792, 'val_loss': 14.34555, 'test_loss': 14.739348}}
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2024-11-13 21:34:45,275 (client:354) INFO: {'Role': 'Client #1', 'Round': 35, 'Results_raw': {'train_loss': 14.938411, 'val_loss': 14.717188, 'test_loss': 14.26193}}
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2024-11-13 21:34:45,281 (server:615) INFO: {'Role': 'Server #', 'Round': 34, 'Results_weighted_avg': {'test_loss': np.float64(59379.282269), 'test_avg_loss': np.float64(17.505685), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61104.76051), 'val_avg_loss': np.float64(18.014375), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59379.282269), 'test_avg_loss': np.float64(17.505685), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61104.76051), 'val_avg_loss': np.float64(18.014375), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8101.242851), 'test_loss_bottom_decile': np.float64(49630.131378), 'test_loss_top_decile': np.float64(66761.49585), 'test_loss_min': np.float64(39295.77829), 'test_loss_max': np.float64(66761.49585), 'test_loss_bottom10%': np.float64(39295.77829), 'test_loss_top10%': np.float64(66761.49585), 'test_loss_cos1': np.float64(0.990821), 'test_loss_entropy': np.float64(2.292483), 'test_avg_loss_std': np.float64(2.388338), 'test_avg_loss_bottom_decile': np.float64(14.631525), 'test_avg_loss_top_decile': np.float64(19.682045), 'test_avg_loss_min': np.float64(11.58484), 'test_avg_loss_max': np.float64(19.682045), 'test_avg_loss_bottom10%': np.float64(11.58484), 'test_avg_loss_top10%': np.float64(19.682045), 'test_avg_loss_cos1': np.float64(0.990821), 'test_avg_loss_entropy': np.float64(2.292483), 'val_loss_std': np.float64(8297.466344), 'val_loss_bottom_decile': np.float64(51733.445679), 'val_loss_top_decile': np.float64(68689.55426), 'val_loss_min': np.float64(40130.285583), 'val_loss_max': np.float64(68689.55426), 'val_loss_bottom10%': np.float64(40130.285583), 'val_loss_top10%': np.float64(68689.55426), 'val_loss_cos1': np.float64(0.990906), 'val_loss_entropy': np.float64(2.292549), 'val_avg_loss_std': np.float64(2.446187), 'val_avg_loss_bottom_decile': np.float64(15.251605), 'val_avg_loss_top_decile': np.float64(20.250458), 'val_avg_loss_min': np.float64(11.830862), 'val_avg_loss_max': np.float64(20.250458), 'val_avg_loss_bottom10%': np.float64(11.830862), 'val_avg_loss_top10%': np.float64(20.250458), 'val_avg_loss_cos1': np.float64(0.990906), 'val_avg_loss_entropy': np.float64(2.292549)}}
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2024-11-13 21:34:45,326 (server:353) INFO: Server: Starting evaluation at the end of round 35.
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2024-11-13 21:34:45,326 (server:359) INFO: ----------- Starting a new training round (Round #36) -------------
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2024-11-13 21:36:33,528 (client:354) INFO: {'Role': 'Client #9', 'Round': 36, 'Results_raw': {'train_loss': 13.614341, 'val_loss': 13.29347, 'test_loss': 12.966863}}
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2024-11-13 21:37:16,100 (client:354) INFO: {'Role': 'Client #6', 'Round': 36, 'Results_raw': {'train_loss': 7.971667, 'val_loss': 7.201196, 'test_loss': 7.390325}}
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2024-11-13 21:37:52,297 (client:354) INFO: {'Role': 'Client #8', 'Round': 36, 'Results_raw': {'train_loss': 14.566286, 'val_loss': 13.731051, 'test_loss': 13.184393}}
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2024-11-13 21:38:30,159 (client:354) INFO: {'Role': 'Client #5', 'Round': 36, 'Results_raw': {'train_loss': 12.448692, 'val_loss': 11.749531, 'test_loss': 12.511929}}
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2024-11-13 21:39:06,586 (client:354) INFO: {'Role': 'Client #7', 'Round': 36, 'Results_raw': {'train_loss': 9.951021, 'val_loss': 8.9434, 'test_loss': 9.271547}}
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2024-11-13 21:39:42,232 (client:354) INFO: {'Role': 'Client #4', 'Round': 36, 'Results_raw': {'train_loss': 15.092217, 'val_loss': 13.573488, 'test_loss': 14.062792}}
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2024-11-13 21:40:19,111 (client:354) INFO: {'Role': 'Client #3', 'Round': 36, 'Results_raw': {'train_loss': 13.651224, 'val_loss': 12.538384, 'test_loss': 12.753195}}
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2024-11-13 21:40:57,095 (client:354) INFO: {'Role': 'Client #2', 'Round': 36, 'Results_raw': {'train_loss': 14.356301, 'val_loss': 13.278048, 'test_loss': 13.604371}}
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2024-11-13 21:41:39,165 (client:354) INFO: {'Role': 'Client #10', 'Round': 36, 'Results_raw': {'train_loss': 15.566221, 'val_loss': 15.060399, 'test_loss': 15.154024}}
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2024-11-13 21:42:19,541 (client:354) INFO: {'Role': 'Client #1', 'Round': 36, 'Results_raw': {'train_loss': 14.89509, 'val_loss': 14.666153, 'test_loss': 14.123573}}
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2024-11-13 21:42:19,545 (server:615) INFO: {'Role': 'Server #', 'Round': 35, 'Results_weighted_avg': {'test_loss': np.float64(60037.135602), 'test_avg_loss': np.float64(17.699627), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61876.622305), 'val_avg_loss': np.float64(18.241929), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(60037.135602), 'test_avg_loss': np.float64(17.699627), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61876.622305), 'val_avg_loss': np.float64(18.241929), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8394.358585), 'test_loss_bottom_decile': np.float64(50528.796265), 'test_loss_top_decile': np.float64(67477.869629), 'test_loss_min': np.float64(38889.279663), 'test_loss_max': np.float64(67477.869629), 'test_loss_bottom10%': np.float64(38889.279663), 'test_loss_top10%': np.float64(67477.869629), 'test_loss_cos1': np.float64(0.990366), 'test_loss_entropy': np.float64(2.291917), 'test_avg_loss_std': np.float64(2.474752), 'test_avg_loss_bottom_decile': np.float64(14.896461), 'test_avg_loss_top_decile': np.float64(19.89324), 'test_avg_loss_min': np.float64(11.465), 'test_avg_loss_max': np.float64(19.89324), 'test_avg_loss_bottom10%': np.float64(11.465), 'test_avg_loss_top10%': np.float64(19.89324), 'test_avg_loss_cos1': np.float64(0.990366), 'test_avg_loss_entropy': np.float64(2.291917), 'val_loss_std': np.float64(8598.07688), 'val_loss_bottom_decile': np.float64(52852.32605), 'val_loss_top_decile': np.float64(69471.186096), 'val_loss_min': np.float64(39751.57724), 'val_loss_max': np.float64(69471.186096), 'val_loss_bottom10%': np.float64(39751.57724), 'val_loss_top10%': np.float64(69471.186096), 'val_loss_cos1': np.float64(0.990483), 'val_loss_entropy': np.float64(2.292009), 'val_avg_loss_std': np.float64(2.53481), 'val_avg_loss_bottom_decile': np.float64(15.581464), 'val_avg_loss_top_decile': np.float64(20.480892), 'val_avg_loss_min': np.float64(11.719215), 'val_avg_loss_max': np.float64(20.480892), 'val_avg_loss_bottom10%': np.float64(11.719215), 'val_avg_loss_top10%': np.float64(20.480892), 'val_avg_loss_cos1': np.float64(0.990483), 'val_avg_loss_entropy': np.float64(2.292009)}}
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2024-11-13 21:42:19,585 (server:353) INFO: Server: Starting evaluation at the end of round 36.
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2024-11-13 21:42:19,585 (server:359) INFO: ----------- Starting a new training round (Round #37) -------------
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2024-11-13 21:44:13,390 (client:354) INFO: {'Role': 'Client #9', 'Round': 37, 'Results_raw': {'train_loss': 13.608312, 'val_loss': 13.225419, 'test_loss': 12.902951}}
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2024-11-13 21:44:53,680 (client:354) INFO: {'Role': 'Client #8', 'Round': 37, 'Results_raw': {'train_loss': 14.58387, 'val_loss': 13.570336, 'test_loss': 12.999159}}
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2024-11-13 21:45:32,892 (client:354) INFO: {'Role': 'Client #1', 'Round': 37, 'Results_raw': {'train_loss': 14.925444, 'val_loss': 14.733066, 'test_loss': 14.278835}}
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2024-11-13 21:46:13,021 (client:354) INFO: {'Role': 'Client #2', 'Round': 37, 'Results_raw': {'train_loss': 14.268413, 'val_loss': 13.430745, 'test_loss': 13.798989}}
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2024-11-13 21:46:53,659 (client:354) INFO: {'Role': 'Client #4', 'Round': 37, 'Results_raw': {'train_loss': 15.104293, 'val_loss': 13.640651, 'test_loss': 14.093886}}
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2024-11-13 21:47:38,038 (client:354) INFO: {'Role': 'Client #5', 'Round': 37, 'Results_raw': {'train_loss': 12.419965, 'val_loss': 11.901889, 'test_loss': 12.644531}}
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2024-11-13 21:48:21,139 (client:354) INFO: {'Role': 'Client #3', 'Round': 37, 'Results_raw': {'train_loss': 13.657478, 'val_loss': 12.54547, 'test_loss': 12.665155}}
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2024-11-13 21:49:01,487 (client:354) INFO: {'Role': 'Client #6', 'Round': 37, 'Results_raw': {'train_loss': 8.014148, 'val_loss': 7.471726, 'test_loss': 7.624115}}
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2024-11-13 21:49:44,344 (client:354) INFO: {'Role': 'Client #10', 'Round': 37, 'Results_raw': {'train_loss': 15.567133, 'val_loss': 14.447435, 'test_loss': 14.838024}}
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2024-11-13 21:50:24,252 (client:354) INFO: {'Role': 'Client #7', 'Round': 37, 'Results_raw': {'train_loss': 9.898723, 'val_loss': 8.740388, 'test_loss': 9.038244}}
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2024-11-13 21:50:24,256 (server:615) INFO: {'Role': 'Server #', 'Round': 36, 'Results_weighted_avg': {'test_loss': np.float64(59449.82886), 'test_avg_loss': np.float64(17.526483), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61166.358118), 'val_avg_loss': np.float64(18.032535), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59449.82886), 'test_avg_loss': np.float64(17.526483), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61166.358118), 'val_avg_loss': np.float64(18.032535), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8176.152528), 'test_loss_bottom_decile': np.float64(49953.972504), 'test_loss_top_decile': np.float64(66913.169128), 'test_loss_min': np.float64(39055.166199), 'test_loss_max': np.float64(66913.169128), 'test_loss_bottom10%': np.float64(39055.166199), 'test_loss_top10%': np.float64(66913.169128), 'test_loss_cos1': np.float64(0.990675), 'test_loss_entropy': np.float64(2.292305), 'test_avg_loss_std': np.float64(2.410422), 'test_avg_loss_bottom_decile': np.float64(14.726997), 'test_avg_loss_top_decile': np.float64(19.72676), 'test_avg_loss_min': np.float64(11.513905), 'test_avg_loss_max': np.float64(19.72676), 'test_avg_loss_bottom10%': np.float64(11.513905), 'test_avg_loss_top10%': np.float64(19.72676), 'test_avg_loss_cos1': np.float64(0.990675), 'test_avg_loss_entropy': np.float64(2.292305), 'val_loss_std': np.float64(8358.297159), 'val_loss_bottom_decile': np.float64(52154.245361), 'val_loss_top_decile': np.float64(68771.480835), 'val_loss_min': np.float64(39892.731506), 'val_loss_max': np.float64(68771.480835), 'val_loss_bottom10%': np.float64(39892.731506), 'val_loss_top10%': np.float64(68771.480835), 'val_loss_cos1': np.float64(0.990792), 'val_loss_entropy': np.float64(2.292407), 'val_avg_loss_std': np.float64(2.464121), 'val_avg_loss_bottom_decile': np.float64(15.375662), 'val_avg_loss_top_decile': np.float64(20.274611), 'val_avg_loss_min': np.float64(11.760829), 'val_avg_loss_max': np.float64(20.274611), 'val_avg_loss_bottom10%': np.float64(11.760829), 'val_avg_loss_top10%': np.float64(20.274611), 'val_avg_loss_cos1': np.float64(0.990792), 'val_avg_loss_entropy': np.float64(2.292407)}}
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2024-11-13 21:50:24,290 (server:353) INFO: Server: Starting evaluation at the end of round 37.
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2024-11-13 21:50:24,290 (server:359) INFO: ----------- Starting a new training round (Round #38) -------------
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2024-11-13 21:52:33,596 (client:354) INFO: {'Role': 'Client #4', 'Round': 38, 'Results_raw': {'train_loss': 15.08223, 'val_loss': 13.470053, 'test_loss': 13.95139}}
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2024-11-13 21:53:13,419 (client:354) INFO: {'Role': 'Client #2', 'Round': 38, 'Results_raw': {'train_loss': 14.247096, 'val_loss': 13.235777, 'test_loss': 13.70653}}
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2024-11-13 21:53:54,281 (client:354) INFO: {'Role': 'Client #9', 'Round': 38, 'Results_raw': {'train_loss': 13.605363, 'val_loss': 13.215313, 'test_loss': 12.922056}}
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2024-11-13 21:54:35,363 (client:354) INFO: {'Role': 'Client #6', 'Round': 38, 'Results_raw': {'train_loss': 7.952847, 'val_loss': 7.22457, 'test_loss': 7.412802}}
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2024-11-13 21:55:18,040 (client:354) INFO: {'Role': 'Client #5', 'Round': 38, 'Results_raw': {'train_loss': 12.389828, 'val_loss': 11.711678, 'test_loss': 12.421299}}
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2024-11-13 21:56:00,858 (client:354) INFO: {'Role': 'Client #7', 'Round': 38, 'Results_raw': {'train_loss': 9.949084, 'val_loss': 8.834738, 'test_loss': 9.204269}}
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2024-11-13 21:56:49,804 (client:354) INFO: {'Role': 'Client #10', 'Round': 38, 'Results_raw': {'train_loss': 15.524554, 'val_loss': 14.489064, 'test_loss': 14.820233}}
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2024-11-13 21:57:28,116 (client:354) INFO: {'Role': 'Client #3', 'Round': 38, 'Results_raw': {'train_loss': 13.628428, 'val_loss': 12.535096, 'test_loss': 12.706518}}
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2024-11-13 21:58:07,713 (client:354) INFO: {'Role': 'Client #1', 'Round': 38, 'Results_raw': {'train_loss': 14.797258, 'val_loss': 14.80244, 'test_loss': 14.263584}}
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2024-11-13 21:58:50,771 (client:354) INFO: {'Role': 'Client #8', 'Round': 38, 'Results_raw': {'train_loss': 14.595059, 'val_loss': 14.363961, 'test_loss': 13.688927}}
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2024-11-13 21:58:50,776 (server:615) INFO: {'Role': 'Server #', 'Round': 37, 'Results_weighted_avg': {'test_loss': np.float64(59261.664084), 'test_avg_loss': np.float64(17.471009), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60940.867908), 'val_avg_loss': np.float64(17.966058), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59261.664084), 'test_avg_loss': np.float64(17.471009), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60940.867908), 'val_avg_loss': np.float64(17.966058), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7933.19015), 'test_loss_bottom_decile': np.float64(50403.768768), 'test_loss_top_decile': np.float64(66385.797913), 'test_loss_min': np.float64(39246.060059), 'test_loss_max': np.float64(66385.797913), 'test_loss_bottom10%': np.float64(39246.060059), 'test_loss_top10%': np.float64(66385.797913), 'test_loss_cos1': np.float64(0.991158), 'test_loss_entropy': np.float64(2.292848), 'test_avg_loss_std': np.float64(2.338794), 'test_avg_loss_bottom_decile': np.float64(14.859602), 'test_avg_loss_top_decile': np.float64(19.571285), 'test_avg_loss_min': np.float64(11.570183), 'test_avg_loss_max': np.float64(19.571285), 'test_avg_loss_bottom10%': np.float64(11.570183), 'test_avg_loss_top10%': np.float64(19.571285), 'test_avg_loss_cos1': np.float64(0.991158), 'test_avg_loss_entropy': np.float64(2.292848), 'val_loss_std': np.float64(8095.065985), 'val_loss_bottom_decile': np.float64(52532.335144), 'val_loss_top_decile': np.float64(68205.64032), 'val_loss_min': np.float64(40123.812256), 'val_loss_max': np.float64(68205.64032), 'val_loss_bottom10%': np.float64(40123.812256), 'val_loss_top10%': np.float64(68205.64032), 'val_loss_cos1': np.float64(0.991293), 'val_loss_entropy': np.float64(2.292971), 'val_avg_loss_std': np.float64(2.386517), 'val_avg_loss_bottom_decile': np.float64(15.487127), 'val_avg_loss_top_decile': np.float64(20.107795), 'val_avg_loss_min': np.float64(11.828954), 'val_avg_loss_max': np.float64(20.107795), 'val_avg_loss_bottom10%': np.float64(11.828954), 'val_avg_loss_top10%': np.float64(20.107795), 'val_avg_loss_cos1': np.float64(0.991293), 'val_avg_loss_entropy': np.float64(2.292971)}}
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2024-11-13 21:58:50,813 (server:353) INFO: Server: Starting evaluation at the end of round 38.
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2024-11-13 21:58:50,813 (server:359) INFO: ----------- Starting a new training round (Round #39) -------------
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2024-11-13 22:00:45,784 (client:354) INFO: {'Role': 'Client #5', 'Round': 39, 'Results_raw': {'train_loss': 12.351123, 'val_loss': 11.729494, 'test_loss': 12.498128}}
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2024-11-13 22:01:26,426 (client:354) INFO: {'Role': 'Client #10', 'Round': 39, 'Results_raw': {'train_loss': 15.456327, 'val_loss': 14.439491, 'test_loss': 14.786837}}
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2024-11-13 22:02:09,778 (client:354) INFO: {'Role': 'Client #7', 'Round': 39, 'Results_raw': {'train_loss': 9.864975, 'val_loss': 8.899883, 'test_loss': 9.282211}}
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2024-11-13 22:02:48,195 (client:354) INFO: {'Role': 'Client #3', 'Round': 39, 'Results_raw': {'train_loss': 13.538887, 'val_loss': 12.584525, 'test_loss': 12.795189}}
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2024-11-13 22:03:24,872 (client:354) INFO: {'Role': 'Client #8', 'Round': 39, 'Results_raw': {'train_loss': 14.459754, 'val_loss': 13.784616, 'test_loss': 13.256139}}
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2024-11-13 22:04:01,237 (client:354) INFO: {'Role': 'Client #4', 'Round': 39, 'Results_raw': {'train_loss': 15.070777, 'val_loss': 13.538755, 'test_loss': 13.993459}}
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2024-11-13 22:04:35,613 (client:354) INFO: {'Role': 'Client #2', 'Round': 39, 'Results_raw': {'train_loss': 14.246846, 'val_loss': 13.29839, 'test_loss': 13.646019}}
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2024-11-13 22:05:08,147 (client:354) INFO: {'Role': 'Client #6', 'Round': 39, 'Results_raw': {'train_loss': 7.928665, 'val_loss': 7.278532, 'test_loss': 7.449338}}
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2024-11-13 22:05:43,855 (client:354) INFO: {'Role': 'Client #1', 'Round': 39, 'Results_raw': {'train_loss': 14.812345, 'val_loss': 14.845892, 'test_loss': 14.331708}}
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2024-11-13 22:06:20,591 (client:354) INFO: {'Role': 'Client #9', 'Round': 39, 'Results_raw': {'train_loss': 13.541579, 'val_loss': 13.223201, 'test_loss': 12.946735}}
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2024-11-13 22:06:20,594 (server:615) INFO: {'Role': 'Server #', 'Round': 38, 'Results_weighted_avg': {'test_loss': np.float64(59873.214569), 'test_avg_loss': np.float64(17.651301), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61600.724051), 'val_avg_loss': np.float64(18.160591), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59873.214569), 'test_avg_loss': np.float64(17.651301), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61600.724051), 'val_avg_loss': np.float64(18.160591), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8282.162317), 'test_loss_bottom_decile': np.float64(50804.430695), 'test_loss_top_decile': np.float64(67518.108032), 'test_loss_min': np.float64(38959.352081), 'test_loss_max': np.float64(67518.108032), 'test_loss_bottom10%': np.float64(38959.352081), 'test_loss_top10%': np.float64(67518.108032), 'test_loss_cos1': np.float64(0.990568), 'test_loss_entropy': np.float64(2.292158), 'test_avg_loss_std': np.float64(2.441675), 'test_avg_loss_bottom_decile': np.float64(14.977721), 'test_avg_loss_top_decile': np.float64(19.905103), 'test_avg_loss_min': np.float64(11.485658), 'test_avg_loss_max': np.float64(19.905103), 'test_avg_loss_bottom10%': np.float64(11.485658), 'test_avg_loss_top10%': np.float64(19.905103), 'test_avg_loss_cos1': np.float64(0.990568), 'test_avg_loss_entropy': np.float64(2.292158), 'val_loss_std': np.float64(8489.368583), 'val_loss_bottom_decile': np.float64(52985.719055), 'val_loss_top_decile': np.float64(69581.949036), 'val_loss_min': np.float64(39808.315948), 'val_loss_max': np.float64(69581.949036), 'val_loss_bottom10%': np.float64(39808.315948), 'val_loss_top10%': np.float64(69581.949036), 'val_loss_cos1': np.float64(0.990637), 'val_loss_entropy': np.float64(2.292212), 'val_avg_loss_std': np.float64(2.502762), 'val_avg_loss_bottom_decile': np.float64(15.62079), 'val_avg_loss_top_decile': np.float64(20.513546), 'val_avg_loss_min': np.float64(11.735942), 'val_avg_loss_max': np.float64(20.513546), 'val_avg_loss_bottom10%': np.float64(11.735942), 'val_avg_loss_top10%': np.float64(20.513546), 'val_avg_loss_cos1': np.float64(0.990637), 'val_avg_loss_entropy': np.float64(2.292212)}}
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2024-11-13 22:06:20,634 (server:353) INFO: Server: Starting evaluation at the end of round 39.
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2024-11-13 22:06:20,635 (server:359) INFO: ----------- Starting a new training round (Round #40) -------------
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2024-11-13 22:07:58,265 (client:354) INFO: {'Role': 'Client #3', 'Round': 40, 'Results_raw': {'train_loss': 13.53293, 'val_loss': 12.453768, 'test_loss': 12.586559}}
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2024-11-13 22:08:30,692 (client:354) INFO: {'Role': 'Client #5', 'Round': 40, 'Results_raw': {'train_loss': 12.349156, 'val_loss': 11.857458, 'test_loss': 12.611326}}
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2024-11-13 22:09:07,016 (client:354) INFO: {'Role': 'Client #4', 'Round': 40, 'Results_raw': {'train_loss': 15.044206, 'val_loss': 13.310939, 'test_loss': 13.858428}}
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2024-11-13 22:09:43,716 (client:354) INFO: {'Role': 'Client #6', 'Round': 40, 'Results_raw': {'train_loss': 7.948511, 'val_loss': 7.220376, 'test_loss': 7.375351}}
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2024-11-13 22:10:21,444 (client:354) INFO: {'Role': 'Client #7', 'Round': 40, 'Results_raw': {'train_loss': 9.900623, 'val_loss': 8.691966, 'test_loss': 9.042094}}
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2024-11-13 22:11:02,100 (client:354) INFO: {'Role': 'Client #2', 'Round': 40, 'Results_raw': {'train_loss': 14.227456, 'val_loss': 13.122352, 'test_loss': 13.535365}}
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2024-11-13 22:11:36,977 (client:354) INFO: {'Role': 'Client #9', 'Round': 40, 'Results_raw': {'train_loss': 13.54079, 'val_loss': 13.288869, 'test_loss': 12.92991}}
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2024-11-13 22:12:12,313 (client:354) INFO: {'Role': 'Client #1', 'Round': 40, 'Results_raw': {'train_loss': 14.806644, 'val_loss': 14.853908, 'test_loss': 14.265282}}
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2024-11-13 22:12:47,877 (client:354) INFO: {'Role': 'Client #8', 'Round': 40, 'Results_raw': {'train_loss': 14.50522, 'val_loss': 13.728952, 'test_loss': 13.155424}}
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2024-11-13 22:13:23,323 (client:354) INFO: {'Role': 'Client #10', 'Round': 40, 'Results_raw': {'train_loss': 15.442327, 'val_loss': 14.567396, 'test_loss': 14.971016}}
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2024-11-13 22:13:23,326 (server:615) INFO: {'Role': 'Server #', 'Round': 39, 'Results_weighted_avg': {'test_loss': np.float64(59253.566629), 'test_avg_loss': np.float64(17.468622), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60893.238022), 'val_avg_loss': np.float64(17.952016), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59253.566629), 'test_avg_loss': np.float64(17.468622), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60893.238022), 'val_avg_loss': np.float64(17.952016), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7992.487523), 'test_loss_bottom_decile': np.float64(49950.396362), 'test_loss_top_decile': np.float64(66834.774292), 'test_loss_min': np.float64(39312.167206), 'test_loss_max': np.float64(66834.774292), 'test_loss_bottom10%': np.float64(39312.167206), 'test_loss_top10%': np.float64(66834.774292), 'test_loss_cos1': np.float64(0.991025), 'test_loss_entropy': np.float64(2.292715), 'test_avg_loss_std': np.float64(2.356276), 'test_avg_loss_bottom_decile': np.float64(14.725942), 'test_avg_loss_top_decile': np.float64(19.703648), 'test_avg_loss_min': np.float64(11.589672), 'test_avg_loss_max': np.float64(19.703648), 'test_avg_loss_bottom10%': np.float64(11.589672), 'test_avg_loss_top10%': np.float64(19.703648), 'test_avg_loss_cos1': np.float64(0.991025), 'test_avg_loss_entropy': np.float64(2.292715), 'val_loss_std': np.float64(8164.909607), 'val_loss_bottom_decile': np.float64(52051.594238), 'val_loss_top_decile': np.float64(68599.226379), 'val_loss_min': np.float64(40128.169098), 'val_loss_max': np.float64(68599.226379), 'val_loss_bottom10%': np.float64(40128.169098), 'val_loss_top10%': np.float64(68599.226379), 'val_loss_cos1': np.float64(0.99113), 'val_loss_entropy': np.float64(2.292806), 'val_avg_loss_std': np.float64(2.407108), 'val_avg_loss_bottom_decile': np.float64(15.345399), 'val_avg_loss_top_decile': np.float64(20.223829), 'val_avg_loss_min': np.float64(11.830239), 'val_avg_loss_max': np.float64(20.223829), 'val_avg_loss_bottom10%': np.float64(11.830239), 'val_avg_loss_top10%': np.float64(20.223829), 'val_avg_loss_cos1': np.float64(0.99113), 'val_avg_loss_entropy': np.float64(2.292806)}}
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2024-11-13 22:13:23,367 (server:353) INFO: Server: Starting evaluation at the end of round 40.
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2024-11-13 22:13:23,367 (server:359) INFO: ----------- Starting a new training round (Round #41) -------------
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2024-11-13 22:15:00,560 (client:354) INFO: {'Role': 'Client #6', 'Round': 41, 'Results_raw': {'train_loss': 7.956075, 'val_loss': 7.210993, 'test_loss': 7.389466}}
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2024-11-13 22:15:42,321 (client:354) INFO: {'Role': 'Client #4', 'Round': 41, 'Results_raw': {'train_loss': 15.00898, 'val_loss': 13.533843, 'test_loss': 14.024813}}
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2024-11-13 22:16:25,092 (client:354) INFO: {'Role': 'Client #9', 'Round': 41, 'Results_raw': {'train_loss': 13.519904, 'val_loss': 13.273993, 'test_loss': 12.942176}}
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2024-11-13 22:17:07,314 (client:354) INFO: {'Role': 'Client #8', 'Round': 41, 'Results_raw': {'train_loss': 14.501444, 'val_loss': 13.537399, 'test_loss': 13.046101}}
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2024-11-13 22:17:49,663 (client:354) INFO: {'Role': 'Client #7', 'Round': 41, 'Results_raw': {'train_loss': 9.825456, 'val_loss': 8.896908, 'test_loss': 9.292943}}
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2024-11-13 22:18:32,082 (client:354) INFO: {'Role': 'Client #1', 'Round': 41, 'Results_raw': {'train_loss': 14.826654, 'val_loss': 14.696474, 'test_loss': 14.196026}}
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2024-11-13 22:19:15,447 (client:354) INFO: {'Role': 'Client #10', 'Round': 41, 'Results_raw': {'train_loss': 15.444179, 'val_loss': 14.329379, 'test_loss': 14.670735}}
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2024-11-13 22:19:53,069 (client:354) INFO: {'Role': 'Client #3', 'Round': 41, 'Results_raw': {'train_loss': 13.466889, 'val_loss': 12.731248, 'test_loss': 12.814107}}
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2024-11-13 22:20:29,473 (client:354) INFO: {'Role': 'Client #5', 'Round': 41, 'Results_raw': {'train_loss': 12.310025, 'val_loss': 11.762527, 'test_loss': 12.545573}}
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2024-11-13 22:21:05,710 (client:354) INFO: {'Role': 'Client #2', 'Round': 41, 'Results_raw': {'train_loss': 14.212772, 'val_loss': 13.174788, 'test_loss': 13.571247}}
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2024-11-13 22:21:05,713 (server:615) INFO: {'Role': 'Server #', 'Round': 40, 'Results_weighted_avg': {'test_loss': np.float64(59511.697656), 'test_avg_loss': np.float64(17.544722), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61188.809525), 'val_avg_loss': np.float64(18.039154), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59511.697656), 'test_avg_loss': np.float64(17.544722), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61188.809525), 'val_avg_loss': np.float64(18.039154), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8168.102396), 'test_loss_bottom_decile': np.float64(50296.238281), 'test_loss_top_decile': np.float64(66846.469788), 'test_loss_min': np.float64(39030.801575), 'test_loss_max': np.float64(66846.469788), 'test_loss_bottom10%': np.float64(39030.801575), 'test_loss_top10%': np.float64(66846.469788), 'test_loss_cos1': np.float64(0.990712), 'test_loss_entropy': np.float64(2.292341), 'test_avg_loss_std': np.float64(2.408049), 'test_avg_loss_bottom_decile': np.float64(14.8279), 'test_avg_loss_top_decile': np.float64(19.707096), 'test_avg_loss_min': np.float64(11.506722), 'test_avg_loss_max': np.float64(19.707096), 'test_avg_loss_bottom10%': np.float64(11.506722), 'test_avg_loss_top10%': np.float64(19.707096), 'test_avg_loss_cos1': np.float64(0.990712), 'test_avg_loss_entropy': np.float64(2.292341), 'val_loss_std': np.float64(8362.66601), 'val_loss_bottom_decile': np.float64(52340.139221), 'val_loss_top_decile': np.float64(69107.623779), 'val_loss_min': np.float64(39869.188446), 'val_loss_max': np.float64(69107.623779), 'val_loss_bottom10%': np.float64(39869.188446), 'val_loss_top10%': np.float64(69107.623779), 'val_loss_cos1': np.float64(0.99079), 'val_loss_entropy': np.float64(2.292404), 'val_avg_loss_std': np.float64(2.465409), 'val_avg_loss_bottom_decile': np.float64(15.430466), 'val_avg_loss_top_decile': np.float64(20.37371), 'val_avg_loss_min': np.float64(11.753888), 'val_avg_loss_max': np.float64(20.37371), 'val_avg_loss_bottom10%': np.float64(11.753888), 'val_avg_loss_top10%': np.float64(20.37371), 'val_avg_loss_cos1': np.float64(0.99079), 'val_avg_loss_entropy': np.float64(2.292404)}}
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2024-11-13 22:21:05,749 (server:353) INFO: Server: Starting evaluation at the end of round 41.
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2024-11-13 22:21:05,749 (server:359) INFO: ----------- Starting a new training round (Round #42) -------------
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2024-11-13 22:22:46,974 (client:354) INFO: {'Role': 'Client #3', 'Round': 42, 'Results_raw': {'train_loss': 13.463306, 'val_loss': 12.591954, 'test_loss': 12.71581}}
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2024-11-13 22:23:20,145 (client:354) INFO: {'Role': 'Client #6', 'Round': 42, 'Results_raw': {'train_loss': 7.838147, 'val_loss': 7.115371, 'test_loss': 7.318353}}
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2024-11-13 22:23:56,749 (client:354) INFO: {'Role': 'Client #10', 'Round': 42, 'Results_raw': {'train_loss': 15.378336, 'val_loss': 14.440815, 'test_loss': 14.750199}}
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2024-11-13 22:24:33,057 (client:354) INFO: {'Role': 'Client #8', 'Round': 42, 'Results_raw': {'train_loss': 14.518687, 'val_loss': 13.556053, 'test_loss': 13.037989}}
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2024-11-13 22:25:09,754 (client:354) INFO: {'Role': 'Client #7', 'Round': 42, 'Results_raw': {'train_loss': 9.917655, 'val_loss': 8.940833, 'test_loss': 9.325447}}
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2024-11-13 22:25:47,153 (client:354) INFO: {'Role': 'Client #4', 'Round': 42, 'Results_raw': {'train_loss': 14.998246, 'val_loss': 13.36758, 'test_loss': 13.897126}}
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2024-11-13 22:26:23,502 (client:354) INFO: {'Role': 'Client #5', 'Round': 42, 'Results_raw': {'train_loss': 12.289766, 'val_loss': 11.765991, 'test_loss': 12.521388}}
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2024-11-13 22:26:56,795 (client:354) INFO: {'Role': 'Client #2', 'Round': 42, 'Results_raw': {'train_loss': 14.118115, 'val_loss': 13.359522, 'test_loss': 13.691368}}
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2024-11-13 22:27:30,523 (client:354) INFO: {'Role': 'Client #9', 'Round': 42, 'Results_raw': {'train_loss': 13.564491, 'val_loss': 13.197115, 'test_loss': 12.856968}}
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2024-11-13 22:28:04,086 (client:354) INFO: {'Role': 'Client #1', 'Round': 42, 'Results_raw': {'train_loss': 14.767681, 'val_loss': 14.554515, 'test_loss': 14.125022}}
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2024-11-13 22:28:04,090 (server:615) INFO: {'Role': 'Server #', 'Round': 41, 'Results_weighted_avg': {'test_loss': np.float64(59814.764111), 'test_avg_loss': np.float64(17.63407), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61518.753186), 'val_avg_loss': np.float64(18.136425), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59814.764111), 'test_avg_loss': np.float64(17.63407), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61518.753186), 'val_avg_loss': np.float64(18.136425), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8321.760483), 'test_loss_bottom_decile': np.float64(50771.096436), 'test_loss_top_decile': np.float64(67326.364807), 'test_loss_min': np.float64(38796.087708), 'test_loss_max': np.float64(67326.364807), 'test_loss_bottom10%': np.float64(38796.087708), 'test_loss_top10%': np.float64(67326.364807), 'test_loss_cos1': np.float64(0.99046), 'test_loss_entropy': np.float64(2.292032), 'test_avg_loss_std': np.float64(2.453349), 'test_avg_loss_bottom_decile': np.float64(14.967894), 'test_avg_loss_top_decile': np.float64(19.848575), 'test_avg_loss_min': np.float64(11.437526), 'test_avg_loss_max': np.float64(19.848575), 'test_avg_loss_bottom10%': np.float64(11.437526), 'test_avg_loss_top10%': np.float64(19.848575), 'test_avg_loss_cos1': np.float64(0.99046), 'test_avg_loss_entropy': np.float64(2.292032), 'val_loss_std': np.float64(8493.111489), 'val_loss_bottom_decile': np.float64(52876.399048), 'val_loss_top_decile': np.float64(69004.467896), 'val_loss_min': np.float64(39626.199219), 'val_loss_max': np.float64(69004.467896), 'val_loss_bottom10%': np.float64(39626.199219), 'val_loss_top10%': np.float64(69004.467896), 'val_loss_cos1': np.float64(0.990604), 'val_loss_entropy': np.float64(2.292155), 'val_avg_loss_std': np.float64(2.503865), 'val_avg_loss_bottom_decile': np.float64(15.588561), 'val_avg_loss_top_decile': np.float64(20.343298), 'val_avg_loss_min': np.float64(11.682252), 'val_avg_loss_max': np.float64(20.343298), 'val_avg_loss_bottom10%': np.float64(11.682252), 'val_avg_loss_top10%': np.float64(20.343298), 'val_avg_loss_cos1': np.float64(0.990604), 'val_avg_loss_entropy': np.float64(2.292155)}}
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2024-11-13 22:28:04,124 (server:353) INFO: Server: Starting evaluation at the end of round 42.
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2024-11-13 22:28:04,125 (server:359) INFO: ----------- Starting a new training round (Round #43) -------------
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2024-11-13 22:29:38,198 (client:354) INFO: {'Role': 'Client #9', 'Round': 43, 'Results_raw': {'train_loss': 13.415881, 'val_loss': 13.192915, 'test_loss': 12.887068}}
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2024-11-13 22:30:11,040 (client:354) INFO: {'Role': 'Client #3', 'Round': 43, 'Results_raw': {'train_loss': 13.50882, 'val_loss': 12.580934, 'test_loss': 12.743204}}
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2024-11-13 22:30:44,583 (client:354) INFO: {'Role': 'Client #2', 'Round': 43, 'Results_raw': {'train_loss': 14.107987, 'val_loss': 13.183049, 'test_loss': 13.534201}}
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2024-11-13 22:31:17,619 (client:354) INFO: {'Role': 'Client #1', 'Round': 43, 'Results_raw': {'train_loss': 14.730604, 'val_loss': 14.783309, 'test_loss': 14.259136}}
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2024-11-13 22:31:51,738 (client:354) INFO: {'Role': 'Client #10', 'Round': 43, 'Results_raw': {'train_loss': 15.372287, 'val_loss': 14.174197, 'test_loss': 14.614647}}
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2024-11-13 22:32:25,338 (client:354) INFO: {'Role': 'Client #6', 'Round': 43, 'Results_raw': {'train_loss': 7.890212, 'val_loss': 7.210647, 'test_loss': 7.386885}}
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2024-11-13 22:32:58,781 (client:354) INFO: {'Role': 'Client #8', 'Round': 43, 'Results_raw': {'train_loss': 14.420271, 'val_loss': 13.532158, 'test_loss': 13.005958}}
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2024-11-13 22:33:32,474 (client:354) INFO: {'Role': 'Client #7', 'Round': 43, 'Results_raw': {'train_loss': 9.746645, 'val_loss': 8.737168, 'test_loss': 9.101791}}
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2024-11-13 22:34:05,932 (client:354) INFO: {'Role': 'Client #4', 'Round': 43, 'Results_raw': {'train_loss': 14.986166, 'val_loss': 13.400848, 'test_loss': 13.947111}}
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2024-11-13 22:34:39,514 (client:354) INFO: {'Role': 'Client #5', 'Round': 43, 'Results_raw': {'train_loss': 12.261154, 'val_loss': 11.773695, 'test_loss': 12.432948}}
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2024-11-13 22:34:39,517 (server:615) INFO: {'Role': 'Server #', 'Round': 42, 'Results_weighted_avg': {'test_loss': np.float64(59640.475449), 'test_avg_loss': np.float64(17.582687), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61323.982965), 'val_avg_loss': np.float64(18.079004), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59640.475449), 'test_avg_loss': np.float64(17.582687), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61323.982965), 'val_avg_loss': np.float64(18.079004), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8189.52188), 'test_loss_bottom_decile': np.float64(50909.779724), 'test_loss_top_decile': np.float64(67151.843262), 'test_loss_min': np.float64(38847.462494), 'test_loss_max': np.float64(67151.843262), 'test_loss_bottom10%': np.float64(38847.462494), 'test_loss_top10%': np.float64(67151.843262), 'test_loss_cos1': np.float64(0.990704), 'test_loss_entropy': np.float64(2.292308), 'test_avg_loss_std': np.float64(2.414364), 'test_avg_loss_bottom_decile': np.float64(15.008779), 'test_avg_loss_top_decile': np.float64(19.797124), 'test_avg_loss_min': np.float64(11.452672), 'test_avg_loss_max': np.float64(19.797124), 'test_avg_loss_bottom10%': np.float64(11.452672), 'test_avg_loss_top10%': np.float64(19.797124), 'test_avg_loss_cos1': np.float64(0.990704), 'test_avg_loss_entropy': np.float64(2.292308), 'val_loss_std': np.float64(8392.22232), 'val_loss_bottom_decile': np.float64(53025.253601), 'val_loss_top_decile': np.float64(69265.701538), 'val_loss_min': np.float64(39679.181946), 'val_loss_max': np.float64(69265.701538), 'val_loss_bottom10%': np.float64(39679.181946), 'val_loss_top10%': np.float64(69265.701538), 'val_loss_cos1': np.float64(0.990765), 'val_loss_entropy': np.float64(2.292354), 'val_avg_loss_std': np.float64(2.474122), 'val_avg_loss_bottom_decile': np.float64(15.632445), 'val_avg_loss_top_decile': np.float64(20.420313), 'val_avg_loss_min': np.float64(11.697872), 'val_avg_loss_max': np.float64(20.420313), 'val_avg_loss_bottom10%': np.float64(11.697872), 'val_avg_loss_top10%': np.float64(20.420313), 'val_avg_loss_cos1': np.float64(0.990765), 'val_avg_loss_entropy': np.float64(2.292354)}}
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2024-11-13 22:34:39,556 (server:353) INFO: Server: Starting evaluation at the end of round 43.
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2024-11-13 22:34:39,557 (server:359) INFO: ----------- Starting a new training round (Round #44) -------------
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2024-11-13 22:36:21,232 (client:354) INFO: {'Role': 'Client #1', 'Round': 44, 'Results_raw': {'train_loss': 14.753002, 'val_loss': 14.587727, 'test_loss': 14.106316}}
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2024-11-13 22:36:58,975 (client:354) INFO: {'Role': 'Client #8', 'Round': 44, 'Results_raw': {'train_loss': 14.429483, 'val_loss': 13.724374, 'test_loss': 13.186032}}
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2024-11-13 22:37:34,916 (client:354) INFO: {'Role': 'Client #4', 'Round': 44, 'Results_raw': {'train_loss': 14.924408, 'val_loss': 13.437826, 'test_loss': 13.953431}}
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2024-11-13 22:38:08,192 (client:354) INFO: {'Role': 'Client #7', 'Round': 44, 'Results_raw': {'train_loss': 9.762581, 'val_loss': 8.601373, 'test_loss': 8.989683}}
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2024-11-13 22:38:41,324 (client:354) INFO: {'Role': 'Client #6', 'Round': 44, 'Results_raw': {'train_loss': 7.826888, 'val_loss': 7.244659, 'test_loss': 7.407885}}
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2024-11-13 22:39:15,320 (client:354) INFO: {'Role': 'Client #5', 'Round': 44, 'Results_raw': {'train_loss': 12.266941, 'val_loss': 11.744603, 'test_loss': 12.483413}}
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2024-11-13 22:39:50,964 (client:354) INFO: {'Role': 'Client #10', 'Round': 44, 'Results_raw': {'train_loss': 15.369591, 'val_loss': 14.214694, 'test_loss': 14.50422}}
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2024-11-13 22:40:26,328 (client:354) INFO: {'Role': 'Client #9', 'Round': 44, 'Results_raw': {'train_loss': 13.430684, 'val_loss': 13.240571, 'test_loss': 12.970622}}
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2024-11-13 22:41:02,231 (client:354) INFO: {'Role': 'Client #2', 'Round': 44, 'Results_raw': {'train_loss': 14.183211, 'val_loss': 13.006194, 'test_loss': 13.465432}}
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2024-11-13 22:41:38,549 (client:354) INFO: {'Role': 'Client #3', 'Round': 44, 'Results_raw': {'train_loss': 13.491336, 'val_loss': 12.525146, 'test_loss': 12.680916}}
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2024-11-13 22:41:38,553 (server:615) INFO: {'Role': 'Server #', 'Round': 43, 'Results_weighted_avg': {'test_loss': np.float64(59515.402737), 'test_avg_loss': np.float64(17.545814), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61162.123175), 'val_avg_loss': np.float64(18.031286), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59515.402737), 'test_avg_loss': np.float64(17.545814), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61162.123175), 'val_avg_loss': np.float64(18.031286), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8190.706173), 'test_loss_bottom_decile': np.float64(50457.232178), 'test_loss_top_decile': np.float64(67120.31488), 'test_loss_min': np.float64(38920.61795), 'test_loss_max': np.float64(67120.31488), 'test_loss_bottom10%': np.float64(38920.61795), 'test_loss_top10%': np.float64(67120.31488), 'test_loss_cos1': np.float64(0.990662), 'test_loss_entropy': np.float64(2.292281), 'test_avg_loss_std': np.float64(2.414713), 'test_avg_loss_bottom_decile': np.float64(14.875363), 'test_avg_loss_top_decile': np.float64(19.787829), 'test_avg_loss_min': np.float64(11.474239), 'test_avg_loss_max': np.float64(19.787829), 'test_avg_loss_bottom10%': np.float64(11.474239), 'test_avg_loss_top10%': np.float64(19.787829), 'test_avg_loss_cos1': np.float64(0.990662), 'test_avg_loss_entropy': np.float64(2.292281), 'val_loss_std': np.float64(8376.105509), 'val_loss_bottom_decile': np.float64(52551.915466), 'val_loss_top_decile': np.float64(68902.321411), 'val_loss_min': np.float64(39717.580627), 'val_loss_max': np.float64(68902.321411), 'val_loss_bottom10%': np.float64(39717.580627), 'val_loss_top10%': np.float64(68902.321411), 'val_loss_cos1': np.float64(0.990752), 'val_loss_entropy': np.float64(2.292354), 'val_avg_loss_std': np.float64(2.469371), 'val_avg_loss_bottom_decile': np.float64(15.4929), 'val_avg_loss_top_decile': np.float64(20.313184), 'val_avg_loss_min': np.float64(11.709192), 'val_avg_loss_max': np.float64(20.313184), 'val_avg_loss_bottom10%': np.float64(11.709192), 'val_avg_loss_top10%': np.float64(20.313184), 'val_avg_loss_cos1': np.float64(0.990752), 'val_avg_loss_entropy': np.float64(2.292354)}}
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2024-11-13 22:41:38,597 (server:353) INFO: Server: Starting evaluation at the end of round 44.
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2024-11-13 22:41:38,598 (server:359) INFO: ----------- Starting a new training round (Round #45) -------------
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2024-11-13 22:43:14,513 (client:354) INFO: {'Role': 'Client #4', 'Round': 45, 'Results_raw': {'train_loss': 14.920223, 'val_loss': 13.85226, 'test_loss': 14.290449}}
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2024-11-13 22:43:47,225 (client:354) INFO: {'Role': 'Client #7', 'Round': 45, 'Results_raw': {'train_loss': 9.717161, 'val_loss': 8.691784, 'test_loss': 8.970627}}
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2024-11-13 22:44:19,840 (client:354) INFO: {'Role': 'Client #5', 'Round': 45, 'Results_raw': {'train_loss': 12.300904, 'val_loss': 11.925425, 'test_loss': 12.666266}}
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2024-11-13 22:44:52,251 (client:354) INFO: {'Role': 'Client #10', 'Round': 45, 'Results_raw': {'train_loss': 15.425156, 'val_loss': 14.251294, 'test_loss': 14.600834}}
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2024-11-13 22:45:24,874 (client:354) INFO: {'Role': 'Client #9', 'Round': 45, 'Results_raw': {'train_loss': 13.429893, 'val_loss': 13.098542, 'test_loss': 12.799391}}
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2024-11-13 22:45:57,704 (client:354) INFO: {'Role': 'Client #2', 'Round': 45, 'Results_raw': {'train_loss': 14.102923, 'val_loss': 13.016291, 'test_loss': 13.452763}}
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2024-11-13 22:46:30,402 (client:354) INFO: {'Role': 'Client #1', 'Round': 45, 'Results_raw': {'train_loss': 14.68794, 'val_loss': 14.550956, 'test_loss': 14.079919}}
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2024-11-13 22:47:02,825 (client:354) INFO: {'Role': 'Client #8', 'Round': 45, 'Results_raw': {'train_loss': 14.274211, 'val_loss': 13.468897, 'test_loss': 12.938011}}
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2024-11-13 22:47:35,858 (client:354) INFO: {'Role': 'Client #6', 'Round': 45, 'Results_raw': {'train_loss': 7.850327, 'val_loss': 7.199742, 'test_loss': 7.352043}}
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2024-11-13 22:48:08,823 (client:354) INFO: {'Role': 'Client #3', 'Round': 45, 'Results_raw': {'train_loss': 13.477947, 'val_loss': 12.424077, 'test_loss': 12.607211}}
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2024-11-13 22:48:08,827 (server:615) INFO: {'Role': 'Server #', 'Round': 44, 'Results_weighted_avg': {'test_loss': np.float64(59278.670535), 'test_avg_loss': np.float64(17.476023), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60942.755646), 'val_avg_loss': np.float64(17.966614), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59278.670535), 'test_avg_loss': np.float64(17.476023), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60942.755646), 'val_avg_loss': np.float64(17.966614), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8105.274009), 'test_loss_bottom_decile': np.float64(50366.01889), 'test_loss_top_decile': np.float64(66745.16925), 'test_loss_min': np.float64(38833.35968), 'test_loss_max': np.float64(66745.16925), 'test_loss_bottom10%': np.float64(38833.35968), 'test_loss_top10%': np.float64(66745.16925), 'test_loss_cos1': np.float64(0.990781), 'test_loss_entropy': np.float64(2.292412), 'test_avg_loss_std': np.float64(2.389527), 'test_avg_loss_bottom_decile': np.float64(14.848473), 'test_avg_loss_top_decile': np.float64(19.677232), 'test_avg_loss_min': np.float64(11.448514), 'test_avg_loss_max': np.float64(19.677232), 'test_avg_loss_bottom10%': np.float64(11.448514), 'test_avg_loss_top10%': np.float64(19.677232), 'test_avg_loss_cos1': np.float64(0.990781), 'test_avg_loss_entropy': np.float64(2.292412), 'val_loss_std': np.float64(8332.629663), 'val_loss_bottom_decile': np.float64(52441.811157), 'val_loss_top_decile': np.float64(68925.961731), 'val_loss_min': np.float64(39641.50354), 'val_loss_max': np.float64(68925.961731), 'val_loss_bottom10%': np.float64(39641.50354), 'val_loss_top10%': np.float64(68925.961731), 'val_loss_cos1': np.float64(0.990782), 'val_loss_entropy': np.float64(2.292397), 'val_avg_loss_std': np.float64(2.456554), 'val_avg_loss_bottom_decile': np.float64(15.46044), 'val_avg_loss_top_decile': np.float64(20.320154), 'val_avg_loss_min': np.float64(11.686764), 'val_avg_loss_max': np.float64(20.320154), 'val_avg_loss_bottom10%': np.float64(11.686764), 'val_avg_loss_top10%': np.float64(20.320154), 'val_avg_loss_cos1': np.float64(0.990782), 'val_avg_loss_entropy': np.float64(2.292397)}}
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2024-11-13 22:48:08,862 (server:353) INFO: Server: Starting evaluation at the end of round 45.
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2024-11-13 22:48:08,863 (server:359) INFO: ----------- Starting a new training round (Round #46) -------------
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2024-11-13 22:49:41,595 (client:354) INFO: {'Role': 'Client #10', 'Round': 46, 'Results_raw': {'train_loss': 15.314558, 'val_loss': 14.243351, 'test_loss': 14.537733}}
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2024-11-13 22:50:15,755 (client:354) INFO: {'Role': 'Client #8', 'Round': 46, 'Results_raw': {'train_loss': 14.35588, 'val_loss': 13.459666, 'test_loss': 12.919966}}
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2024-11-13 22:50:49,710 (client:354) INFO: {'Role': 'Client #4', 'Round': 46, 'Results_raw': {'train_loss': 14.867724, 'val_loss': 13.401849, 'test_loss': 13.872069}}
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2024-11-13 22:51:21,964 (client:354) INFO: {'Role': 'Client #5', 'Round': 46, 'Results_raw': {'train_loss': 12.230812, 'val_loss': 11.660536, 'test_loss': 12.387188}}
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2024-11-13 22:51:54,691 (client:354) INFO: {'Role': 'Client #3', 'Round': 46, 'Results_raw': {'train_loss': 13.37361, 'val_loss': 12.597086, 'test_loss': 12.782386}}
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2024-11-13 22:52:27,675 (client:354) INFO: {'Role': 'Client #2', 'Round': 46, 'Results_raw': {'train_loss': 14.082117, 'val_loss': 13.231666, 'test_loss': 13.575139}}
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2024-11-13 22:53:00,500 (client:354) INFO: {'Role': 'Client #6', 'Round': 46, 'Results_raw': {'train_loss': 7.848388, 'val_loss': 7.076603, 'test_loss': 7.246411}}
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2024-11-13 22:53:35,102 (client:354) INFO: {'Role': 'Client #7', 'Round': 46, 'Results_raw': {'train_loss': 9.683359, 'val_loss': 8.640891, 'test_loss': 9.002627}}
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2024-11-13 22:54:13,842 (client:354) INFO: {'Role': 'Client #1', 'Round': 46, 'Results_raw': {'train_loss': 14.606705, 'val_loss': 14.621487, 'test_loss': 14.14588}}
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2024-11-13 22:54:50,784 (client:354) INFO: {'Role': 'Client #9', 'Round': 46, 'Results_raw': {'train_loss': 13.455559, 'val_loss': 13.016475, 'test_loss': 12.720272}}
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2024-11-13 22:54:50,787 (server:615) INFO: {'Role': 'Server #', 'Round': 45, 'Results_weighted_avg': {'test_loss': np.float64(58921.094772), 'test_avg_loss': np.float64(17.370606), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60549.695859), 'val_avg_loss': np.float64(17.850736), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58921.094772), 'test_avg_loss': np.float64(17.370606), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60549.695859), 'val_avg_loss': np.float64(17.850736), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8105.696942), 'test_loss_bottom_decile': np.float64(49571.727844), 'test_loss_top_decile': np.float64(66465.468933), 'test_loss_min': np.float64(38759.88205), 'test_loss_max': np.float64(66465.468933), 'test_loss_bottom10%': np.float64(38759.88205), 'test_loss_top10%': np.float64(66465.468933), 'test_loss_cos1': np.float64(0.99067), 'test_loss_entropy': np.float64(2.292311), 'test_avg_loss_std': np.float64(2.389651), 'test_avg_loss_bottom_decile': np.float64(14.614307), 'test_avg_loss_top_decile': np.float64(19.594773), 'test_avg_loss_min': np.float64(11.426852), 'test_avg_loss_max': np.float64(19.594773), 'test_avg_loss_bottom10%': np.float64(11.426852), 'test_avg_loss_top10%': np.float64(19.594773), 'test_avg_loss_cos1': np.float64(0.99067), 'test_avg_loss_entropy': np.float64(2.292311), 'val_loss_std': np.float64(8310.645262), 'val_loss_bottom_decile': np.float64(51567.698425), 'val_loss_top_decile': np.float64(68465.118835), 'val_loss_min': np.float64(39547.241913), 'val_loss_max': np.float64(68465.118835), 'val_loss_bottom10%': np.float64(39547.241913), 'val_loss_top10%': np.float64(68465.118835), 'val_loss_cos1': np.float64(0.990712), 'val_loss_entropy': np.float64(2.292337), 'val_avg_loss_std': np.float64(2.450072), 'val_avg_loss_bottom_decile': np.float64(15.202741), 'val_avg_loss_top_decile': np.float64(20.184292), 'val_avg_loss_min': np.float64(11.658975), 'val_avg_loss_max': np.float64(20.184292), 'val_avg_loss_bottom10%': np.float64(11.658975), 'val_avg_loss_top10%': np.float64(20.184292), 'val_avg_loss_cos1': np.float64(0.990712), 'val_avg_loss_entropy': np.float64(2.292337)}}
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2024-11-13 22:54:50,825 (server:353) INFO: Server: Starting evaluation at the end of round 46.
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2024-11-13 22:54:50,826 (server:359) INFO: ----------- Starting a new training round (Round #47) -------------
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2024-11-13 22:56:37,784 (client:354) INFO: {'Role': 'Client #7', 'Round': 47, 'Results_raw': {'train_loss': 9.635528, 'val_loss': 8.679127, 'test_loss': 9.008348}}
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2024-11-13 22:57:10,798 (client:354) INFO: {'Role': 'Client #5', 'Round': 47, 'Results_raw': {'train_loss': 12.230825, 'val_loss': 11.579248, 'test_loss': 12.366194}}
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2024-11-13 22:57:43,407 (client:354) INFO: {'Role': 'Client #3', 'Round': 47, 'Results_raw': {'train_loss': 13.401699, 'val_loss': 12.48412, 'test_loss': 12.585293}}
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2024-11-13 22:58:16,225 (client:354) INFO: {'Role': 'Client #8', 'Round': 47, 'Results_raw': {'train_loss': 14.313428, 'val_loss': 13.591665, 'test_loss': 13.159068}}
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2024-11-13 22:58:48,120 (client:354) INFO: {'Role': 'Client #2', 'Round': 47, 'Results_raw': {'train_loss': 14.147748, 'val_loss': 12.960724, 'test_loss': 13.401333}}
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2024-11-13 22:59:23,123 (client:354) INFO: {'Role': 'Client #4', 'Round': 47, 'Results_raw': {'train_loss': 14.8979, 'val_loss': 13.382905, 'test_loss': 13.792281}}
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2024-11-13 23:00:00,080 (client:354) INFO: {'Role': 'Client #1', 'Round': 47, 'Results_raw': {'train_loss': 14.602879, 'val_loss': 14.655548, 'test_loss': 14.160019}}
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2024-11-13 23:00:36,635 (client:354) INFO: {'Role': 'Client #10', 'Round': 47, 'Results_raw': {'train_loss': 15.281604, 'val_loss': 14.195244, 'test_loss': 14.538546}}
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2024-11-13 23:01:13,469 (client:354) INFO: {'Role': 'Client #9', 'Round': 47, 'Results_raw': {'train_loss': 13.382261, 'val_loss': 13.168728, 'test_loss': 12.808567}}
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2024-11-13 23:01:49,597 (client:354) INFO: {'Role': 'Client #6', 'Round': 47, 'Results_raw': {'train_loss': 7.822783, 'val_loss': 7.096263, 'test_loss': 7.279344}}
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2024-11-13 23:01:49,600 (server:615) INFO: {'Role': 'Server #', 'Round': 46, 'Results_weighted_avg': {'test_loss': np.float64(59500.631772), 'test_avg_loss': np.float64(17.54146), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61170.416864), 'val_avg_loss': np.float64(18.033731), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59500.631772), 'test_avg_loss': np.float64(17.54146), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61170.416864), 'val_avg_loss': np.float64(18.033731), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8238.545035), 'test_loss_bottom_decile': np.float64(50624.04129), 'test_loss_top_decile': np.float64(67079.251831), 'test_loss_min': np.float64(38679.332214), 'test_loss_max': np.float64(67079.251831), 'test_loss_bottom10%': np.float64(38679.332214), 'test_loss_top10%': np.float64(67079.251831), 'test_loss_cos1': np.float64(0.99055), 'test_loss_entropy': np.float64(2.292139), 'test_avg_loss_std': np.float64(2.428816), 'test_avg_loss_bottom_decile': np.float64(14.92454), 'test_avg_loss_top_decile': np.float64(19.775723), 'test_avg_loss_min': np.float64(11.403105), 'test_avg_loss_max': np.float64(19.775723), 'test_avg_loss_bottom10%': np.float64(11.403105), 'test_avg_loss_top10%': np.float64(19.775723), 'test_avg_loss_cos1': np.float64(0.99055), 'test_avg_loss_entropy': np.float64(2.292139), 'val_loss_std': np.float64(8434.638211), 'val_loss_bottom_decile': np.float64(52716.040833), 'val_loss_top_decile': np.float64(69182.353821), 'val_loss_min': np.float64(39504.251953), 'val_loss_max': np.float64(69182.353821), 'val_loss_bottom10%': np.float64(39504.251953), 'val_loss_top10%': np.float64(69182.353821), 'val_loss_cos1': np.float64(0.990627), 'val_loss_entropy': np.float64(2.292201), 'val_avg_loss_std': np.float64(2.486627), 'val_avg_loss_bottom_decile': np.float64(15.541286), 'val_avg_loss_top_decile': np.float64(20.395741), 'val_avg_loss_min': np.float64(11.646301), 'val_avg_loss_max': np.float64(20.395741), 'val_avg_loss_bottom10%': np.float64(11.646301), 'val_avg_loss_top10%': np.float64(20.395741), 'val_avg_loss_cos1': np.float64(0.990627), 'val_avg_loss_entropy': np.float64(2.292201)}}
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2024-11-13 23:01:49,629 (server:353) INFO: Server: Starting evaluation at the end of round 47.
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2024-11-13 23:01:49,629 (server:359) INFO: ----------- Starting a new training round (Round #48) -------------
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2024-11-13 23:03:27,483 (client:354) INFO: {'Role': 'Client #7', 'Round': 48, 'Results_raw': {'train_loss': 9.658, 'val_loss': 8.643478, 'test_loss': 9.006775}}
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2024-11-13 23:04:05,219 (client:354) INFO: {'Role': 'Client #4', 'Round': 48, 'Results_raw': {'train_loss': 14.898015, 'val_loss': 13.354954, 'test_loss': 13.770419}}
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2024-11-13 23:04:44,059 (client:354) INFO: {'Role': 'Client #8', 'Round': 48, 'Results_raw': {'train_loss': 14.292314, 'val_loss': 13.56148, 'test_loss': 13.043712}}
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2024-11-13 23:05:20,563 (client:354) INFO: {'Role': 'Client #5', 'Round': 48, 'Results_raw': {'train_loss': 12.211004, 'val_loss': 11.652204, 'test_loss': 12.331073}}
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2024-11-13 23:05:56,850 (client:354) INFO: {'Role': 'Client #6', 'Round': 48, 'Results_raw': {'train_loss': 7.801532, 'val_loss': 7.152266, 'test_loss': 7.325111}}
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2024-11-13 23:06:32,403 (client:354) INFO: {'Role': 'Client #9', 'Round': 48, 'Results_raw': {'train_loss': 13.386154, 'val_loss': 13.130328, 'test_loss': 12.881644}}
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2024-11-13 23:07:06,794 (client:354) INFO: {'Role': 'Client #2', 'Round': 48, 'Results_raw': {'train_loss': 14.088799, 'val_loss': 13.146056, 'test_loss': 13.539921}}
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2024-11-13 23:07:42,169 (client:354) INFO: {'Role': 'Client #10', 'Round': 48, 'Results_raw': {'train_loss': 15.271388, 'val_loss': 14.095773, 'test_loss': 14.390708}}
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2024-11-13 23:08:19,410 (client:354) INFO: {'Role': 'Client #3', 'Round': 48, 'Results_raw': {'train_loss': 13.416384, 'val_loss': 12.395692, 'test_loss': 12.502435}}
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2024-11-13 23:08:59,581 (client:354) INFO: {'Role': 'Client #1', 'Round': 48, 'Results_raw': {'train_loss': 14.620959, 'val_loss': 14.59212, 'test_loss': 14.121471}}
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2024-11-13 23:08:59,586 (server:615) INFO: {'Role': 'Server #', 'Round': 47, 'Results_weighted_avg': {'test_loss': np.float64(59164.509525), 'test_avg_loss': np.float64(17.442367), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60781.808844), 'val_avg_loss': np.float64(17.919165), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59164.509525), 'test_avg_loss': np.float64(17.442367), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60781.808844), 'val_avg_loss': np.float64(17.919165), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8147.401475), 'test_loss_bottom_decile': np.float64(50244.258575), 'test_loss_top_decile': np.float64(66416.693787), 'test_loss_min': np.float64(38606.623901), 'test_loss_max': np.float64(66416.693787), 'test_loss_bottom10%': np.float64(38606.623901), 'test_loss_top10%': np.float64(66416.693787), 'test_loss_cos1': np.float64(0.990651), 'test_loss_entropy': np.float64(2.292258), 'test_avg_loss_std': np.float64(2.401946), 'test_avg_loss_bottom_decile': np.float64(14.812576), 'test_avg_loss_top_decile': np.float64(19.580393), 'test_avg_loss_min': np.float64(11.38167), 'test_avg_loss_max': np.float64(19.580393), 'test_avg_loss_bottom10%': np.float64(11.38167), 'test_avg_loss_top10%': np.float64(19.580393), 'test_avg_loss_cos1': np.float64(0.990651), 'test_avg_loss_entropy': np.float64(2.292258), 'val_loss_std': np.float64(8316.942573), 'val_loss_bottom_decile': np.float64(52178.857056), 'val_loss_top_decile': np.float64(68349.51239), 'val_loss_min': np.float64(39466.124268), 'val_loss_max': np.float64(68349.51239), 'val_loss_bottom10%': np.float64(39466.124268), 'val_loss_top10%': np.float64(68349.51239), 'val_loss_cos1': np.float64(0.990768), 'val_loss_entropy': np.float64(2.292366), 'val_avg_loss_std': np.float64(2.451929), 'val_avg_loss_bottom_decile': np.float64(15.382918), 'val_avg_loss_top_decile': np.float64(20.15021), 'val_avg_loss_min': np.float64(11.63506), 'val_avg_loss_max': np.float64(20.15021), 'val_avg_loss_bottom10%': np.float64(11.63506), 'val_avg_loss_top10%': np.float64(20.15021), 'val_avg_loss_cos1': np.float64(0.990768), 'val_avg_loss_entropy': np.float64(2.292366)}}
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2024-11-13 23:08:59,629 (server:353) INFO: Server: Starting evaluation at the end of round 48.
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2024-11-13 23:08:59,629 (server:359) INFO: ----------- Starting a new training round (Round #49) -------------
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2024-11-13 23:10:42,691 (client:354) INFO: {'Role': 'Client #10', 'Round': 49, 'Results_raw': {'train_loss': 15.206857, 'val_loss': 14.237971, 'test_loss': 14.613342}}
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2024-11-13 23:11:23,313 (client:354) INFO: {'Role': 'Client #3', 'Round': 49, 'Results_raw': {'train_loss': 13.372984, 'val_loss': 12.35784, 'test_loss': 12.557101}}
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2024-11-13 23:12:04,202 (client:354) INFO: {'Role': 'Client #2', 'Round': 49, 'Results_raw': {'train_loss': 14.023422, 'val_loss': 12.938035, 'test_loss': 13.292125}}
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2024-11-13 23:12:44,581 (client:354) INFO: {'Role': 'Client #6', 'Round': 49, 'Results_raw': {'train_loss': 7.800663, 'val_loss': 7.059562, 'test_loss': 7.272735}}
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2024-11-13 23:13:21,599 (client:354) INFO: {'Role': 'Client #7', 'Round': 49, 'Results_raw': {'train_loss': 9.697899, 'val_loss': 8.554001, 'test_loss': 8.90055}}
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2024-11-13 23:13:59,185 (client:354) INFO: {'Role': 'Client #4', 'Round': 49, 'Results_raw': {'train_loss': 14.801535, 'val_loss': 13.304777, 'test_loss': 13.778065}}
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2024-11-13 23:14:39,258 (client:354) INFO: {'Role': 'Client #5', 'Round': 49, 'Results_raw': {'train_loss': 12.16781, 'val_loss': 11.702086, 'test_loss': 12.383698}}
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2024-11-13 23:15:16,664 (client:354) INFO: {'Role': 'Client #9', 'Round': 49, 'Results_raw': {'train_loss': 13.327488, 'val_loss': 12.9731, 'test_loss': 12.768403}}
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2024-11-13 23:15:53,710 (client:354) INFO: {'Role': 'Client #8', 'Round': 49, 'Results_raw': {'train_loss': 14.260348, 'val_loss': 13.46515, 'test_loss': 12.981409}}
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2024-11-13 23:16:30,950 (client:354) INFO: {'Role': 'Client #1', 'Round': 49, 'Results_raw': {'train_loss': 14.572635, 'val_loss': 14.709122, 'test_loss': 14.166615}}
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2024-11-13 23:16:30,956 (server:615) INFO: {'Role': 'Server #', 'Round': 48, 'Results_weighted_avg': {'test_loss': np.float64(59312.99361), 'test_avg_loss': np.float64(17.486142), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60930.499088), 'val_avg_loss': np.float64(17.963001), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59312.99361), 'test_avg_loss': np.float64(17.486142), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60930.499088), 'val_avg_loss': np.float64(17.963001), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8084.369333), 'test_loss_bottom_decile': np.float64(50366.409302), 'test_loss_top_decile': np.float64(66801.604248), 'test_loss_min': np.float64(38973.499695), 'test_loss_max': np.float64(66801.604248), 'test_loss_bottom10%': np.float64(38973.499695), 'test_loss_top10%': np.float64(66801.604248), 'test_loss_cos1': np.float64(0.990839), 'test_loss_entropy': np.float64(2.292486), 'test_avg_loss_std': np.float64(2.383364), 'test_avg_loss_bottom_decile': np.float64(14.848588), 'test_avg_loss_top_decile': np.float64(19.693869), 'test_avg_loss_min': np.float64(11.489829), 'test_avg_loss_max': np.float64(19.693869), 'test_avg_loss_bottom10%': np.float64(11.489829), 'test_avg_loss_top10%': np.float64(19.693869), 'test_avg_loss_cos1': np.float64(0.990839), 'test_avg_loss_entropy': np.float64(2.292486), 'val_loss_std': np.float64(8279.76134), 'val_loss_bottom_decile': np.float64(52421.549072), 'val_loss_top_decile': np.float64(68902.869446), 'val_loss_min': np.float64(39815.499908), 'val_loss_max': np.float64(68902.869446), 'val_loss_bottom10%': np.float64(39815.499908), 'val_loss_top10%': np.float64(68902.869446), 'val_loss_cos1': np.float64(0.990893), 'val_loss_entropy': np.float64(2.292535), 'val_avg_loss_std': np.float64(2.440967), 'val_avg_loss_bottom_decile': np.float64(15.454466), 'val_avg_loss_top_decile': np.float64(20.313346), 'val_avg_loss_min': np.float64(11.73806), 'val_avg_loss_max': np.float64(20.313346), 'val_avg_loss_bottom10%': np.float64(11.73806), 'val_avg_loss_top10%': np.float64(20.313346), 'val_avg_loss_cos1': np.float64(0.990893), 'val_avg_loss_entropy': np.float64(2.292535)}}
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2024-11-13 23:16:31,002 (server:353) INFO: Server: Starting evaluation at the end of round 49.
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2024-11-13 23:16:31,003 (server:359) INFO: ----------- Starting a new training round (Round #50) -------------
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2024-11-13 23:18:24,149 (client:354) INFO: {'Role': 'Client #8', 'Round': 50, 'Results_raw': {'train_loss': 14.239043, 'val_loss': 13.628263, 'test_loss': 13.123455}}
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2024-11-13 23:19:01,388 (client:354) INFO: {'Role': 'Client #1', 'Round': 50, 'Results_raw': {'train_loss': 14.592264, 'val_loss': 14.523195, 'test_loss': 14.072515}}
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2024-11-13 23:19:42,928 (client:354) INFO: {'Role': 'Client #9', 'Round': 50, 'Results_raw': {'train_loss': 13.34743, 'val_loss': 13.119902, 'test_loss': 12.794001}}
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2024-11-13 23:20:21,411 (client:354) INFO: {'Role': 'Client #10', 'Round': 50, 'Results_raw': {'train_loss': 15.270005, 'val_loss': 14.285355, 'test_loss': 14.762651}}
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2024-11-13 23:20:57,943 (client:354) INFO: {'Role': 'Client #3', 'Round': 50, 'Results_raw': {'train_loss': 13.347286, 'val_loss': 12.494255, 'test_loss': 12.575755}}
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2024-11-13 23:21:34,720 (client:354) INFO: {'Role': 'Client #5', 'Round': 50, 'Results_raw': {'train_loss': 12.163995, 'val_loss': 11.60083, 'test_loss': 12.405803}}
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2024-11-13 23:22:10,852 (client:354) INFO: {'Role': 'Client #7', 'Round': 50, 'Results_raw': {'train_loss': 9.687392, 'val_loss': 8.825971, 'test_loss': 9.189779}}
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2024-11-13 23:22:50,479 (client:354) INFO: {'Role': 'Client #2', 'Round': 50, 'Results_raw': {'train_loss': 14.062247, 'val_loss': 13.007276, 'test_loss': 13.409268}}
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2024-11-13 23:23:27,707 (client:354) INFO: {'Role': 'Client #6', 'Round': 50, 'Results_raw': {'train_loss': 7.772817, 'val_loss': 7.093218, 'test_loss': 7.281832}}
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2024-11-13 23:24:04,122 (client:354) INFO: {'Role': 'Client #4', 'Round': 50, 'Results_raw': {'train_loss': 14.819243, 'val_loss': 13.384504, 'test_loss': 13.871281}}
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2024-11-13 23:24:04,133 (server:615) INFO: {'Role': 'Server #', 'Round': 49, 'Results_weighted_avg': {'test_loss': np.float64(59453.357315), 'test_avg_loss': np.float64(17.527523), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61073.004453), 'val_avg_loss': np.float64(18.005013), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59453.357315), 'test_avg_loss': np.float64(17.527523), 'test_total': np.float64(3392.0), 'val_loss': np.float64(61073.004453), 'val_avg_loss': np.float64(18.005013), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8244.052485), 'test_loss_bottom_decile': np.float64(50471.511444), 'test_loss_top_decile': np.float64(67343.515747), 'test_loss_min': np.float64(38718.140869), 'test_loss_max': np.float64(67343.515747), 'test_loss_bottom10%': np.float64(38718.140869), 'test_loss_top10%': np.float64(67343.515747), 'test_loss_cos1': np.float64(0.990523), 'test_loss_entropy': np.float64(2.292121), 'test_avg_loss_std': np.float64(2.43044), 'test_avg_loss_bottom_decile': np.float64(14.879573), 'test_avg_loss_top_decile': np.float64(19.853631), 'test_avg_loss_min': np.float64(11.414546), 'test_avg_loss_max': np.float64(19.853631), 'test_avg_loss_bottom10%': np.float64(11.414546), 'test_avg_loss_top10%': np.float64(19.853631), 'test_avg_loss_cos1': np.float64(0.990523), 'test_avg_loss_entropy': np.float64(2.292121), 'val_loss_std': np.float64(8420.178531), 'val_loss_bottom_decile': np.float64(52557.698303), 'val_loss_top_decile': np.float64(69166.020752), 'val_loss_min': np.float64(39568.418671), 'val_loss_max': np.float64(69166.020752), 'val_loss_bottom10%': np.float64(39568.418671), 'val_loss_top10%': np.float64(69166.020752), 'val_loss_cos1': np.float64(0.990629), 'val_loss_entropy': np.float64(2.292223), 'val_avg_loss_std': np.float64(2.482364), 'val_avg_loss_bottom_decile': np.float64(15.494604), 'val_avg_loss_top_decile': np.float64(20.390926), 'val_avg_loss_min': np.float64(11.665218), 'val_avg_loss_max': np.float64(20.390926), 'val_avg_loss_bottom10%': np.float64(11.665218), 'val_avg_loss_top10%': np.float64(20.390926), 'val_avg_loss_cos1': np.float64(0.990629), 'val_avg_loss_entropy': np.float64(2.292223)}}
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2024-11-13 23:24:04,161 (server:353) INFO: Server: Starting evaluation at the end of round 50.
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2024-11-13 23:24:04,161 (server:359) INFO: ----------- Starting a new training round (Round #51) -------------
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2024-11-13 23:25:41,229 (client:354) INFO: {'Role': 'Client #8', 'Round': 51, 'Results_raw': {'train_loss': 14.275268, 'val_loss': 13.55915, 'test_loss': 12.958554}}
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2024-11-13 23:26:16,647 (client:354) INFO: {'Role': 'Client #1', 'Round': 51, 'Results_raw': {'train_loss': 14.574894, 'val_loss': 14.582815, 'test_loss': 14.090238}}
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2024-11-13 23:26:55,418 (client:354) INFO: {'Role': 'Client #4', 'Round': 51, 'Results_raw': {'train_loss': 14.840018, 'val_loss': 13.260186, 'test_loss': 13.776315}}
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2024-11-13 23:27:35,449 (client:354) INFO: {'Role': 'Client #10', 'Round': 51, 'Results_raw': {'train_loss': 15.180341, 'val_loss': 14.27612, 'test_loss': 14.575744}}
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2024-11-13 23:28:15,353 (client:354) INFO: {'Role': 'Client #3', 'Round': 51, 'Results_raw': {'train_loss': 13.343119, 'val_loss': 12.521286, 'test_loss': 12.762585}}
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2024-11-13 23:28:55,198 (client:354) INFO: {'Role': 'Client #2', 'Round': 51, 'Results_raw': {'train_loss': 14.015433, 'val_loss': 13.149857, 'test_loss': 13.516814}}
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2024-11-13 23:29:35,212 (client:354) INFO: {'Role': 'Client #7', 'Round': 51, 'Results_raw': {'train_loss': 9.625311, 'val_loss': 8.601683, 'test_loss': 8.922863}}
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2024-11-13 23:30:11,186 (client:354) INFO: {'Role': 'Client #5', 'Round': 51, 'Results_raw': {'train_loss': 12.176958, 'val_loss': 11.696974, 'test_loss': 12.382456}}
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2024-11-13 23:30:46,843 (client:354) INFO: {'Role': 'Client #9', 'Round': 51, 'Results_raw': {'train_loss': 13.309783, 'val_loss': 13.043992, 'test_loss': 12.760364}}
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2024-11-13 23:31:24,474 (client:354) INFO: {'Role': 'Client #6', 'Round': 51, 'Results_raw': {'train_loss': 7.741878, 'val_loss': 7.067935, 'test_loss': 7.270565}}
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2024-11-13 23:31:24,479 (server:615) INFO: {'Role': 'Server #', 'Round': 50, 'Results_weighted_avg': {'test_loss': np.float64(59248.800125), 'test_avg_loss': np.float64(17.467217), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60872.701791), 'val_avg_loss': np.float64(17.945962), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59248.800125), 'test_avg_loss': np.float64(17.467217), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60872.701791), 'val_avg_loss': np.float64(17.945962), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8185.712356), 'test_loss_bottom_decile': np.float64(50182.18634), 'test_loss_top_decile': np.float64(66894.581787), 'test_loss_min': np.float64(38745.404755), 'test_loss_max': np.float64(66894.581787), 'test_loss_bottom10%': np.float64(38745.404755), 'test_loss_top10%': np.float64(66894.581787), 'test_loss_cos1': np.float64(0.990591), 'test_loss_entropy': np.float64(2.292209), 'test_avg_loss_std': np.float64(2.413241), 'test_avg_loss_bottom_decile': np.float64(14.794277), 'test_avg_loss_top_decile': np.float64(19.72128), 'test_avg_loss_min': np.float64(11.422584), 'test_avg_loss_max': np.float64(19.72128), 'test_avg_loss_bottom10%': np.float64(11.422584), 'test_avg_loss_top10%': np.float64(19.72128), 'test_avg_loss_cos1': np.float64(0.990591), 'test_avg_loss_entropy': np.float64(2.292209), 'val_loss_std': np.float64(8377.370133), 'val_loss_bottom_decile': np.float64(52190.650818), 'val_loss_top_decile': np.float64(69003.551453), 'val_loss_min': np.float64(39587.239227), 'val_loss_max': np.float64(69003.551453), 'val_loss_bottom10%': np.float64(39587.239227), 'val_loss_top10%': np.float64(69003.551453), 'val_loss_cos1': np.float64(0.990663), 'val_loss_entropy': np.float64(2.292274), 'val_avg_loss_std': np.float64(2.469744), 'val_avg_loss_bottom_decile': np.float64(15.386395), 'val_avg_loss_top_decile': np.float64(20.343028), 'val_avg_loss_min': np.float64(11.670766), 'val_avg_loss_max': np.float64(20.343028), 'val_avg_loss_bottom10%': np.float64(11.670766), 'val_avg_loss_top10%': np.float64(20.343028), 'val_avg_loss_cos1': np.float64(0.990663), 'val_avg_loss_entropy': np.float64(2.292274)}}
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2024-11-13 23:31:24,521 (server:353) INFO: Server: Starting evaluation at the end of round 51.
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2024-11-13 23:31:24,522 (server:359) INFO: ----------- Starting a new training round (Round #52) -------------
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2024-11-13 23:33:03,049 (client:354) INFO: {'Role': 'Client #10', 'Round': 52, 'Results_raw': {'train_loss': 15.241435, 'val_loss': 14.145562, 'test_loss': 14.498935}}
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2024-11-13 23:33:39,373 (client:354) INFO: {'Role': 'Client #1', 'Round': 52, 'Results_raw': {'train_loss': 14.597923, 'val_loss': 14.569133, 'test_loss': 14.105507}}
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2024-11-13 23:34:18,346 (client:354) INFO: {'Role': 'Client #5', 'Round': 52, 'Results_raw': {'train_loss': 12.178834, 'val_loss': 11.573282, 'test_loss': 12.387329}}
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2024-11-13 23:34:55,751 (client:354) INFO: {'Role': 'Client #4', 'Round': 52, 'Results_raw': {'train_loss': 14.811359, 'val_loss': 13.264679, 'test_loss': 13.80886}}
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2024-11-13 23:35:32,156 (client:354) INFO: {'Role': 'Client #7', 'Round': 52, 'Results_raw': {'train_loss': 9.582466, 'val_loss': 8.781407, 'test_loss': 9.285641}}
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2024-11-13 23:36:09,183 (client:354) INFO: {'Role': 'Client #9', 'Round': 52, 'Results_raw': {'train_loss': 13.331017, 'val_loss': 13.248001, 'test_loss': 12.838803}}
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2024-11-13 23:36:45,055 (client:354) INFO: {'Role': 'Client #3', 'Round': 52, 'Results_raw': {'train_loss': 13.312333, 'val_loss': 12.442929, 'test_loss': 12.617744}}
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2024-11-13 23:37:20,679 (client:354) INFO: {'Role': 'Client #6', 'Round': 52, 'Results_raw': {'train_loss': 7.711538, 'val_loss': 7.076393, 'test_loss': 7.25611}}
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2024-11-13 23:37:56,127 (client:354) INFO: {'Role': 'Client #2', 'Round': 52, 'Results_raw': {'train_loss': 14.044343, 'val_loss': 13.023359, 'test_loss': 13.464575}}
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2024-11-13 23:38:31,207 (client:354) INFO: {'Role': 'Client #8', 'Round': 52, 'Results_raw': {'train_loss': 14.189664, 'val_loss': 13.460618, 'test_loss': 12.970223}}
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2024-11-13 23:38:31,210 (server:615) INFO: {'Role': 'Server #', 'Round': 51, 'Results_weighted_avg': {'test_loss': np.float64(58592.768777), 'test_avg_loss': np.float64(17.273812), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60153.089996), 'val_avg_loss': np.float64(17.733812), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58592.768777), 'test_avg_loss': np.float64(17.273812), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60153.089996), 'val_avg_loss': np.float64(17.733812), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7953.186258), 'test_loss_bottom_decile': np.float64(49659.503387), 'test_loss_top_decile': np.float64(66120.874023), 'test_loss_min': np.float64(38740.52948), 'test_loss_max': np.float64(66120.874023), 'test_loss_bottom10%': np.float64(38740.52948), 'test_loss_top10%': np.float64(66120.874023), 'test_loss_cos1': np.float64(0.990913), 'test_loss_entropy': np.float64(2.292593), 'test_avg_loss_std': np.float64(2.344689), 'test_avg_loss_bottom_decile': np.float64(14.640184), 'test_avg_loss_top_decile': np.float64(19.493182), 'test_avg_loss_min': np.float64(11.421147), 'test_avg_loss_max': np.float64(19.493182), 'test_avg_loss_bottom10%': np.float64(11.421147), 'test_avg_loss_top10%': np.float64(19.493182), 'test_avg_loss_cos1': np.float64(0.990913), 'test_avg_loss_entropy': np.float64(2.292593), 'val_loss_std': np.float64(8125.421503), 'val_loss_bottom_decile': np.float64(51570.195862), 'val_loss_top_decile': np.float64(67947.82959), 'val_loss_min': np.float64(39560.697937), 'val_loss_max': np.float64(67947.82959), 'val_loss_bottom10%': np.float64(39560.697937), 'val_loss_top10%': np.float64(67947.82959), 'val_loss_cos1': np.float64(0.991), 'val_loss_entropy': np.float64(2.292672), 'val_avg_loss_std': np.float64(2.395466), 'val_avg_loss_bottom_decile': np.float64(15.203478), 'val_avg_loss_top_decile': np.float64(20.031789), 'val_avg_loss_min': np.float64(11.662942), 'val_avg_loss_max': np.float64(20.031789), 'val_avg_loss_bottom10%': np.float64(11.662942), 'val_avg_loss_top10%': np.float64(20.031789), 'val_avg_loss_cos1': np.float64(0.991), 'val_avg_loss_entropy': np.float64(2.292672)}}
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2024-11-13 23:38:31,248 (server:353) INFO: Server: Starting evaluation at the end of round 52.
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2024-11-13 23:38:31,248 (server:359) INFO: ----------- Starting a new training round (Round #53) -------------
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2024-11-13 23:40:10,000 (client:354) INFO: {'Role': 'Client #9', 'Round': 53, 'Results_raw': {'train_loss': 13.324494, 'val_loss': 13.169283, 'test_loss': 12.860412}}
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2024-11-13 23:40:44,117 (client:354) INFO: {'Role': 'Client #1', 'Round': 53, 'Results_raw': {'train_loss': 14.526809, 'val_loss': 14.449679, 'test_loss': 13.980809}}
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2024-11-13 23:41:18,300 (client:354) INFO: {'Role': 'Client #2', 'Round': 53, 'Results_raw': {'train_loss': 14.056295, 'val_loss': 13.108073, 'test_loss': 13.467649}}
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2024-11-13 23:41:52,426 (client:354) INFO: {'Role': 'Client #6', 'Round': 53, 'Results_raw': {'train_loss': 7.717681, 'val_loss': 7.053588, 'test_loss': 7.249666}}
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2024-11-13 23:42:27,515 (client:354) INFO: {'Role': 'Client #3', 'Round': 53, 'Results_raw': {'train_loss': 13.264392, 'val_loss': 12.432035, 'test_loss': 12.568719}}
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2024-11-13 23:43:03,212 (client:354) INFO: {'Role': 'Client #4', 'Round': 53, 'Results_raw': {'train_loss': 14.768994, 'val_loss': 13.502255, 'test_loss': 14.024147}}
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2024-11-13 23:43:38,644 (client:354) INFO: {'Role': 'Client #10', 'Round': 53, 'Results_raw': {'train_loss': 15.224138, 'val_loss': 14.136322, 'test_loss': 14.450042}}
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2024-11-13 23:44:15,289 (client:354) INFO: {'Role': 'Client #8', 'Round': 53, 'Results_raw': {'train_loss': 14.249549, 'val_loss': 13.374106, 'test_loss': 12.908976}}
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2024-11-13 23:44:53,634 (client:354) INFO: {'Role': 'Client #7', 'Round': 53, 'Results_raw': {'train_loss': 9.443772, 'val_loss': 8.518538, 'test_loss': 8.854336}}
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2024-11-13 23:45:30,297 (client:354) INFO: {'Role': 'Client #5', 'Round': 53, 'Results_raw': {'train_loss': 12.13849, 'val_loss': 11.667047, 'test_loss': 12.417182}}
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2024-11-13 23:45:30,299 (server:615) INFO: {'Role': 'Server #', 'Round': 52, 'Results_weighted_avg': {'test_loss': np.float64(58877.443936), 'test_avg_loss': np.float64(17.357737), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60450.704373), 'val_avg_loss': np.float64(17.821552), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58877.443936), 'test_avg_loss': np.float64(17.357737), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60450.704373), 'val_avg_loss': np.float64(17.821552), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8080.467293), 'test_loss_bottom_decile': np.float64(50178.350861), 'test_loss_top_decile': np.float64(66416.90332), 'test_loss_min': np.float64(38505.656067), 'test_loss_max': np.float64(66416.90332), 'test_loss_bottom10%': np.float64(38505.656067), 'test_loss_top10%': np.float64(66416.90332), 'test_loss_cos1': np.float64(0.990713), 'test_loss_entropy': np.float64(2.292339), 'test_avg_loss_std': np.float64(2.382213), 'test_avg_loss_bottom_decile': np.float64(14.793146), 'test_avg_loss_top_decile': np.float64(19.580455), 'test_avg_loss_min': np.float64(11.351903), 'test_avg_loss_max': np.float64(19.580455), 'test_avg_loss_bottom10%': np.float64(11.351903), 'test_avg_loss_top10%': np.float64(19.580455), 'test_avg_loss_cos1': np.float64(0.990713), 'test_avg_loss_entropy': np.float64(2.292339), 'val_loss_std': np.float64(8241.122172), 'val_loss_bottom_decile': np.float64(52233.040222), 'val_loss_top_decile': np.float64(68400.670532), 'val_loss_min': np.float64(39362.476349), 'val_loss_max': np.float64(68400.670532), 'val_loss_bottom10%': np.float64(39362.476349), 'val_loss_top10%': np.float64(68400.670532), 'val_loss_cos1': np.float64(0.990835), 'val_loss_entropy': np.float64(2.292462), 'val_avg_loss_std': np.float64(2.429576), 'val_avg_loss_bottom_decile': np.float64(15.398892), 'val_avg_loss_top_decile': np.float64(20.165292), 'val_avg_loss_min': np.float64(11.604504), 'val_avg_loss_max': np.float64(20.165292), 'val_avg_loss_bottom10%': np.float64(11.604504), 'val_avg_loss_top10%': np.float64(20.165292), 'val_avg_loss_cos1': np.float64(0.990835), 'val_avg_loss_entropy': np.float64(2.292462)}}
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2024-11-13 23:45:30,329 (server:353) INFO: Server: Starting evaluation at the end of round 53.
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2024-11-13 23:45:30,330 (server:359) INFO: ----------- Starting a new training round (Round #54) -------------
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2024-11-13 23:47:10,985 (client:354) INFO: {'Role': 'Client #5', 'Round': 54, 'Results_raw': {'train_loss': 12.155974, 'val_loss': 11.592881, 'test_loss': 12.411226}}
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2024-11-13 23:47:47,602 (client:354) INFO: {'Role': 'Client #4', 'Round': 54, 'Results_raw': {'train_loss': 14.690854, 'val_loss': 13.325909, 'test_loss': 13.79745}}
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2024-11-13 23:48:25,641 (client:354) INFO: {'Role': 'Client #2', 'Round': 54, 'Results_raw': {'train_loss': 13.963652, 'val_loss': 13.114956, 'test_loss': 13.549727}}
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2024-11-13 23:49:05,330 (client:354) INFO: {'Role': 'Client #9', 'Round': 54, 'Results_raw': {'train_loss': 13.214793, 'val_loss': 13.125294, 'test_loss': 12.768697}}
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2024-11-13 23:49:50,133 (client:354) INFO: {'Role': 'Client #7', 'Round': 54, 'Results_raw': {'train_loss': 9.52218, 'val_loss': 8.573817, 'test_loss': 9.014894}}
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2024-11-13 23:50:29,504 (client:354) INFO: {'Role': 'Client #10', 'Round': 54, 'Results_raw': {'train_loss': 15.158941, 'val_loss': 14.281025, 'test_loss': 14.643463}}
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2024-11-13 23:51:06,894 (client:354) INFO: {'Role': 'Client #3', 'Round': 54, 'Results_raw': {'train_loss': 13.308342, 'val_loss': 12.416978, 'test_loss': 12.627371}}
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2024-11-13 23:51:43,230 (client:354) INFO: {'Role': 'Client #8', 'Round': 54, 'Results_raw': {'train_loss': 14.260619, 'val_loss': 13.567006, 'test_loss': 13.087986}}
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2024-11-13 23:52:19,643 (client:354) INFO: {'Role': 'Client #1', 'Round': 54, 'Results_raw': {'train_loss': 14.493436, 'val_loss': 14.642879, 'test_loss': 14.147005}}
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2024-11-13 23:52:56,231 (client:354) INFO: {'Role': 'Client #6', 'Round': 54, 'Results_raw': {'train_loss': 7.731129, 'val_loss': 7.119191, 'test_loss': 7.27708}}
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2024-11-13 23:52:56,234 (server:615) INFO: {'Role': 'Server #', 'Round': 53, 'Results_weighted_avg': {'test_loss': np.float64(58926.918304), 'test_avg_loss': np.float64(17.372323), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60525.852042), 'val_avg_loss': np.float64(17.843706), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58926.918304), 'test_avg_loss': np.float64(17.372323), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60525.852042), 'val_avg_loss': np.float64(17.843706), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8063.647858), 'test_loss_bottom_decile': np.float64(50258.869812), 'test_loss_top_decile': np.float64(66367.912048), 'test_loss_min': np.float64(38540.348999), 'test_loss_max': np.float64(66367.912048), 'test_loss_bottom10%': np.float64(38540.348999), 'test_loss_top10%': np.float64(66367.912048), 'test_loss_cos1': np.float64(0.990767), 'test_loss_entropy': np.float64(2.292394), 'test_avg_loss_std': np.float64(2.377255), 'test_avg_loss_bottom_decile': np.float64(14.816884), 'test_avg_loss_top_decile': np.float64(19.566012), 'test_avg_loss_min': np.float64(11.362131), 'test_avg_loss_max': np.float64(19.566012), 'test_avg_loss_bottom10%': np.float64(11.362131), 'test_avg_loss_top10%': np.float64(19.566012), 'test_avg_loss_cos1': np.float64(0.990767), 'test_avg_loss_entropy': np.float64(2.292394), 'val_loss_std': np.float64(8265.648081), 'val_loss_bottom_decile': np.float64(52248.285461), 'val_loss_top_decile': np.float64(68609.081665), 'val_loss_min': np.float64(39356.970184), 'val_loss_max': np.float64(68609.081665), 'val_loss_bottom10%': np.float64(39356.970184), 'val_loss_top10%': np.float64(68609.081665), 'val_loss_cos1': np.float64(0.990804), 'val_loss_entropy': np.float64(2.292422), 'val_avg_loss_std': np.float64(2.436807), 'val_avg_loss_bottom_decile': np.float64(15.403386), 'val_avg_loss_top_decile': np.float64(20.226734), 'val_avg_loss_min': np.float64(11.60288), 'val_avg_loss_max': np.float64(20.226734), 'val_avg_loss_bottom10%': np.float64(11.60288), 'val_avg_loss_top10%': np.float64(20.226734), 'val_avg_loss_cos1': np.float64(0.990804), 'val_avg_loss_entropy': np.float64(2.292422)}}
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2024-11-13 23:52:56,274 (server:353) INFO: Server: Starting evaluation at the end of round 54.
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2024-11-13 23:52:56,274 (server:359) INFO: ----------- Starting a new training round (Round #55) -------------
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2024-11-13 23:54:41,500 (client:354) INFO: {'Role': 'Client #5', 'Round': 55, 'Results_raw': {'train_loss': 12.117656, 'val_loss': 11.591063, 'test_loss': 12.306792}}
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2024-11-13 23:55:18,357 (client:354) INFO: {'Role': 'Client #2', 'Round': 55, 'Results_raw': {'train_loss': 13.967297, 'val_loss': 12.990694, 'test_loss': 13.444183}}
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2024-11-13 23:55:52,268 (client:354) INFO: {'Role': 'Client #3', 'Round': 55, 'Results_raw': {'train_loss': 13.28303, 'val_loss': 12.446355, 'test_loss': 12.650199}}
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2024-11-13 23:56:27,535 (client:354) INFO: {'Role': 'Client #7', 'Round': 55, 'Results_raw': {'train_loss': 9.571711, 'val_loss': 8.60217, 'test_loss': 8.871606}}
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2024-11-13 23:57:03,606 (client:354) INFO: {'Role': 'Client #6', 'Round': 55, 'Results_raw': {'train_loss': 7.723433, 'val_loss': 7.140285, 'test_loss': 7.342596}}
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2024-11-13 23:57:38,334 (client:354) INFO: {'Role': 'Client #9', 'Round': 55, 'Results_raw': {'train_loss': 13.225847, 'val_loss': 13.08607, 'test_loss': 12.799038}}
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2024-11-13 23:58:12,889 (client:354) INFO: {'Role': 'Client #1', 'Round': 55, 'Results_raw': {'train_loss': 14.487111, 'val_loss': 14.623756, 'test_loss': 14.163495}}
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2024-11-13 23:58:49,318 (client:354) INFO: {'Role': 'Client #10', 'Round': 55, 'Results_raw': {'train_loss': 15.147771, 'val_loss': 14.212909, 'test_loss': 14.57914}}
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2024-11-13 23:59:27,619 (client:354) INFO: {'Role': 'Client #4', 'Round': 55, 'Results_raw': {'train_loss': 14.789584, 'val_loss': 13.228901, 'test_loss': 13.761031}}
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2024-11-14 00:00:06,061 (client:354) INFO: {'Role': 'Client #8', 'Round': 55, 'Results_raw': {'train_loss': 14.163485, 'val_loss': 13.529384, 'test_loss': 12.929231}}
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2024-11-14 00:00:06,065 (server:615) INFO: {'Role': 'Server #', 'Round': 54, 'Results_weighted_avg': {'test_loss': np.float64(59077.093735), 'test_avg_loss': np.float64(17.416596), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60633.603464), 'val_avg_loss': np.float64(17.875473), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59077.093735), 'test_avg_loss': np.float64(17.416596), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60633.603464), 'val_avg_loss': np.float64(17.875473), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8129.186446), 'test_loss_bottom_decile': np.float64(50530.900177), 'test_loss_top_decile': np.float64(66607.904663), 'test_loss_min': np.float64(38505.318726), 'test_loss_max': np.float64(66607.904663), 'test_loss_bottom10%': np.float64(38505.318726), 'test_loss_top10%': np.float64(66607.904663), 'test_loss_cos1': np.float64(0.990665), 'test_loss_entropy': np.float64(2.292277), 'test_avg_loss_std': np.float64(2.396576), 'test_avg_loss_bottom_decile': np.float64(14.897081), 'test_avg_loss_top_decile': np.float64(19.636764), 'test_avg_loss_min': np.float64(11.351804), 'test_avg_loss_max': np.float64(19.636764), 'test_avg_loss_bottom10%': np.float64(11.351804), 'test_avg_loss_top10%': np.float64(19.636764), 'test_avg_loss_cos1': np.float64(0.990665), 'test_avg_loss_entropy': np.float64(2.292277), 'val_loss_std': np.float64(8299.76826), 'val_loss_bottom_decile': np.float64(52495.83252), 'val_loss_top_decile': np.float64(68540.138855), 'val_loss_min': np.float64(39279.535248), 'val_loss_max': np.float64(68540.138855), 'val_loss_bottom10%': np.float64(39279.535248), 'val_loss_top10%': np.float64(68540.138855), 'val_loss_cos1': np.float64(0.990761), 'val_loss_entropy': np.float64(2.292359), 'val_avg_loss_std': np.float64(2.446866), 'val_avg_loss_bottom_decile': np.float64(15.476366), 'val_avg_loss_top_decile': np.float64(20.206409), 'val_avg_loss_min': np.float64(11.580052), 'val_avg_loss_max': np.float64(20.206409), 'val_avg_loss_bottom10%': np.float64(11.580052), 'val_avg_loss_top10%': np.float64(20.206409), 'val_avg_loss_cos1': np.float64(0.990761), 'val_avg_loss_entropy': np.float64(2.292359)}}
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2024-11-14 00:00:06,103 (server:353) INFO: Server: Starting evaluation at the end of round 55.
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2024-11-14 00:00:06,103 (server:359) INFO: ----------- Starting a new training round (Round #56) -------------
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2024-11-14 00:01:39,460 (client:354) INFO: {'Role': 'Client #5', 'Round': 56, 'Results_raw': {'train_loss': 12.085789, 'val_loss': 11.646404, 'test_loss': 12.384177}}
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2024-11-14 00:02:16,279 (client:354) INFO: {'Role': 'Client #10', 'Round': 56, 'Results_raw': {'train_loss': 15.132827, 'val_loss': 14.18874, 'test_loss': 14.484543}}
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2024-11-14 00:02:53,396 (client:354) INFO: {'Role': 'Client #7', 'Round': 56, 'Results_raw': {'train_loss': 9.528265, 'val_loss': 8.754572, 'test_loss': 9.123099}}
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2024-11-14 00:03:26,941 (client:354) INFO: {'Role': 'Client #8', 'Round': 56, 'Results_raw': {'train_loss': 14.222447, 'val_loss': 13.395451, 'test_loss': 12.91537}}
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2024-11-14 00:03:59,573 (client:354) INFO: {'Role': 'Client #9', 'Round': 56, 'Results_raw': {'train_loss': 13.200678, 'val_loss': 13.180976, 'test_loss': 12.849347}}
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2024-11-14 00:04:31,935 (client:354) INFO: {'Role': 'Client #4', 'Round': 56, 'Results_raw': {'train_loss': 14.765414, 'val_loss': 13.503442, 'test_loss': 14.063248}}
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2024-11-14 00:05:05,843 (client:354) INFO: {'Role': 'Client #3', 'Round': 56, 'Results_raw': {'train_loss': 13.245885, 'val_loss': 12.336312, 'test_loss': 12.635857}}
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2024-11-14 00:05:43,024 (client:354) INFO: {'Role': 'Client #1', 'Round': 56, 'Results_raw': {'train_loss': 14.538362, 'val_loss': 14.879827, 'test_loss': 14.48011}}
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2024-11-14 00:06:18,378 (client:354) INFO: {'Role': 'Client #2', 'Round': 56, 'Results_raw': {'train_loss': 13.925676, 'val_loss': 13.074116, 'test_loss': 13.514548}}
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2024-11-14 00:06:51,647 (client:354) INFO: {'Role': 'Client #6', 'Round': 56, 'Results_raw': {'train_loss': 7.730579, 'val_loss': 7.019712, 'test_loss': 7.224657}}
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2024-11-14 00:06:51,650 (server:615) INFO: {'Role': 'Server #', 'Round': 55, 'Results_weighted_avg': {'test_loss': np.float64(59116.568939), 'test_avg_loss': np.float64(17.428234), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60703.321835), 'val_avg_loss': np.float64(17.896026), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59116.568939), 'test_avg_loss': np.float64(17.428234), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60703.321835), 'val_avg_loss': np.float64(17.896026), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8201.882129), 'test_loss_bottom_decile': np.float64(50456.580261), 'test_loss_top_decile': np.float64(66934.795471), 'test_loss_min': np.float64(38419.630371), 'test_loss_max': np.float64(66934.795471), 'test_loss_bottom10%': np.float64(38419.630371), 'test_loss_top10%': np.float64(66934.795471), 'test_loss_cos1': np.float64(0.990512), 'test_loss_entropy': np.float64(2.292106), 'test_avg_loss_std': np.float64(2.418008), 'test_avg_loss_bottom_decile': np.float64(14.875171), 'test_avg_loss_top_decile': np.float64(19.733135), 'test_avg_loss_min': np.float64(11.326542), 'test_avg_loss_max': np.float64(19.733135), 'test_avg_loss_bottom10%': np.float64(11.326542), 'test_avg_loss_top10%': np.float64(19.733135), 'test_avg_loss_cos1': np.float64(0.990512), 'test_avg_loss_entropy': np.float64(2.292106), 'val_loss_std': np.float64(8404.37651), 'val_loss_bottom_decile': np.float64(52426.617493), 'val_loss_top_decile': np.float64(68958.991638), 'val_loss_min': np.float64(39233.889313), 'val_loss_max': np.float64(68958.991638), 'val_loss_bottom10%': np.float64(39233.889313), 'val_loss_top10%': np.float64(68958.991638), 'val_loss_cos1': np.float64(0.990551), 'val_loss_entropy': np.float64(2.292138), 'val_avg_loss_std': np.float64(2.477705), 'val_avg_loss_bottom_decile': np.float64(15.45596), 'val_avg_loss_top_decile': np.float64(20.329891), 'val_avg_loss_min': np.float64(11.566595), 'val_avg_loss_max': np.float64(20.329891), 'val_avg_loss_bottom10%': np.float64(11.566595), 'val_avg_loss_top10%': np.float64(20.329891), 'val_avg_loss_cos1': np.float64(0.990551), 'val_avg_loss_entropy': np.float64(2.292138)}}
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2024-11-14 00:06:51,685 (server:353) INFO: Server: Starting evaluation at the end of round 56.
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2024-11-14 00:06:51,685 (server:359) INFO: ----------- Starting a new training round (Round #57) -------------
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2024-11-14 00:08:28,117 (client:354) INFO: {'Role': 'Client #2', 'Round': 57, 'Results_raw': {'train_loss': 13.949194, 'val_loss': 12.918997, 'test_loss': 13.313588}}
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2024-11-14 00:09:04,224 (client:354) INFO: {'Role': 'Client #10', 'Round': 57, 'Results_raw': {'train_loss': 15.123056, 'val_loss': 14.192681, 'test_loss': 14.550903}}
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2024-11-14 00:09:40,775 (client:354) INFO: {'Role': 'Client #5', 'Round': 57, 'Results_raw': {'train_loss': 12.076012, 'val_loss': 11.54406, 'test_loss': 12.249093}}
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2024-11-14 00:10:16,190 (client:354) INFO: {'Role': 'Client #7', 'Round': 57, 'Results_raw': {'train_loss': 9.517912, 'val_loss': 8.737136, 'test_loss': 8.997768}}
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2024-11-14 00:10:50,409 (client:354) INFO: {'Role': 'Client #4', 'Round': 57, 'Results_raw': {'train_loss': 14.683934, 'val_loss': 13.430124, 'test_loss': 13.864503}}
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2024-11-14 00:11:26,994 (client:354) INFO: {'Role': 'Client #6', 'Round': 57, 'Results_raw': {'train_loss': 7.729417, 'val_loss': 7.06732, 'test_loss': 7.299464}}
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2024-11-14 00:12:01,211 (client:354) INFO: {'Role': 'Client #8', 'Round': 57, 'Results_raw': {'train_loss': 14.183917, 'val_loss': 13.575759, 'test_loss': 13.057933}}
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2024-11-14 00:12:34,125 (client:354) INFO: {'Role': 'Client #1', 'Round': 57, 'Results_raw': {'train_loss': 14.488532, 'val_loss': 14.592683, 'test_loss': 14.102107}}
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2024-11-14 00:13:07,827 (client:354) INFO: {'Role': 'Client #9', 'Round': 57, 'Results_raw': {'train_loss': 13.266863, 'val_loss': 13.076555, 'test_loss': 12.823029}}
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2024-11-14 00:13:41,504 (client:354) INFO: {'Role': 'Client #3', 'Round': 57, 'Results_raw': {'train_loss': 13.247196, 'val_loss': 12.43088, 'test_loss': 12.671575}}
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2024-11-14 00:13:41,507 (server:615) INFO: {'Role': 'Server #', 'Round': 56, 'Results_weighted_avg': {'test_loss': np.float64(58544.780496), 'test_avg_loss': np.float64(17.259664), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60067.721506), 'val_avg_loss': np.float64(17.708644), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58544.780496), 'test_avg_loss': np.float64(17.259664), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60067.721506), 'val_avg_loss': np.float64(17.708644), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7969.485283), 'test_loss_bottom_decile': np.float64(50334.660156), 'test_loss_top_decile': np.float64(66259.079834), 'test_loss_min': np.float64(38290.535065), 'test_loss_max': np.float64(66259.079834), 'test_loss_bottom10%': np.float64(38290.535065), 'test_loss_top10%': np.float64(66259.079834), 'test_loss_cos1': np.float64(0.990862), 'test_loss_entropy': np.float64(2.292501), 'test_avg_loss_std': np.float64(2.349494), 'test_avg_loss_bottom_decile': np.float64(14.839228), 'test_avg_loss_top_decile': np.float64(19.533927), 'test_avg_loss_min': np.float64(11.288483), 'test_avg_loss_max': np.float64(19.533927), 'test_avg_loss_bottom10%': np.float64(11.288483), 'test_avg_loss_top10%': np.float64(19.533927), 'test_avg_loss_cos1': np.float64(0.990862), 'test_avg_loss_entropy': np.float64(2.292501), 'val_loss_std': np.float64(8155.811216), 'val_loss_bottom_decile': np.float64(52315.769653), 'val_loss_top_decile': np.float64(68086.736389), 'val_loss_min': np.float64(39094.795258), 'val_loss_max': np.float64(68086.736389), 'val_loss_bottom10%': np.float64(39094.795258), 'val_loss_top10%': np.float64(68086.736389), 'val_loss_cos1': np.float64(0.990908), 'val_loss_entropy': np.float64(2.292543), 'val_avg_loss_std': np.float64(2.404425), 'val_avg_loss_bottom_decile': np.float64(15.423281), 'val_avg_loss_top_decile': np.float64(20.072741), 'val_avg_loss_min': np.float64(11.525588), 'val_avg_loss_max': np.float64(20.072741), 'val_avg_loss_bottom10%': np.float64(11.525588), 'val_avg_loss_top10%': np.float64(20.072741), 'val_avg_loss_cos1': np.float64(0.990908), 'val_avg_loss_entropy': np.float64(2.292543)}}
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2024-11-14 00:13:41,536 (server:353) INFO: Server: Starting evaluation at the end of round 57.
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2024-11-14 00:13:41,536 (server:359) INFO: ----------- Starting a new training round (Round #58) -------------
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2024-11-14 00:15:16,031 (client:354) INFO: {'Role': 'Client #6', 'Round': 58, 'Results_raw': {'train_loss': 7.656979, 'val_loss': 7.006713, 'test_loss': 7.231033}}
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2024-11-14 00:15:50,809 (client:354) INFO: {'Role': 'Client #8', 'Round': 58, 'Results_raw': {'train_loss': 14.150947, 'val_loss': 13.381136, 'test_loss': 12.898661}}
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2024-11-14 00:16:25,494 (client:354) INFO: {'Role': 'Client #10', 'Round': 58, 'Results_raw': {'train_loss': 15.127191, 'val_loss': 14.15169, 'test_loss': 14.566766}}
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2024-11-14 00:16:59,267 (client:354) INFO: {'Role': 'Client #9', 'Round': 58, 'Results_raw': {'train_loss': 13.340882, 'val_loss': 13.054952, 'test_loss': 12.742187}}
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2024-11-14 00:17:33,061 (client:354) INFO: {'Role': 'Client #4', 'Round': 58, 'Results_raw': {'train_loss': 14.673272, 'val_loss': 13.44721, 'test_loss': 13.888201}}
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2024-11-14 00:18:06,695 (client:354) INFO: {'Role': 'Client #2', 'Round': 58, 'Results_raw': {'train_loss': 13.992154, 'val_loss': 13.060248, 'test_loss': 13.504669}}
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2024-11-14 00:18:40,817 (client:354) INFO: {'Role': 'Client #3', 'Round': 58, 'Results_raw': {'train_loss': 13.214726, 'val_loss': 12.339243, 'test_loss': 12.582982}}
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2024-11-14 00:19:15,145 (client:354) INFO: {'Role': 'Client #5', 'Round': 58, 'Results_raw': {'train_loss': 12.037038, 'val_loss': 11.707559, 'test_loss': 12.354686}}
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2024-11-14 00:19:49,415 (client:354) INFO: {'Role': 'Client #7', 'Round': 58, 'Results_raw': {'train_loss': 9.417994, 'val_loss': 8.730593, 'test_loss': 9.080043}}
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2024-11-14 00:20:25,536 (client:354) INFO: {'Role': 'Client #1', 'Round': 58, 'Results_raw': {'train_loss': 14.489922, 'val_loss': 14.573518, 'test_loss': 14.070801}}
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2024-11-14 00:20:25,540 (server:615) INFO: {'Role': 'Server #', 'Round': 57, 'Results_weighted_avg': {'test_loss': np.float64(58478.617844), 'test_avg_loss': np.float64(17.240159), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59993.510818), 'val_avg_loss': np.float64(17.686766), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58478.617844), 'test_avg_loss': np.float64(17.240159), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59993.510818), 'val_avg_loss': np.float64(17.686766), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7972.436658), 'test_loss_bottom_decile': np.float64(49951.493195), 'test_loss_top_decile': np.float64(66009.741394), 'test_loss_min': np.float64(38448.938171), 'test_loss_max': np.float64(66009.741394), 'test_loss_bottom10%': np.float64(38448.938171), 'test_loss_top10%': np.float64(66009.741394), 'test_loss_cos1': np.float64(0.990835), 'test_loss_entropy': np.float64(2.292495), 'test_avg_loss_std': np.float64(2.350365), 'test_avg_loss_bottom_decile': np.float64(14.726266), 'test_avg_loss_top_decile': np.float64(19.460419), 'test_avg_loss_min': np.float64(11.335182), 'test_avg_loss_max': np.float64(19.460419), 'test_avg_loss_bottom10%': np.float64(11.335182), 'test_avg_loss_top10%': np.float64(19.460419), 'test_avg_loss_cos1': np.float64(0.990835), 'test_avg_loss_entropy': np.float64(2.292495), 'val_loss_std': np.float64(8179.49457), 'val_loss_bottom_decile': np.float64(51834.427307), 'val_loss_top_decile': np.float64(68207.228638), 'val_loss_min': np.float64(39209.620331), 'val_loss_max': np.float64(68207.228638), 'val_loss_bottom10%': np.float64(39209.620331), 'val_loss_top10%': np.float64(68207.228638), 'val_loss_cos1': np.float64(0.990833), 'val_loss_entropy': np.float64(2.292485), 'val_avg_loss_std': np.float64(2.411408), 'val_avg_loss_bottom_decile': np.float64(15.281376), 'val_avg_loss_top_decile': np.float64(20.108263), 'val_avg_loss_min': np.float64(11.55944), 'val_avg_loss_max': np.float64(20.108263), 'val_avg_loss_bottom10%': np.float64(11.55944), 'val_avg_loss_top10%': np.float64(20.108263), 'val_avg_loss_cos1': np.float64(0.990833), 'val_avg_loss_entropy': np.float64(2.292485)}}
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2024-11-14 00:20:25,586 (server:353) INFO: Server: Starting evaluation at the end of round 58.
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2024-11-14 00:20:25,587 (server:359) INFO: ----------- Starting a new training round (Round #59) -------------
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2024-11-14 00:22:00,348 (client:354) INFO: {'Role': 'Client #1', 'Round': 59, 'Results_raw': {'train_loss': 14.414216, 'val_loss': 14.427766, 'test_loss': 14.022095}}
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2024-11-14 00:22:34,560 (client:354) INFO: {'Role': 'Client #10', 'Round': 59, 'Results_raw': {'train_loss': 15.096613, 'val_loss': 14.097086, 'test_loss': 14.478604}}
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2024-11-14 00:23:08,721 (client:354) INFO: {'Role': 'Client #5', 'Round': 59, 'Results_raw': {'train_loss': 12.077815, 'val_loss': 11.649158, 'test_loss': 12.431637}}
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2024-11-14 00:23:42,835 (client:354) INFO: {'Role': 'Client #3', 'Round': 59, 'Results_raw': {'train_loss': 13.223442, 'val_loss': 12.288522, 'test_loss': 12.477348}}
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2024-11-14 00:24:16,239 (client:354) INFO: {'Role': 'Client #8', 'Round': 59, 'Results_raw': {'train_loss': 14.115898, 'val_loss': 13.413441, 'test_loss': 12.872448}}
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2024-11-14 00:24:51,773 (client:354) INFO: {'Role': 'Client #2', 'Round': 59, 'Results_raw': {'train_loss': 13.848725, 'val_loss': 12.922479, 'test_loss': 13.305559}}
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2024-11-14 00:25:26,049 (client:354) INFO: {'Role': 'Client #6', 'Round': 59, 'Results_raw': {'train_loss': 7.64885, 'val_loss': 6.977226, 'test_loss': 7.188721}}
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2024-11-14 00:26:00,870 (client:354) INFO: {'Role': 'Client #7', 'Round': 59, 'Results_raw': {'train_loss': 9.549106, 'val_loss': 8.806889, 'test_loss': 9.135496}}
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2024-11-14 00:26:36,854 (client:354) INFO: {'Role': 'Client #9', 'Round': 59, 'Results_raw': {'train_loss': 13.232577, 'val_loss': 13.028786, 'test_loss': 12.770693}}
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2024-11-14 00:27:13,505 (client:354) INFO: {'Role': 'Client #4', 'Round': 59, 'Results_raw': {'train_loss': 14.753256, 'val_loss': 13.339401, 'test_loss': 13.783155}}
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2024-11-14 00:27:13,507 (server:615) INFO: {'Role': 'Server #', 'Round': 58, 'Results_weighted_avg': {'test_loss': np.float64(58804.101184), 'test_avg_loss': np.float64(17.336115), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60333.990237), 'val_avg_loss': np.float64(17.787143), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58804.101184), 'test_avg_loss': np.float64(17.336115), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60333.990237), 'val_avg_loss': np.float64(17.787143), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8185.815415), 'test_loss_bottom_decile': np.float64(50044.153717), 'test_loss_top_decile': np.float64(66509.608704), 'test_loss_min': np.float64(38185.770538), 'test_loss_max': np.float64(66509.608704), 'test_loss_bottom10%': np.float64(38185.770538), 'test_loss_top10%': np.float64(66509.608704), 'test_loss_cos1': np.float64(0.99045), 'test_loss_entropy': np.float64(2.292035), 'test_avg_loss_std': np.float64(2.413271), 'test_avg_loss_bottom_decile': np.float64(14.753583), 'test_avg_loss_top_decile': np.float64(19.607786), 'test_avg_loss_min': np.float64(11.257597), 'test_avg_loss_max': np.float64(19.607786), 'test_avg_loss_bottom10%': np.float64(11.257597), 'test_avg_loss_top10%': np.float64(19.607786), 'test_avg_loss_cos1': np.float64(0.99045), 'test_avg_loss_entropy': np.float64(2.292035), 'val_loss_std': np.float64(8389.916542), 'val_loss_bottom_decile': np.float64(51976.928833), 'val_loss_top_decile': np.float64(68788.108154), 'val_loss_min': np.float64(38939.886932), 'val_loss_max': np.float64(68788.108154), 'val_loss_bottom10%': np.float64(38939.886932), 'val_loss_top10%': np.float64(68788.108154), 'val_loss_cos1': np.float64(0.990469), 'val_loss_entropy': np.float64(2.292045), 'val_avg_loss_std': np.float64(2.473442), 'val_avg_loss_bottom_decile': np.float64(15.323387), 'val_avg_loss_top_decile': np.float64(20.279513), 'val_avg_loss_min': np.float64(11.479919), 'val_avg_loss_max': np.float64(20.279513), 'val_avg_loss_bottom10%': np.float64(11.479919), 'val_avg_loss_top10%': np.float64(20.279513), 'val_avg_loss_cos1': np.float64(0.990469), 'val_avg_loss_entropy': np.float64(2.292045)}}
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2024-11-14 00:27:13,541 (server:353) INFO: Server: Starting evaluation at the end of round 59.
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2024-11-14 00:27:13,542 (server:359) INFO: ----------- Starting a new training round (Round #60) -------------
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2024-11-14 00:28:53,089 (client:354) INFO: {'Role': 'Client #4', 'Round': 60, 'Results_raw': {'train_loss': 14.703241, 'val_loss': 13.357119, 'test_loss': 13.899714}}
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2024-11-14 00:29:34,401 (client:354) INFO: {'Role': 'Client #10', 'Round': 60, 'Results_raw': {'train_loss': 15.069909, 'val_loss': 14.260033, 'test_loss': 14.592762}}
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2024-11-14 00:30:11,676 (client:354) INFO: {'Role': 'Client #1', 'Round': 60, 'Results_raw': {'train_loss': 14.458511, 'val_loss': 14.561209, 'test_loss': 14.162518}}
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2024-11-14 00:30:48,252 (client:354) INFO: {'Role': 'Client #9', 'Round': 60, 'Results_raw': {'train_loss': 13.165275, 'val_loss': 13.001023, 'test_loss': 12.676063}}
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2024-11-14 00:31:24,666 (client:354) INFO: {'Role': 'Client #5', 'Round': 60, 'Results_raw': {'train_loss': 12.085912, 'val_loss': 11.601706, 'test_loss': 12.363457}}
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2024-11-14 00:32:00,943 (client:354) INFO: {'Role': 'Client #3', 'Round': 60, 'Results_raw': {'train_loss': 13.172839, 'val_loss': 12.401289, 'test_loss': 12.615739}}
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2024-11-14 00:32:37,005 (client:354) INFO: {'Role': 'Client #7', 'Round': 60, 'Results_raw': {'train_loss': 9.535797, 'val_loss': 8.472879, 'test_loss': 8.954935}}
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2024-11-14 00:33:14,587 (client:354) INFO: {'Role': 'Client #2', 'Round': 60, 'Results_raw': {'train_loss': 13.871439, 'val_loss': 12.969731, 'test_loss': 13.379848}}
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2024-11-14 00:33:53,138 (client:354) INFO: {'Role': 'Client #6', 'Round': 60, 'Results_raw': {'train_loss': 7.641514, 'val_loss': 7.09005, 'test_loss': 7.324419}}
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2024-11-14 00:34:32,244 (client:354) INFO: {'Role': 'Client #8', 'Round': 60, 'Results_raw': {'train_loss': 14.142754, 'val_loss': 13.410617, 'test_loss': 12.852285}}
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2024-11-14 00:34:32,247 (server:615) INFO: {'Role': 'Server #', 'Round': 59, 'Results_weighted_avg': {'test_loss': np.float64(58084.680231), 'test_avg_loss': np.float64(17.124021), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59572.37648), 'val_avg_loss': np.float64(17.562611), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58084.680231), 'test_avg_loss': np.float64(17.124021), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59572.37648), 'val_avg_loss': np.float64(17.562611), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7991.368931), 'test_loss_bottom_decile': np.float64(49576.793152), 'test_loss_top_decile': np.float64(65565.795776), 'test_loss_min': np.float64(37942.740173), 'test_loss_max': np.float64(65565.795776), 'test_loss_bottom10%': np.float64(37942.740173), 'test_loss_top10%': np.float64(65565.795776), 'test_loss_cos1': np.float64(0.990668), 'test_loss_entropy': np.float64(2.29229), 'test_avg_loss_std': np.float64(2.355946), 'test_avg_loss_bottom_decile': np.float64(14.6158), 'test_avg_loss_top_decile': np.float64(19.329539), 'test_avg_loss_min': np.float64(11.185949), 'test_avg_loss_max': np.float64(19.329539), 'test_avg_loss_bottom10%': np.float64(11.185949), 'test_avg_loss_top10%': np.float64(19.329539), 'test_avg_loss_cos1': np.float64(0.990668), 'test_avg_loss_entropy': np.float64(2.29229), 'val_loss_std': np.float64(8187.036675), 'val_loss_bottom_decile': np.float64(51467.618225), 'val_loss_top_decile': np.float64(67733.660583), 'val_loss_min': np.float64(38701.160431), 'val_loss_max': np.float64(67733.660583), 'val_loss_bottom10%': np.float64(38701.160431), 'val_loss_top10%': np.float64(67733.660583), 'val_loss_cos1': np.float64(0.990688), 'val_loss_entropy': np.float64(2.292305), 'val_avg_loss_std': np.float64(2.413631), 'val_avg_loss_bottom_decile': np.float64(15.173237), 'val_avg_loss_top_decile': np.float64(19.96865), 'val_avg_loss_min': np.float64(11.40954), 'val_avg_loss_max': np.float64(19.96865), 'val_avg_loss_bottom10%': np.float64(11.40954), 'val_avg_loss_top10%': np.float64(19.96865), 'val_avg_loss_cos1': np.float64(0.990688), 'val_avg_loss_entropy': np.float64(2.292305)}}
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2024-11-14 00:34:32,282 (server:353) INFO: Server: Starting evaluation at the end of round 60.
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2024-11-14 00:34:32,282 (server:359) INFO: ----------- Starting a new training round (Round #61) -------------
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2024-11-14 00:36:11,987 (client:354) INFO: {'Role': 'Client #6', 'Round': 61, 'Results_raw': {'train_loss': 7.651199, 'val_loss': 7.092458, 'test_loss': 7.297843}}
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2024-11-14 00:36:48,320 (client:354) INFO: {'Role': 'Client #2', 'Round': 61, 'Results_raw': {'train_loss': 13.859151, 'val_loss': 12.897125, 'test_loss': 13.273495}}
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2024-11-14 00:37:25,050 (client:354) INFO: {'Role': 'Client #8', 'Round': 61, 'Results_raw': {'train_loss': 14.138566, 'val_loss': 13.363831, 'test_loss': 12.874448}}
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2024-11-14 00:38:01,301 (client:354) INFO: {'Role': 'Client #4', 'Round': 61, 'Results_raw': {'train_loss': 14.650553, 'val_loss': 13.231596, 'test_loss': 13.73311}}
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2024-11-14 00:38:42,024 (client:354) INFO: {'Role': 'Client #5', 'Round': 61, 'Results_raw': {'train_loss': 12.023346, 'val_loss': 11.474247, 'test_loss': 12.295065}}
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2024-11-14 00:39:18,412 (client:354) INFO: {'Role': 'Client #7', 'Round': 61, 'Results_raw': {'train_loss': 9.563751, 'val_loss': 8.510671, 'test_loss': 8.897961}}
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2024-11-14 00:39:55,111 (client:354) INFO: {'Role': 'Client #9', 'Round': 61, 'Results_raw': {'train_loss': 13.168927, 'val_loss': 13.212397, 'test_loss': 12.898373}}
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2024-11-14 00:40:31,445 (client:354) INFO: {'Role': 'Client #10', 'Round': 61, 'Results_raw': {'train_loss': 15.009927, 'val_loss': 14.119186, 'test_loss': 14.515046}}
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2024-11-14 00:41:08,255 (client:354) INFO: {'Role': 'Client #1', 'Round': 61, 'Results_raw': {'train_loss': 14.388211, 'val_loss': 14.552538, 'test_loss': 14.037254}}
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2024-11-14 00:41:44,866 (client:354) INFO: {'Role': 'Client #3', 'Round': 61, 'Results_raw': {'train_loss': 13.186899, 'val_loss': 12.366211, 'test_loss': 12.576461}}
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2024-11-14 00:41:44,870 (server:615) INFO: {'Role': 'Server #', 'Round': 60, 'Results_weighted_avg': {'test_loss': np.float64(58228.247281), 'test_avg_loss': np.float64(17.166346), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59712.185733), 'val_avg_loss': np.float64(17.603828), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58228.247281), 'test_avg_loss': np.float64(17.166346), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59712.185733), 'val_avg_loss': np.float64(17.603828), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7956.587779), 'test_loss_bottom_decile': np.float64(49797.201416), 'test_loss_top_decile': np.float64(65441.766663), 'test_loss_min': np.float64(38057.46048), 'test_loss_max': np.float64(65441.766663), 'test_loss_bottom10%': np.float64(38057.46048), 'test_loss_top10%': np.float64(65441.766663), 'test_loss_cos1': np.float64(0.990793), 'test_loss_entropy': np.float64(2.292419), 'test_avg_loss_std': np.float64(2.345692), 'test_avg_loss_bottom_decile': np.float64(14.680779), 'test_avg_loss_top_decile': np.float64(19.292974), 'test_avg_loss_min': np.float64(11.21977), 'test_avg_loss_max': np.float64(19.292974), 'test_avg_loss_bottom10%': np.float64(11.21977), 'test_avg_loss_top10%': np.float64(19.292974), 'test_avg_loss_cos1': np.float64(0.990793), 'test_avg_loss_entropy': np.float64(2.292419), 'val_loss_std': np.float64(8147.726422), 'val_loss_bottom_decile': np.float64(51621.756531), 'val_loss_top_decile': np.float64(67804.022888), 'val_loss_min': np.float64(38802.621918), 'val_loss_max': np.float64(67804.022888), 'val_loss_bottom10%': np.float64(38802.621918), 'val_loss_top10%': np.float64(67804.022888), 'val_loss_cos1': np.float64(0.990819), 'val_loss_entropy': np.float64(2.292434), 'val_avg_loss_std': np.float64(2.402042), 'val_avg_loss_bottom_decile': np.float64(15.218678), 'val_avg_loss_top_decile': np.float64(19.989394), 'val_avg_loss_min': np.float64(11.439452), 'val_avg_loss_max': np.float64(19.989394), 'val_avg_loss_bottom10%': np.float64(11.439452), 'val_avg_loss_top10%': np.float64(19.989394), 'val_avg_loss_cos1': np.float64(0.990819), 'val_avg_loss_entropy': np.float64(2.292434)}}
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2024-11-14 00:41:44,904 (server:353) INFO: Server: Starting evaluation at the end of round 61.
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2024-11-14 00:41:44,905 (server:359) INFO: ----------- Starting a new training round (Round #62) -------------
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2024-11-14 00:43:30,489 (client:354) INFO: {'Role': 'Client #4', 'Round': 62, 'Results_raw': {'train_loss': 14.621163, 'val_loss': 13.477081, 'test_loss': 14.049641}}
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2024-11-14 00:44:07,915 (client:354) INFO: {'Role': 'Client #8', 'Round': 62, 'Results_raw': {'train_loss': 14.054294, 'val_loss': 13.500382, 'test_loss': 13.024114}}
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2024-11-14 00:44:44,794 (client:354) INFO: {'Role': 'Client #3', 'Round': 62, 'Results_raw': {'train_loss': 13.221077, 'val_loss': 12.329845, 'test_loss': 12.544505}}
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2024-11-14 00:45:21,447 (client:354) INFO: {'Role': 'Client #10', 'Round': 62, 'Results_raw': {'train_loss': 15.092189, 'val_loss': 14.15618, 'test_loss': 14.503501}}
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2024-11-14 00:45:58,155 (client:354) INFO: {'Role': 'Client #2', 'Round': 62, 'Results_raw': {'train_loss': 13.892913, 'val_loss': 12.855959, 'test_loss': 13.338097}}
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2024-11-14 00:46:34,922 (client:354) INFO: {'Role': 'Client #6', 'Round': 62, 'Results_raw': {'train_loss': 7.620609, 'val_loss': 7.020648, 'test_loss': 7.243803}}
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2024-11-14 00:47:13,694 (client:354) INFO: {'Role': 'Client #9', 'Round': 62, 'Results_raw': {'train_loss': 13.217639, 'val_loss': 13.14785, 'test_loss': 12.798224}}
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2024-11-14 00:47:56,125 (client:354) INFO: {'Role': 'Client #5', 'Round': 62, 'Results_raw': {'train_loss': 12.006613, 'val_loss': 11.487816, 'test_loss': 12.263435}}
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2024-11-14 00:48:34,701 (client:354) INFO: {'Role': 'Client #7', 'Round': 62, 'Results_raw': {'train_loss': 9.43395, 'val_loss': 8.657526, 'test_loss': 9.037252}}
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2024-11-14 00:49:13,296 (client:354) INFO: {'Role': 'Client #1', 'Round': 62, 'Results_raw': {'train_loss': 14.428695, 'val_loss': 14.539686, 'test_loss': 14.050217}}
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2024-11-14 00:49:13,299 (server:615) INFO: {'Role': 'Server #', 'Round': 61, 'Results_weighted_avg': {'test_loss': np.float64(58688.92225), 'test_avg_loss': np.float64(17.302159), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60203.276654), 'val_avg_loss': np.float64(17.748608), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58688.92225), 'test_avg_loss': np.float64(17.302159), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60203.276654), 'val_avg_loss': np.float64(17.748608), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8106.334838), 'test_loss_bottom_decile': np.float64(50566.154053), 'test_loss_top_decile': np.float64(65961.703369), 'test_loss_min': np.float64(37903.807709), 'test_loss_max': np.float64(65961.703369), 'test_loss_bottom10%': np.float64(37903.807709), 'test_loss_top10%': np.float64(65961.703369), 'test_loss_cos1': np.float64(0.990595), 'test_loss_entropy': np.float64(2.292161), 'test_avg_loss_std': np.float64(2.389839), 'test_avg_loss_bottom_decile': np.float64(14.907475), 'test_avg_loss_top_decile': np.float64(19.446257), 'test_avg_loss_min': np.float64(11.174472), 'test_avg_loss_max': np.float64(19.446257), 'test_avg_loss_bottom10%': np.float64(11.174472), 'test_avg_loss_top10%': np.float64(19.446257), 'test_avg_loss_cos1': np.float64(0.990595), 'test_avg_loss_entropy': np.float64(2.292161), 'val_loss_std': np.float64(8293.3066), 'val_loss_bottom_decile': np.float64(52469.970154), 'val_loss_top_decile': np.float64(68374.120911), 'val_loss_min': np.float64(38665.267578), 'val_loss_max': np.float64(68374.120911), 'val_loss_bottom10%': np.float64(38665.267578), 'val_loss_top10%': np.float64(68374.120911), 'val_loss_cos1': np.float64(0.990645), 'val_loss_entropy': np.float64(2.292199), 'val_avg_loss_std': np.float64(2.444961), 'val_avg_loss_bottom_decile': np.float64(15.468741), 'val_avg_loss_top_decile': np.float64(20.157465), 'val_avg_loss_min': np.float64(11.398959), 'val_avg_loss_max': np.float64(20.157465), 'val_avg_loss_bottom10%': np.float64(11.398959), 'val_avg_loss_top10%': np.float64(20.157465), 'val_avg_loss_cos1': np.float64(0.990645), 'val_avg_loss_entropy': np.float64(2.292199)}}
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2024-11-14 00:49:13,338 (server:353) INFO: Server: Starting evaluation at the end of round 62.
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2024-11-14 00:49:13,339 (server:359) INFO: ----------- Starting a new training round (Round #63) -------------
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2024-11-14 00:50:49,965 (client:354) INFO: {'Role': 'Client #8', 'Round': 63, 'Results_raw': {'train_loss': 13.986809, 'val_loss': 13.449597, 'test_loss': 13.012167}}
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2024-11-14 00:51:24,055 (client:354) INFO: {'Role': 'Client #3', 'Round': 63, 'Results_raw': {'train_loss': 13.145314, 'val_loss': 12.419883, 'test_loss': 12.574024}}
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2024-11-14 00:51:59,351 (client:354) INFO: {'Role': 'Client #10', 'Round': 63, 'Results_raw': {'train_loss': 15.062727, 'val_loss': 14.028648, 'test_loss': 14.412054}}
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2024-11-14 00:52:34,589 (client:354) INFO: {'Role': 'Client #1', 'Round': 63, 'Results_raw': {'train_loss': 14.403755, 'val_loss': 14.621688, 'test_loss': 14.116561}}
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2024-11-14 00:53:09,121 (client:354) INFO: {'Role': 'Client #9', 'Round': 63, 'Results_raw': {'train_loss': 13.148377, 'val_loss': 12.962952, 'test_loss': 12.674025}}
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2024-11-14 00:53:43,448 (client:354) INFO: {'Role': 'Client #7', 'Round': 63, 'Results_raw': {'train_loss': 9.443013, 'val_loss': 9.411611, 'test_loss': 9.577682}}
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2024-11-14 00:54:17,750 (client:354) INFO: {'Role': 'Client #4', 'Round': 63, 'Results_raw': {'train_loss': 14.621616, 'val_loss': 13.28433, 'test_loss': 13.820862}}
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2024-11-14 00:54:52,499 (client:354) INFO: {'Role': 'Client #5', 'Round': 63, 'Results_raw': {'train_loss': 12.020919, 'val_loss': 11.542035, 'test_loss': 12.278077}}
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2024-11-14 00:55:26,701 (client:354) INFO: {'Role': 'Client #2', 'Round': 63, 'Results_raw': {'train_loss': 13.837301, 'val_loss': 12.957688, 'test_loss': 13.396402}}
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2024-11-14 00:56:01,568 (client:354) INFO: {'Role': 'Client #6', 'Round': 63, 'Results_raw': {'train_loss': 7.632979, 'val_loss': 7.024911, 'test_loss': 7.219759}}
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2024-11-14 00:56:01,571 (server:615) INFO: {'Role': 'Server #', 'Round': 62, 'Results_weighted_avg': {'test_loss': np.float64(58241.297052), 'test_avg_loss': np.float64(17.170194), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59723.048459), 'val_avg_loss': np.float64(17.607031), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58241.297052), 'test_avg_loss': np.float64(17.170194), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59723.048459), 'val_avg_loss': np.float64(17.607031), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8007.243768), 'test_loss_bottom_decile': np.float64(49525.048706), 'test_loss_top_decile': np.float64(65839.036621), 'test_loss_min': np.float64(38219.384949), 'test_loss_max': np.float64(65839.036621), 'test_loss_bottom10%': np.float64(38219.384949), 'test_loss_top10%': np.float64(65839.036621), 'test_loss_cos1': np.float64(0.990681), 'test_loss_entropy': np.float64(2.292325), 'test_avg_loss_std': np.float64(2.360626), 'test_avg_loss_bottom_decile': np.float64(14.600545), 'test_avg_loss_top_decile': np.float64(19.410093), 'test_avg_loss_min': np.float64(11.267507), 'test_avg_loss_max': np.float64(19.410093), 'test_avg_loss_bottom10%': np.float64(11.267507), 'test_avg_loss_top10%': np.float64(19.410093), 'test_avg_loss_cos1': np.float64(0.990681), 'test_avg_loss_entropy': np.float64(2.292325), 'val_loss_std': np.float64(8191.165533), 'val_loss_bottom_decile': np.float64(51456.283508), 'val_loss_top_decile': np.float64(67988.662048), 'val_loss_min': np.float64(38953.394257), 'val_loss_max': np.float64(67988.662048), 'val_loss_bottom10%': np.float64(38953.394257), 'val_loss_top10%': np.float64(67988.662048), 'val_loss_cos1': np.float64(0.990725), 'val_loss_entropy': np.float64(2.292362), 'val_avg_loss_std': np.float64(2.414848), 'val_avg_loss_bottom_decile': np.float64(15.169895), 'val_avg_loss_top_decile': np.float64(20.043827), 'val_avg_loss_min': np.float64(11.483902), 'val_avg_loss_max': np.float64(20.043827), 'val_avg_loss_bottom10%': np.float64(11.483902), 'val_avg_loss_top10%': np.float64(20.043827), 'val_avg_loss_cos1': np.float64(0.990725), 'val_avg_loss_entropy': np.float64(2.292362)}}
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2024-11-14 00:56:01,602 (server:353) INFO: Server: Starting evaluation at the end of round 63.
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2024-11-14 00:56:01,602 (server:359) INFO: ----------- Starting a new training round (Round #64) -------------
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2024-11-14 00:57:37,870 (client:354) INFO: {'Role': 'Client #10', 'Round': 64, 'Results_raw': {'train_loss': 14.967404, 'val_loss': 14.226297, 'test_loss': 14.492732}}
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2024-11-14 00:58:15,107 (client:354) INFO: {'Role': 'Client #9', 'Round': 64, 'Results_raw': {'train_loss': 13.183578, 'val_loss': 12.955107, 'test_loss': 12.717387}}
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2024-11-14 00:58:50,773 (client:354) INFO: {'Role': 'Client #1', 'Round': 64, 'Results_raw': {'train_loss': 14.370928, 'val_loss': 14.565747, 'test_loss': 14.130617}}
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2024-11-14 00:59:25,022 (client:354) INFO: {'Role': 'Client #5', 'Round': 64, 'Results_raw': {'train_loss': 11.988697, 'val_loss': 11.633408, 'test_loss': 12.357379}}
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2024-11-14 00:59:59,517 (client:354) INFO: {'Role': 'Client #2', 'Round': 64, 'Results_raw': {'train_loss': 13.832432, 'val_loss': 12.896289, 'test_loss': 13.344001}}
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2024-11-14 01:00:34,967 (client:354) INFO: {'Role': 'Client #7', 'Round': 64, 'Results_raw': {'train_loss': 9.346323, 'val_loss': 8.623919, 'test_loss': 8.996195}}
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2024-11-14 01:01:09,834 (client:354) INFO: {'Role': 'Client #6', 'Round': 64, 'Results_raw': {'train_loss': 7.597885, 'val_loss': 7.069999, 'test_loss': 7.277966}}
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2024-11-14 01:01:44,095 (client:354) INFO: {'Role': 'Client #8', 'Round': 64, 'Results_raw': {'train_loss': 14.097739, 'val_loss': 13.595646, 'test_loss': 13.048769}}
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2024-11-14 01:02:17,555 (client:354) INFO: {'Role': 'Client #3', 'Round': 64, 'Results_raw': {'train_loss': 13.109413, 'val_loss': 12.414225, 'test_loss': 12.57956}}
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2024-11-14 01:02:51,127 (client:354) INFO: {'Role': 'Client #4', 'Round': 64, 'Results_raw': {'train_loss': 14.672504, 'val_loss': 13.308318, 'test_loss': 13.807433}}
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2024-11-14 01:02:51,131 (server:615) INFO: {'Role': 'Server #', 'Round': 63, 'Results_weighted_avg': {'test_loss': np.float64(59232.664102), 'test_avg_loss': np.float64(17.46246), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60762.595453), 'val_avg_loss': np.float64(17.913501), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(59232.664102), 'test_avg_loss': np.float64(17.46246), 'test_total': np.float64(3392.0), 'val_loss': np.float64(60762.595453), 'val_avg_loss': np.float64(17.913501), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8267.099418), 'test_loss_bottom_decile': np.float64(50917.745361), 'test_loss_top_decile': np.float64(66659.39856), 'test_loss_min': np.float64(38084.312042), 'test_loss_max': np.float64(66659.39856), 'test_loss_bottom10%': np.float64(38084.312042), 'test_loss_top10%': np.float64(66659.39856), 'test_loss_cos1': np.float64(0.9904), 'test_loss_entropy': np.float64(2.291937), 'test_avg_loss_std': np.float64(2.437234), 'test_avg_loss_bottom_decile': np.float64(15.011128), 'test_avg_loss_top_decile': np.float64(19.651945), 'test_avg_loss_min': np.float64(11.227686), 'test_avg_loss_max': np.float64(19.651945), 'test_avg_loss_bottom10%': np.float64(11.227686), 'test_avg_loss_top10%': np.float64(19.651945), 'test_avg_loss_cos1': np.float64(0.9904), 'test_avg_loss_entropy': np.float64(2.291937), 'val_loss_std': np.float64(8492.588786), 'val_loss_bottom_decile': np.float64(52851.669312), 'val_loss_top_decile': np.float64(69031.086548), 'val_loss_min': np.float64(38821.963135), 'val_loss_max': np.float64(69031.086548), 'val_loss_bottom10%': np.float64(38821.963135), 'val_loss_top10%': np.float64(69031.086548), 'val_loss_cos1': np.float64(0.990373), 'val_loss_entropy': np.float64(2.291898), 'val_avg_loss_std': np.float64(2.503711), 'val_avg_loss_bottom_decile': np.float64(15.58127), 'val_avg_loss_top_decile': np.float64(20.351146), 'val_avg_loss_min': np.float64(11.445154), 'val_avg_loss_max': np.float64(20.351146), 'val_avg_loss_bottom10%': np.float64(11.445154), 'val_avg_loss_top10%': np.float64(20.351146), 'val_avg_loss_cos1': np.float64(0.990373), 'val_avg_loss_entropy': np.float64(2.291898)}}
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2024-11-14 01:02:51,162 (server:353) INFO: Server: Starting evaluation at the end of round 64.
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2024-11-14 01:02:51,163 (server:359) INFO: ----------- Starting a new training round (Round #65) -------------
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2024-11-14 01:04:26,474 (client:354) INFO: {'Role': 'Client #2', 'Round': 65, 'Results_raw': {'train_loss': 13.849623, 'val_loss': 12.965567, 'test_loss': 13.407973}}
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2024-11-14 01:05:01,826 (client:354) INFO: {'Role': 'Client #5', 'Round': 65, 'Results_raw': {'train_loss': 11.984557, 'val_loss': 11.469888, 'test_loss': 12.226819}}
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2024-11-14 01:05:36,392 (client:354) INFO: {'Role': 'Client #10', 'Round': 65, 'Results_raw': {'train_loss': 15.006864, 'val_loss': 14.30827, 'test_loss': 14.69363}}
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2024-11-14 01:06:10,773 (client:354) INFO: {'Role': 'Client #1', 'Round': 65, 'Results_raw': {'train_loss': 14.373088, 'val_loss': 14.533429, 'test_loss': 14.030173}}
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2024-11-14 01:06:45,411 (client:354) INFO: {'Role': 'Client #7', 'Round': 65, 'Results_raw': {'train_loss': 9.385209, 'val_loss': 8.393763, 'test_loss': 8.82777}}
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2024-11-14 01:07:19,937 (client:354) INFO: {'Role': 'Client #3', 'Round': 65, 'Results_raw': {'train_loss': 13.133662, 'val_loss': 12.434275, 'test_loss': 12.583322}}
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2024-11-14 01:07:54,592 (client:354) INFO: {'Role': 'Client #9', 'Round': 65, 'Results_raw': {'train_loss': 13.126315, 'val_loss': 12.917317, 'test_loss': 12.711791}}
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2024-11-14 01:08:29,310 (client:354) INFO: {'Role': 'Client #8', 'Round': 65, 'Results_raw': {'train_loss': 14.08996, 'val_loss': 13.362759, 'test_loss': 12.83925}}
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2024-11-14 01:09:04,767 (client:354) INFO: {'Role': 'Client #4', 'Round': 65, 'Results_raw': {'train_loss': 14.662446, 'val_loss': 13.280192, 'test_loss': 13.788342}}
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2024-11-14 01:09:40,090 (client:354) INFO: {'Role': 'Client #6', 'Round': 65, 'Results_raw': {'train_loss': 7.636429, 'val_loss': 7.043329, 'test_loss': 7.245921}}
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2024-11-14 01:09:40,093 (server:615) INFO: {'Role': 'Server #', 'Round': 64, 'Results_weighted_avg': {'test_loss': np.float64(58268.276971), 'test_avg_loss': np.float64(17.178148), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59700.039218), 'val_avg_loss': np.float64(17.600247), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58268.276971), 'test_avg_loss': np.float64(17.178148), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59700.039218), 'val_avg_loss': np.float64(17.600247), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7956.0121), 'test_loss_bottom_decile': np.float64(50518.488953), 'test_loss_top_decile': np.float64(65603.046509), 'test_loss_min': np.float64(37858.312439), 'test_loss_max': np.float64(65603.046509), 'test_loss_bottom10%': np.float64(37858.312439), 'test_loss_top10%': np.float64(65603.046509), 'test_loss_cos1': np.float64(0.990807), 'test_loss_entropy': np.float64(2.292415), 'test_avg_loss_std': np.float64(2.345522), 'test_avg_loss_bottom_decile': np.float64(14.893422), 'test_avg_loss_top_decile': np.float64(19.340521), 'test_avg_loss_min': np.float64(11.161059), 'test_avg_loss_max': np.float64(19.340521), 'test_avg_loss_bottom10%': np.float64(11.161059), 'test_avg_loss_top10%': np.float64(19.340521), 'test_avg_loss_cos1': np.float64(0.990807), 'test_avg_loss_entropy': np.float64(2.292415), 'val_loss_std': np.float64(8110.652885), 'val_loss_bottom_decile': np.float64(52339.836304), 'val_loss_top_decile': np.float64(67589.1745), 'val_loss_min': np.float64(38629.315277), 'val_loss_max': np.float64(67589.1745), 'val_loss_bottom10%': np.float64(38629.315277), 'val_loss_top10%': np.float64(67589.1745), 'val_loss_cos1': np.float64(0.990897), 'val_loss_entropy': np.float64(2.292502), 'val_avg_loss_std': np.float64(2.391112), 'val_avg_loss_bottom_decile': np.float64(15.430376), 'val_avg_loss_top_decile': np.float64(19.926054), 'val_avg_loss_min': np.float64(11.388359), 'val_avg_loss_max': np.float64(19.926054), 'val_avg_loss_bottom10%': np.float64(11.388359), 'val_avg_loss_top10%': np.float64(19.926054), 'val_avg_loss_cos1': np.float64(0.990897), 'val_avg_loss_entropy': np.float64(2.292502)}}
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2024-11-14 01:09:40,126 (server:353) INFO: Server: Starting evaluation at the end of round 65.
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2024-11-14 01:09:40,127 (server:359) INFO: ----------- Starting a new training round (Round #66) -------------
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2024-11-14 01:11:15,915 (client:354) INFO: {'Role': 'Client #4', 'Round': 66, 'Results_raw': {'train_loss': 14.572417, 'val_loss': 13.343036, 'test_loss': 13.803085}}
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2024-11-14 01:11:50,298 (client:354) INFO: {'Role': 'Client #6', 'Round': 66, 'Results_raw': {'train_loss': 7.621078, 'val_loss': 7.038739, 'test_loss': 7.259781}}
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2024-11-14 01:12:25,523 (client:354) INFO: {'Role': 'Client #9', 'Round': 66, 'Results_raw': {'train_loss': 13.107467, 'val_loss': 13.035629, 'test_loss': 12.699119}}
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2024-11-14 01:13:02,328 (client:354) INFO: {'Role': 'Client #5', 'Round': 66, 'Results_raw': {'train_loss': 11.939933, 'val_loss': 11.475592, 'test_loss': 12.182809}}
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2024-11-14 01:13:38,118 (client:354) INFO: {'Role': 'Client #2', 'Round': 66, 'Results_raw': {'train_loss': 13.81967, 'val_loss': 12.985077, 'test_loss': 13.390783}}
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2024-11-14 01:14:12,330 (client:354) INFO: {'Role': 'Client #10', 'Round': 66, 'Results_raw': {'train_loss': 14.92444, 'val_loss': 14.075632, 'test_loss': 14.502145}}
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2024-11-14 01:14:46,538 (client:354) INFO: {'Role': 'Client #8', 'Round': 66, 'Results_raw': {'train_loss': 13.987461, 'val_loss': 13.350811, 'test_loss': 12.801615}}
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2024-11-14 01:15:20,705 (client:354) INFO: {'Role': 'Client #1', 'Round': 66, 'Results_raw': {'train_loss': 14.376521, 'val_loss': 14.474938, 'test_loss': 14.074495}}
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2024-11-14 01:15:55,155 (client:354) INFO: {'Role': 'Client #7', 'Round': 66, 'Results_raw': {'train_loss': 9.373978, 'val_loss': 8.695778, 'test_loss': 9.163075}}
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2024-11-14 01:16:29,320 (client:354) INFO: {'Role': 'Client #3', 'Round': 66, 'Results_raw': {'train_loss': 13.094827, 'val_loss': 12.329587, 'test_loss': 12.513456}}
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2024-11-14 01:16:29,323 (server:615) INFO: {'Role': 'Server #', 'Round': 65, 'Results_weighted_avg': {'test_loss': np.float64(58061.085226), 'test_avg_loss': np.float64(17.117065), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59525.256546), 'val_avg_loss': np.float64(17.54872), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58061.085226), 'test_avg_loss': np.float64(17.117065), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59525.256546), 'val_avg_loss': np.float64(17.54872), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7901.478654), 'test_loss_bottom_decile': np.float64(49760.75), 'test_loss_top_decile': np.float64(65324.556213), 'test_loss_min': np.float64(38008.057281), 'test_loss_max': np.float64(65324.556213), 'test_loss_bottom10%': np.float64(38008.057281), 'test_loss_top10%': np.float64(65324.556213), 'test_loss_cos1': np.float64(0.990867), 'test_loss_entropy': np.float64(2.292504), 'test_avg_loss_std': np.float64(2.329445), 'test_avg_loss_bottom_decile': np.float64(14.670032), 'test_avg_loss_top_decile': np.float64(19.258419), 'test_avg_loss_min': np.float64(11.205206), 'test_avg_loss_max': np.float64(19.258419), 'test_avg_loss_bottom10%': np.float64(11.205206), 'test_avg_loss_top10%': np.float64(19.258419), 'test_avg_loss_cos1': np.float64(0.990867), 'test_avg_loss_entropy': np.float64(2.292504), 'val_loss_std': np.float64(8091.647361), 'val_loss_bottom_decile': np.float64(51658.738647), 'val_loss_top_decile': np.float64(67608.144592), 'val_loss_min': np.float64(38729.202728), 'val_loss_max': np.float64(67608.144592), 'val_loss_bottom10%': np.float64(38729.202728), 'val_loss_top10%': np.float64(67608.144592), 'val_loss_cos1': np.float64(0.990887), 'val_loss_entropy': np.float64(2.292514), 'val_avg_loss_std': np.float64(2.385509), 'val_avg_loss_bottom_decile': np.float64(15.229581), 'val_avg_loss_top_decile': np.float64(19.931646), 'val_avg_loss_min': np.float64(11.417807), 'val_avg_loss_max': np.float64(19.931646), 'val_avg_loss_bottom10%': np.float64(11.417807), 'val_avg_loss_top10%': np.float64(19.931646), 'val_avg_loss_cos1': np.float64(0.990887), 'val_avg_loss_entropy': np.float64(2.292514)}}
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2024-11-14 01:16:29,356 (server:353) INFO: Server: Starting evaluation at the end of round 66.
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2024-11-14 01:16:29,356 (server:359) INFO: ----------- Starting a new training round (Round #67) -------------
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2024-11-14 01:18:06,194 (client:354) INFO: {'Role': 'Client #3', 'Round': 67, 'Results_raw': {'train_loss': 13.125432, 'val_loss': 12.3423, 'test_loss': 12.54857}}
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2024-11-14 01:18:40,528 (client:354) INFO: {'Role': 'Client #9', 'Round': 67, 'Results_raw': {'train_loss': 13.077683, 'val_loss': 13.063199, 'test_loss': 12.750651}}
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2024-11-14 01:19:14,972 (client:354) INFO: {'Role': 'Client #5', 'Round': 67, 'Results_raw': {'train_loss': 11.926814, 'val_loss': 11.508579, 'test_loss': 12.204062}}
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2024-11-14 01:19:49,847 (client:354) INFO: {'Role': 'Client #7', 'Round': 67, 'Results_raw': {'train_loss': 9.319368, 'val_loss': 8.430722, 'test_loss': 8.817213}}
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2024-11-14 01:20:24,157 (client:354) INFO: {'Role': 'Client #4', 'Round': 67, 'Results_raw': {'train_loss': 14.595769, 'val_loss': 13.368374, 'test_loss': 13.894182}}
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2024-11-14 01:20:58,517 (client:354) INFO: {'Role': 'Client #1', 'Round': 67, 'Results_raw': {'train_loss': 14.283736, 'val_loss': 14.481543, 'test_loss': 14.050762}}
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2024-11-14 01:21:32,991 (client:354) INFO: {'Role': 'Client #6', 'Round': 67, 'Results_raw': {'train_loss': 7.608626, 'val_loss': 6.972405, 'test_loss': 7.191624}}
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2024-11-14 01:22:08,526 (client:354) INFO: {'Role': 'Client #10', 'Round': 67, 'Results_raw': {'train_loss': 14.960032, 'val_loss': 14.110467, 'test_loss': 14.463575}}
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2024-11-14 01:22:43,072 (client:354) INFO: {'Role': 'Client #2', 'Round': 67, 'Results_raw': {'train_loss': 13.78321, 'val_loss': 12.929659, 'test_loss': 13.363606}}
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2024-11-14 01:23:17,635 (client:354) INFO: {'Role': 'Client #8', 'Round': 67, 'Results_raw': {'train_loss': 14.020673, 'val_loss': 13.404203, 'test_loss': 12.901955}}
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2024-11-14 01:23:17,638 (server:615) INFO: {'Role': 'Server #', 'Round': 66, 'Results_weighted_avg': {'test_loss': np.float64(58045.112024), 'test_avg_loss': np.float64(17.112356), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59456.574265), 'val_avg_loss': np.float64(17.528471), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58045.112024), 'test_avg_loss': np.float64(17.112356), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59456.574265), 'val_avg_loss': np.float64(17.528471), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7833.856027), 'test_loss_bottom_decile': np.float64(49684.740417), 'test_loss_top_decile': np.float64(65256.217407), 'test_loss_min': np.float64(38232.175415), 'test_loss_max': np.float64(65256.217407), 'test_loss_bottom10%': np.float64(38232.175415), 'test_loss_top10%': np.float64(65256.217407), 'test_loss_cos1': np.float64(0.991015), 'test_loss_entropy': np.float64(2.292686), 'test_avg_loss_std': np.float64(2.309509), 'test_avg_loss_bottom_decile': np.float64(14.647624), 'test_avg_loss_top_decile': np.float64(19.238272), 'test_avg_loss_min': np.float64(11.271278), 'test_avg_loss_max': np.float64(19.238272), 'test_avg_loss_bottom10%': np.float64(11.271278), 'test_avg_loss_top10%': np.float64(19.238272), 'test_avg_loss_cos1': np.float64(0.991015), 'test_avg_loss_entropy': np.float64(2.292686), 'val_loss_std': np.float64(8016.692842), 'val_loss_bottom_decile': np.float64(51401.787231), 'val_loss_top_decile': np.float64(67535.3255), 'val_loss_min': np.float64(38971.925751), 'val_loss_max': np.float64(67535.3255), 'val_loss_bottom10%': np.float64(38971.925751), 'val_loss_top10%': np.float64(67535.3255), 'val_loss_cos1': np.float64(0.991032), 'val_loss_entropy': np.float64(2.292697), 'val_avg_loss_std': np.float64(2.363412), 'val_avg_loss_bottom_decile': np.float64(15.153829), 'val_avg_loss_top_decile': np.float64(19.910179), 'val_avg_loss_min': np.float64(11.489365), 'val_avg_loss_max': np.float64(19.910179), 'val_avg_loss_bottom10%': np.float64(11.489365), 'val_avg_loss_top10%': np.float64(19.910179), 'val_avg_loss_cos1': np.float64(0.991032), 'val_avg_loss_entropy': np.float64(2.292697)}}
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2024-11-14 01:23:17,681 (server:353) INFO: Server: Starting evaluation at the end of round 67.
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2024-11-14 01:23:17,682 (server:359) INFO: ----------- Starting a new training round (Round #68) -------------
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2024-11-14 01:24:53,930 (client:354) INFO: {'Role': 'Client #9', 'Round': 68, 'Results_raw': {'train_loss': 13.09119, 'val_loss': 13.186672, 'test_loss': 12.872151}}
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2024-11-14 01:25:28,282 (client:354) INFO: {'Role': 'Client #2', 'Round': 68, 'Results_raw': {'train_loss': 13.765185, 'val_loss': 12.872783, 'test_loss': 13.309785}}
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2024-11-14 01:26:02,969 (client:354) INFO: {'Role': 'Client #5', 'Round': 68, 'Results_raw': {'train_loss': 11.961694, 'val_loss': 11.545074, 'test_loss': 12.342405}}
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2024-11-14 01:26:38,845 (client:354) INFO: {'Role': 'Client #1', 'Round': 68, 'Results_raw': {'train_loss': 14.323389, 'val_loss': 14.54173, 'test_loss': 14.157601}}
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2024-11-14 01:27:16,908 (client:354) INFO: {'Role': 'Client #10', 'Round': 68, 'Results_raw': {'train_loss': 14.931585, 'val_loss': 14.043071, 'test_loss': 14.442753}}
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2024-11-14 01:27:52,260 (client:354) INFO: {'Role': 'Client #4', 'Round': 68, 'Results_raw': {'train_loss': 14.576185, 'val_loss': 13.315997, 'test_loss': 13.847465}}
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2024-11-14 01:28:27,567 (client:354) INFO: {'Role': 'Client #3', 'Round': 68, 'Results_raw': {'train_loss': 13.084985, 'val_loss': 12.312793, 'test_loss': 12.52091}}
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2024-11-14 01:29:02,582 (client:354) INFO: {'Role': 'Client #7', 'Round': 68, 'Results_raw': {'train_loss': 9.357109, 'val_loss': 8.603217, 'test_loss': 8.81923}}
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2024-11-14 01:29:37,758 (client:354) INFO: {'Role': 'Client #8', 'Round': 68, 'Results_raw': {'train_loss': 14.060408, 'val_loss': 13.262084, 'test_loss': 12.75939}}
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2024-11-14 01:30:12,290 (client:354) INFO: {'Role': 'Client #6', 'Round': 68, 'Results_raw': {'train_loss': 7.585938, 'val_loss': 7.010942, 'test_loss': 7.193634}}
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2024-11-14 01:30:12,293 (server:615) INFO: {'Role': 'Server #', 'Round': 67, 'Results_weighted_avg': {'test_loss': np.float64(58127.106467), 'test_avg_loss': np.float64(17.136529), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59536.468152), 'val_avg_loss': np.float64(17.552025), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58127.106467), 'test_avg_loss': np.float64(17.136529), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59536.468152), 'val_avg_loss': np.float64(17.552025), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7976.947233), 'test_loss_bottom_decile': np.float64(49498.115387), 'test_loss_top_decile': np.float64(65654.512024), 'test_loss_min': np.float64(38069.433899), 'test_loss_max': np.float64(65654.512024), 'test_loss_bottom10%': np.float64(38069.433899), 'test_loss_top10%': np.float64(65654.512024), 'test_loss_cos1': np.float64(0.990715), 'test_loss_entropy': np.float64(2.292348), 'test_avg_loss_std': np.float64(2.351694), 'test_avg_loss_bottom_decile': np.float64(14.592605), 'test_avg_loss_top_decile': np.float64(19.355693), 'test_avg_loss_min': np.float64(11.2233), 'test_avg_loss_max': np.float64(19.355693), 'test_avg_loss_bottom10%': np.float64(11.2233), 'test_avg_loss_top10%': np.float64(19.355693), 'test_avg_loss_cos1': np.float64(0.990715), 'test_avg_loss_entropy': np.float64(2.292348), 'val_loss_std': np.float64(8175.694238), 'val_loss_bottom_decile': np.float64(51301.919922), 'val_loss_top_decile': np.float64(67749.732178), 'val_loss_min': np.float64(38772.562927), 'val_loss_max': np.float64(67749.732178), 'val_loss_bottom10%': np.float64(38772.562927), 'val_loss_top10%': np.float64(67749.732178), 'val_loss_cos1': np.float64(0.990703), 'val_loss_entropy': np.float64(2.292331), 'val_avg_loss_std': np.float64(2.410287), 'val_avg_loss_bottom_decile': np.float64(15.124387), 'val_avg_loss_top_decile': np.float64(19.973388), 'val_avg_loss_min': np.float64(11.43059), 'val_avg_loss_max': np.float64(19.973388), 'val_avg_loss_bottom10%': np.float64(11.43059), 'val_avg_loss_top10%': np.float64(19.973388), 'val_avg_loss_cos1': np.float64(0.990703), 'val_avg_loss_entropy': np.float64(2.292331)}}
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2024-11-14 01:30:12,328 (server:353) INFO: Server: Starting evaluation at the end of round 68.
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2024-11-14 01:30:12,329 (server:359) INFO: ----------- Starting a new training round (Round #69) -------------
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2024-11-14 01:31:48,117 (client:354) INFO: {'Role': 'Client #10', 'Round': 69, 'Results_raw': {'train_loss': 14.939331, 'val_loss': 13.960492, 'test_loss': 14.364252}}
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2024-11-14 01:32:22,239 (client:354) INFO: {'Role': 'Client #8', 'Round': 69, 'Results_raw': {'train_loss': 13.95335, 'val_loss': 13.513993, 'test_loss': 13.003044}}
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2024-11-14 01:32:56,222 (client:354) INFO: {'Role': 'Client #6', 'Round': 69, 'Results_raw': {'train_loss': 7.586277, 'val_loss': 6.98405, 'test_loss': 7.201555}}
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2024-11-14 01:33:30,380 (client:354) INFO: {'Role': 'Client #5', 'Round': 69, 'Results_raw': {'train_loss': 11.939226, 'val_loss': 11.624904, 'test_loss': 12.332597}}
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2024-11-14 01:34:05,175 (client:354) INFO: {'Role': 'Client #9', 'Round': 69, 'Results_raw': {'train_loss': 13.030418, 'val_loss': 13.056115, 'test_loss': 12.738994}}
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2024-11-14 01:34:41,129 (client:354) INFO: {'Role': 'Client #7', 'Round': 69, 'Results_raw': {'train_loss': 9.349306, 'val_loss': 8.394916, 'test_loss': 8.686783}}
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2024-11-14 01:35:17,015 (client:354) INFO: {'Role': 'Client #2', 'Round': 69, 'Results_raw': {'train_loss': 13.751776, 'val_loss': 12.840853, 'test_loss': 13.330995}}
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2024-11-14 01:35:51,667 (client:354) INFO: {'Role': 'Client #4', 'Round': 69, 'Results_raw': {'train_loss': 14.490458, 'val_loss': 13.338157, 'test_loss': 13.857408}}
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2024-11-14 01:36:26,160 (client:354) INFO: {'Role': 'Client #1', 'Round': 69, 'Results_raw': {'train_loss': 14.339534, 'val_loss': 14.428902, 'test_loss': 13.957914}}
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2024-11-14 01:37:01,023 (client:354) INFO: {'Role': 'Client #3', 'Round': 69, 'Results_raw': {'train_loss': 13.153174, 'val_loss': 12.262387, 'test_loss': 12.435469}}
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2024-11-14 01:37:01,027 (server:615) INFO: {'Role': 'Server #', 'Round': 68, 'Results_weighted_avg': {'test_loss': np.float64(58187.989066), 'test_avg_loss': np.float64(17.154478), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59592.76076), 'val_avg_loss': np.float64(17.568621), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58187.989066), 'test_avg_loss': np.float64(17.154478), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59592.76076), 'val_avg_loss': np.float64(17.568621), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(8003.512088), 'test_loss_bottom_decile': np.float64(49540.396118), 'test_loss_top_decile': np.float64(65614.380371), 'test_loss_min': np.float64(38100.569733), 'test_loss_max': np.float64(65614.380371), 'test_loss_bottom10%': np.float64(38100.569733), 'test_loss_top10%': np.float64(65614.380371), 'test_loss_cos1': np.float64(0.990673), 'test_loss_entropy': np.float64(2.292305), 'test_avg_loss_std': np.float64(2.359526), 'test_avg_loss_bottom_decile': np.float64(14.60507), 'test_avg_loss_top_decile': np.float64(19.343862), 'test_avg_loss_min': np.float64(11.232479), 'test_avg_loss_max': np.float64(19.343862), 'test_avg_loss_bottom10%': np.float64(11.232479), 'test_avg_loss_top10%': np.float64(19.343862), 'test_avg_loss_cos1': np.float64(0.990673), 'test_avg_loss_entropy': np.float64(2.292305), 'val_loss_std': np.float64(8184.487955), 'val_loss_bottom_decile': np.float64(51313.976868), 'val_loss_top_decile': np.float64(67909.981445), 'val_loss_min': np.float64(38821.691711), 'val_loss_max': np.float64(67909.981445), 'val_loss_bottom10%': np.float64(38821.691711), 'val_loss_top10%': np.float64(67909.981445), 'val_loss_cos1': np.float64(0.9907), 'val_loss_entropy': np.float64(2.29233), 'val_avg_loss_std': np.float64(2.41288), 'val_avg_loss_bottom_decile': np.float64(15.127941), 'val_avg_loss_top_decile': np.float64(20.020631), 'val_avg_loss_min': np.float64(11.445074), 'val_avg_loss_max': np.float64(20.020631), 'val_avg_loss_bottom10%': np.float64(11.445074), 'val_avg_loss_top10%': np.float64(20.020631), 'val_avg_loss_cos1': np.float64(0.9907), 'val_avg_loss_entropy': np.float64(2.29233)}}
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2024-11-14 01:37:01,066 (server:370) INFO: Server: Training is finished! Starting evaluation.
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2024-11-14 01:38:03,530 (server:615) INFO: {'Role': 'Server #', 'Round': 69, 'Results_weighted_avg': {'test_loss': np.float64(58078.727985), 'test_avg_loss': np.float64(17.122267), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59485.923132), 'val_avg_loss': np.float64(17.537124), 'val_total': np.float64(3392.0)}, 'Results_avg': {'test_loss': np.float64(58078.727985), 'test_avg_loss': np.float64(17.122267), 'test_total': np.float64(3392.0), 'val_loss': np.float64(59485.923132), 'val_avg_loss': np.float64(17.537124), 'val_total': np.float64(3392.0)}, 'Results_fairness': {'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(7982.128528), 'test_loss_bottom_decile': np.float64(49585.558472), 'test_loss_top_decile': np.float64(65409.343811), 'test_loss_min': np.float64(37947.675476), 'test_loss_max': np.float64(65409.343811), 'test_loss_bottom10%': np.float64(37947.675476), 'test_loss_top10%': np.float64(65409.343811), 'test_loss_cos1': np.float64(0.990687), 'test_loss_entropy': np.float64(2.292311), 'test_avg_loss_std': np.float64(2.353222), 'test_avg_loss_bottom_decile': np.float64(14.618384), 'test_avg_loss_top_decile': np.float64(19.283415), 'test_avg_loss_min': np.float64(11.187404), 'test_avg_loss_max': np.float64(19.283415), 'test_avg_loss_bottom10%': np.float64(11.187404), 'test_avg_loss_top10%': np.float64(19.283415), 'test_avg_loss_cos1': np.float64(0.990687), 'test_avg_loss_entropy': np.float64(2.292311), 'val_loss_std': np.float64(8171.513329), 'val_loss_bottom_decile': np.float64(51396.266602), 'val_loss_top_decile': np.float64(67842.57428), 'val_loss_min': np.float64(38652.544556), 'val_loss_max': np.float64(67842.57428), 'val_loss_bottom10%': np.float64(38652.544556), 'val_loss_top10%': np.float64(67842.57428), 'val_loss_cos1': np.float64(0.990696), 'val_loss_entropy': np.float64(2.292315), 'val_avg_loss_std': np.float64(2.409055), 'val_avg_loss_bottom_decile': np.float64(15.152201), 'val_avg_loss_top_decile': np.float64(20.000759), 'val_avg_loss_min': np.float64(11.395208), 'val_avg_loss_max': np.float64(20.000759), 'val_avg_loss_bottom10%': np.float64(11.395208), 'val_avg_loss_top10%': np.float64(20.000759), 'val_avg_loss_cos1': np.float64(0.990696), 'val_avg_loss_entropy': np.float64(2.292315)}}
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2024-11-14 01:38:03,533 (server:420) INFO: Server: Final evaluation is finished! Starting merging results.
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2024-11-14 01:38:03,534 (server:546) INFO: {'Role': 'Server #', 'Round': 'Final', 'Results_raw': {'client_best_individual': {'val_loss': 38629.315277, 'test_loss': 37858.312439, 'test_avg_loss': 11.161059, 'test_total': 3392.0, 'val_avg_loss': 11.388359, 'val_total': 3392.0}, 'client_summarized_weighted_avg': {'val_loss': np.float64(59456.574265), 'test_loss': np.float64(58045.112024), 'test_avg_loss': np.float64(17.112356), 'test_total': np.float64(3392.0), 'val_avg_loss': np.float64(17.528471), 'val_total': np.float64(3392.0)}, 'client_summarized_avg': {'val_loss': np.float64(59456.574265), 'test_loss': np.float64(58045.112024), 'test_avg_loss': np.float64(17.112356), 'test_total': np.float64(3392.0), 'val_avg_loss': np.float64(17.528471), 'val_total': np.float64(3392.0)}, 'client_summarized_fairness': {'val_loss_entropy': np.float64(2.273632), 'val_loss_cos1': np.float64(0.974339), 'val_loss_top10%': np.float64(276400.308716), 'val_loss_bottom10%': np.float64(100362.033997), 'val_loss_max': np.float64(276400.308716), 'val_loss_min': np.float64(100362.033997), 'val_loss_top_decile': np.float64(276400.308716), 'val_loss_bottom_decile': np.float64(166428.48645), 'val_loss_std': np.float64(47619.912951), 'test_total': np.float64(3392.0), 'val_total': np.float64(3392.0), 'test_loss_std': np.float64(44221.570644), 'test_loss_bottom_decile': np.float64(164565.058838), 'test_loss_top_decile': np.float64(257037.366577), 'test_loss_min': np.float64(97363.599854), 'test_loss_max': np.float64(257037.366577), 'test_loss_bottom10%': np.float64(97363.599854), 'test_loss_top10%': np.float64(257037.366577), 'test_loss_cos1': np.float64(0.975817), 'test_loss_entropy': np.float64(2.275183), 'test_avg_loss_std': np.float64(13.03702), 'test_avg_loss_bottom_decile': np.float64(48.515642), 'test_avg_loss_top_decile': np.float64(75.777526), 'test_avg_loss_min': np.float64(28.703891), 'test_avg_loss_max': np.float64(75.777526), 'test_avg_loss_bottom10%': np.float64(28.703891), 'test_avg_loss_top10%': np.float64(75.777526), 'test_avg_loss_cos1': np.float64(0.975817), 'test_avg_loss_entropy': np.float64(2.275183), 'val_avg_loss_std': np.float64(14.038889), 'val_avg_loss_bottom_decile': np.float64(49.065002), 'val_avg_loss_top_decile': np.float64(81.48594), 'val_avg_loss_min': np.float64(29.587864), 'val_avg_loss_max': np.float64(81.48594), 'val_avg_loss_bottom10%': np.float64(29.587864), 'val_avg_loss_top10%': np.float64(81.48594), 'val_avg_loss_cos1': np.float64(0.974339), 'val_avg_loss_entropy': np.float64(2.273632)}}}
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2024-11-14 01:38:03,536 (server:565) INFO: {'Role': 'Client #1', 'Round': 70, 'Results_raw': {'test_loss': 63145.387756, 'test_avg_loss': 18.615975, 'test_total': 3392, 'val_loss': 67842.57428, 'val_avg_loss': 20.000759, 'val_total': 3392}}
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2024-11-14 01:38:03,536 (server:565) INFO: {'Role': 'Client #2', 'Round': 70, 'Results_raw': {'test_loss': 63759.158264, 'test_avg_loss': 18.796922, 'test_total': 3392, 'val_loss': 61998.397522, 'val_avg_loss': 18.277829, 'val_total': 3392}}
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2024-11-14 01:38:03,537 (server:565) INFO: {'Role': 'Client #3', 'Round': 70, 'Results_raw': {'test_loss': 57131.737122, 'test_avg_loss': 16.843083, 'test_total': 3392, 'val_loss': 59344.659973, 'val_avg_loss': 17.495478, 'val_total': 3392}}
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2024-11-14 01:38:03,537 (server:565) INFO: {'Role': 'Client #4', 'Round': 70, 'Results_raw': {'test_loss': 62270.352356, 'test_avg_loss': 18.358005, 'test_total': 3392, 'val_loss': 61970.559143, 'val_avg_loss': 18.269622, 'val_total': 3392}}
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2024-11-14 01:38:03,538 (server:565) INFO: {'Role': 'Client #5', 'Round': 70, 'Results_raw': {'test_loss': 60709.961426, 'test_avg_loss': 17.897984, 'test_total': 3392, 'val_loss': 60877.654297, 'val_avg_loss': 17.947422, 'val_total': 3392}}
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2024-11-14 01:38:03,538 (server:565) INFO: {'Role': 'Client #6', 'Round': 70, 'Results_raw': {'test_loss': 37947.675476, 'test_avg_loss': 11.187404, 'test_total': 3392, 'val_loss': 38652.544556, 'val_avg_loss': 11.395208, 'val_total': 3392}}
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2024-11-14 01:38:03,538 (server:565) INFO: {'Role': 'Client #7', 'Round': 70, 'Results_raw': {'test_loss': 49585.558472, 'test_avg_loss': 14.618384, 'test_total': 3392, 'val_loss': 51396.266602, 'val_avg_loss': 15.152201, 'val_total': 3392}}
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2024-11-14 01:38:03,539 (server:565) INFO: {'Role': 'Client #8', 'Round': 70, 'Results_raw': {'test_loss': 62840.667419, 'test_avg_loss': 18.52614, 'test_total': 3392, 'val_loss': 66413.611877, 'val_avg_loss': 19.579485, 'val_total': 3392}}
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2024-11-14 01:38:03,539 (server:565) INFO: {'Role': 'Client #9', 'Round': 70, 'Results_raw': {'test_loss': 57987.437744, 'test_avg_loss': 17.095353, 'test_total': 3392, 'val_loss': 61098.156433, 'val_avg_loss': 18.012428, 'val_total': 3392}}
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2024-11-14 01:38:03,540 (server:565) INFO: {'Role': 'Client #10', 'Round': 70, 'Results_raw': {'test_loss': 65409.343811, 'test_avg_loss': 19.283415, 'test_total': 3392, 'val_loss': 65264.806641, 'val_avg_loss': 19.240804, 'val_total': 3392}}
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2024-11-14 01:38:03,549 (monitor:173) INFO: In worker #0, the system-related metrics are: {'id': 0, 'fl_end_time_minutes': 534.940399, 'total_model_size': 0, 'total_flops': 0, 'total_upload_bytes': 0, 'total_download_bytes': 11787256, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,550 (client:582) INFO: ================= client 1 received finish message =================
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2024-11-14 01:38:03,554 (monitor:173) INFO: In worker #1, the system-related metrics are: {'id': 1, 'fl_end_time_minutes': 534.94021, 'total_model_size': 563714, 'total_flops': 22141095984000.0, 'total_upload_bytes': 0, 'total_download_bytes': 2595040, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,555 (client:582) INFO: ================= client 2 received finish message =================
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2024-11-14 01:38:03,557 (monitor:173) INFO: In worker #2, the system-related metrics are: {'id': 2, 'fl_end_time_minutes': 534.939697, 'total_model_size': 563714, 'total_flops': 22141095984000.0, 'total_upload_bytes': 0, 'total_download_bytes': 2595040, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,557 (client:582) INFO: ================= client 3 received finish message =================
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2024-11-14 01:38:03,560 (monitor:173) INFO: In worker #3, the system-related metrics are: {'id': 3, 'fl_end_time_minutes': 534.939232, 'total_model_size': 563714, 'total_flops': 22141095984000.0, 'total_upload_bytes': 0, 'total_download_bytes': 2595040, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,560 (client:582) INFO: ================= client 4 received finish message =================
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2024-11-14 01:38:03,562 (monitor:173) INFO: In worker #4, the system-related metrics are: {'id': 4, 'fl_end_time_minutes': 534.938791, 'total_model_size': 563714, 'total_flops': 22141095984000.0, 'total_upload_bytes': 0, 'total_download_bytes': 2595040, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,563 (client:582) INFO: ================= client 5 received finish message =================
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2024-11-14 01:38:03,565 (monitor:173) INFO: In worker #5, the system-related metrics are: {'id': 5, 'fl_end_time_minutes': 534.938436, 'total_model_size': 563714, 'total_flops': 22141095984000.0, 'total_upload_bytes': 0, 'total_download_bytes': 2595040, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,565 (client:582) INFO: ================= client 6 received finish message =================
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2024-11-14 01:38:03,567 (monitor:173) INFO: In worker #6, the system-related metrics are: {'id': 6, 'fl_end_time_minutes': 534.938141, 'total_model_size': 563714, 'total_flops': 22141095984000.0, 'total_upload_bytes': 0, 'total_download_bytes': 2595040, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,568 (client:582) INFO: ================= client 7 received finish message =================
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2024-11-14 01:38:03,570 (monitor:173) INFO: In worker #7, the system-related metrics are: {'id': 7, 'fl_end_time_minutes': 534.937825, 'total_model_size': 563714, 'total_flops': 22141095984000.0, 'total_upload_bytes': 0, 'total_download_bytes': 2595040, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,570 (client:582) INFO: ================= client 8 received finish message =================
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2024-11-14 01:38:03,572 (monitor:173) INFO: In worker #8, the system-related metrics are: {'id': 8, 'fl_end_time_minutes': 534.937562, 'total_model_size': 563714, 'total_flops': 22141095984000.0, 'total_upload_bytes': 0, 'total_download_bytes': 2595040, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,572 (client:582) INFO: ================= client 9 received finish message =================
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2024-11-14 01:38:03,575 (monitor:173) INFO: In worker #9, the system-related metrics are: {'id': 9, 'fl_end_time_minutes': 534.937302, 'total_model_size': 563714, 'total_flops': 22141095984000.0, 'total_upload_bytes': 0, 'total_download_bytes': 2595040, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,575 (client:582) INFO: ================= client 10 received finish message =================
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2024-11-14 01:38:03,578 (monitor:173) INFO: In worker #10, the system-related metrics are: {'id': 10, 'fl_end_time_minutes': 534.937039, 'total_model_size': 563714, 'total_flops': 22141095984000.0, 'total_upload_bytes': 0, 'total_download_bytes': 2595040, 'global_convergence_round': 0, 'local_convergence_round': 0, 'global_convergence_time_minutes': 0, 'local_convergence_time_minutes': 0}
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2024-11-14 01:38:03,579 (monitor:338) INFO: We will compress the file eval_results.raw into a .gz file, and delete the old one
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2024-11-14 01:38:03,612 (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(534.938603), 'sys_avg/total_model_size': '500.46K', 'sys_avg/total_flops': '18.31T', 'sys_avg/total_upload_bytes': '0.0', 'sys_avg/total_download_bytes': '3.27M', '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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2024-11-14 01:38:03,613 (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(0.001108), 'sys_std/total_model_size': '158.26K', 'sys_std/total_flops': '5.79T', 'sys_std/total_upload_bytes': '0.0', 'sys_std/total_download_bytes': '2.52M', '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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