1414 lines
309 KiB
Plaintext
1414 lines
309 KiB
Plaintext
2024-11-14 11:58:30,656 (logging:124) INFO: the current machine is at 127.0.1.1
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2024-11-14 11:58:30,657 (logging:126) INFO: the current dir is /home/czzhangheng/code/FederatedScope
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2024-11-14 11:58:30,657 (logging:127) INFO: the output dir is exp/FedAvg_FedDGCN_on_trafficflow_lr0.01_lstep1/sub_exp_20241114115830
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2024-11-14 12:00:28,794 (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: RMSE
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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: 883
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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/PeMS07
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save_data: False
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scaler: [309.541473, 189.507461]
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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_lr0.01_lstep1
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expname_tag:
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feat_engr:
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num_bins: 5
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scenario: hfl
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secure:
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dp:
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encrypt:
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type: dummy
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key_size: 3072
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type: encrypt
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selec_threshold: 0.05
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selec_woe_binning: quantile
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type:
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federate:
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atc_load_from:
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atc_vanilla: False
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client_num: 10
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data_weighted_aggr: False
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ignore_weight: False
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join_in_info: []
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make_global_eval: False
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master_addr: 127.0.0.1
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master_port: 29500
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merge_test_data: False
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merge_val_data: False
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method: FedAvg
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mode: standalone
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online_aggr: False
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process_num: 1
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resource_info_file:
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restore_from:
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sample_client_num: 10
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sample_client_rate: -1.0
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sampler: uniform
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save_to:
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share_local_model: False
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total_round_num: 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: 88
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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_lr0.01_lstep1/sub_exp_20241114115830
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personalization:
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K: 5
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beta: 1.0
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epoch_feature: 1
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epoch_linear: 2
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local_param: []
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local_update_steps: 1
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lr: 0.01
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lr_feature: 0.1
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lr_linear: 0.1
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regular_weight: 0.1
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share_non_trainable_para: False
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weight_decay: 0.0
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print_decimal_digits: 6
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quantization:
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method: none
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nbits: 8
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regularizer:
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mu: 0.0
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type:
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seed: 10
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sgdmf:
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use: False
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train:
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batch_or_epoch: epoch
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batch_size: 64
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data_para_dids: []
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early_stop: False
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early_stop_patience: 15
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epochs: 250
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grad_norm: True
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local_update_steps: 1
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loss_func: mae
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lr_decay: False
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lr_decay_rate: 0.3
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lr_decay_step: [5, 20, 40, 70]
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lr_init: 0.003
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max_grad_norm: 5
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optimizer:
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lr: 0.01
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type: Adam
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weight_decay: 0.0
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real_value: True
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scheduler:
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type:
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warmup_ratio: 0.0
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seed: 10
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weight_decay: 0
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trainer:
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disp_freq: 50
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local_entropy:
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alpha: 0.75
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eps: 0.0001
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gamma: 0.03
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inc_factor: 1.0
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log_dir: ./
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sam:
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adaptive: False
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eta: 0.0
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rho: 1.0
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type: trafficflowtrainer
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val_freq: 100000000
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use_gpu: True
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verbose: 1
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vertical:
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use: False
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wandb:
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use: False
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2024-11-14 12:00:28,965 (utils:147) INFO: The device information file is not provided
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2024-11-14 12:00:29,155 (fed_runner:173) INFO: Server has been set up ...
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2024-11-14 12:00:29,203 (fed_runner:225) INFO: Client 1 has been set up ...
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2024-11-14 12:00:29,220 (fed_runner:225) INFO: Client 2 has been set up ...
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2024-11-14 12:00:29,237 (fed_runner:225) INFO: Client 3 has been set up ...
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2024-11-14 12:00:29,254 (fed_runner:225) INFO: Client 4 has been set up ...
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2024-11-14 12:00:29,270 (fed_runner:225) INFO: Client 5 has been set up ...
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2024-11-14 12:00:29,290 (fed_runner:225) INFO: Client 6 has been set up ...
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2024-11-14 12:00:29,307 (fed_runner:225) INFO: Client 7 has been set up ...
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2024-11-14 12:00:29,323 (fed_runner:225) INFO: Client 8 has been set up ...
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2024-11-14 12:00:29,340 (fed_runner:225) INFO: Client 9 has been set up ...
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2024-11-14 12:00:29,357 (fed_runner:225) INFO: Client 10 has been set up ...
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2024-11-14 12:00:29,358 (trainer:345) INFO: Model meta-info: <class 'federatedscope.trafficflow.model.FedDGCN.FedDGCN'>.
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2024-11-14 12:00:29,360 (trainer:353) INFO: Num of original para names: 50.
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2024-11-14 12:00:29,361 (trainer:354) INFO: Num of original trainable para names: 50.
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2024-11-14 12:00:29,361 (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.update.bias_pool', 'encoder2.DGCRM_cells.0.gate.fc.fc3.weight', 'end_conv1.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc3.weight', 'encoder2.DGCRM_cells.0.gate.weights_pool', 'encoder1.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc3.bias', 'D_i_W_emb', 'encoder2.DGCRM_cells.0.update.weights_pool', 'end_conv1.bias', 'encoder1.DGCRM_cells.0.update.weights_pool', 'end_conv3.bias', 'encoder2.DGCRM_cells.0.gate.bias_pool', 'encoder1.DGCRM_cells.0.gate.bias', 'encoder1.DGCRM_cells.0.update.weights', 'encoder1.DGCRM_cells.0.update.fc.fc1.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder1.DGCRM_cells.0.update.fc.fc3.weight', 'encoder2.DGCRM_cells.0.update.fc.fc2.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc2.weight', 'encoder2.DGCRM_cells.0.update.fc.fc3.weight', 'encoder1.DGCRM_cells.0.update.fc.fc1.weight', 'encoder2.DGCRM_cells.0.update.fc.fc1.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc2.weight', 'encoder1.DGCRM_cells.0.update.fc.fc2.bias', 'encoder2.DGCRM_cells.0.update.fc.fc1.bias', 'end_conv2.bias', 'end_conv2.weight', 'end_conv3.weight', 'encoder1.DGCRM_cells.0.gate.weights_pool', 'encoder1.DGCRM_cells.0.update.fc.fc3.bias', 'encoder1.DGCRM_cells.0.update.fc.fc2.weight', 'node_embeddings1', 'encoder2.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder1.DGCRM_cells.0.gate.weights', 'encoder2.DGCRM_cells.0.update.fc.fc3.bias', 'encoder2.DGCRM_cells.0.gate.bias', 'encoder2.DGCRM_cells.0.gate.weights', 'encoder1.DGCRM_cells.0.update.bias', 'encoder2.DGCRM_cells.0.update.bias', 'T_i_D_emb', 'encoder1.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc3.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc2.bias', 'encoder2.DGCRM_cells.0.update.fc.fc2.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc2.bias', 'encoder2.DGCRM_cells.0.update.weights', 'encoder1.DGCRM_cells.0.update.bias_pool', 'encoder1.DGCRM_cells.0.gate.bias_pool', 'node_embeddings2'}.
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2024-11-14 12:00:29,361 (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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2024-11-14 12:00:29,363 (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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2024-11-14 12:00:29,404 (server:843) INFO: ----------- Starting training (Round #0) -------------
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2024-11-14 12:01:27,636 (client:354) INFO: {'Role': 'Client #9', 'Round': 0, 'Results_raw': {'train_loss': 47.864444, 'val_loss': 37.10342, 'test_loss': 37.770994}}
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2024-11-14 12:02:24,798 (client:354) INFO: {'Role': 'Client #3', 'Round': 0, 'Results_raw': {'train_loss': 44.834297, 'val_loss': 37.113003, 'test_loss': 36.595015}}
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2024-11-14 12:03:21,480 (client:354) INFO: {'Role': 'Client #6', 'Round': 0, 'Results_raw': {'train_loss': 46.413283, 'val_loss': 34.87648, 'test_loss': 36.12816}}
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2024-11-14 12:04:21,612 (client:354) INFO: {'Role': 'Client #7', 'Round': 0, 'Results_raw': {'train_loss': 45.035706, 'val_loss': 34.393102, 'test_loss': 34.631281}}
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2024-11-14 12:05:36,198 (client:354) INFO: {'Role': 'Client #4', 'Round': 0, 'Results_raw': {'train_loss': 49.18938, 'val_loss': 38.177213, 'test_loss': 37.091178}}
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2024-11-14 12:06:50,568 (client:354) INFO: {'Role': 'Client #2', 'Round': 0, 'Results_raw': {'train_loss': 41.903577, 'val_loss': 32.187559, 'test_loss': 32.547043}}
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2024-11-14 12:08:05,667 (client:354) INFO: {'Role': 'Client #1', 'Round': 0, 'Results_raw': {'train_loss': 47.482478, 'val_loss': 36.80912, 'test_loss': 37.28215}}
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2024-11-14 12:09:19,060 (client:354) INFO: {'Role': 'Client #8', 'Round': 0, 'Results_raw': {'train_loss': 44.384128, 'val_loss': 35.281225, 'test_loss': 35.770979}}
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2024-11-14 12:10:32,708 (client:354) INFO: {'Role': 'Client #5', 'Round': 0, 'Results_raw': {'train_loss': 46.635268, 'val_loss': 35.810034, 'test_loss': 36.586624}}
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2024-11-14 12:11:42,664 (client:354) INFO: {'Role': 'Client #10', 'Round': 0, 'Results_raw': {'train_loss': 46.735159, 'val_loss': 36.152974, 'test_loss': 36.791216}}
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2024-11-14 12:11:42,709 (server:353) INFO: Server: Starting evaluation at the end of round 0.
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2024-11-14 12:11:42,709 (server:359) INFO: ----------- Starting a new training round (Round #1) -------------
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2024-11-14 12:15:06,076 (client:354) INFO: {'Role': 'Client #6', 'Round': 1, 'Results_raw': {'train_loss': 39.49293, 'val_loss': 33.424784, 'test_loss': 35.238539}}
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2024-11-14 12:16:16,084 (client:354) INFO: {'Role': 'Client #4', 'Round': 1, 'Results_raw': {'train_loss': 41.708594, 'val_loss': 38.347738, 'test_loss': 37.442513}}
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2024-11-14 12:17:26,120 (client:354) INFO: {'Role': 'Client #5', 'Round': 1, 'Results_raw': {'train_loss': 38.465869, 'val_loss': 34.769573, 'test_loss': 35.799045}}
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2024-11-14 12:18:37,016 (client:354) INFO: {'Role': 'Client #8', 'Round': 1, 'Results_raw': {'train_loss': 37.88872, 'val_loss': 34.304084, 'test_loss': 34.939012}}
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2024-11-14 12:19:47,385 (client:354) INFO: {'Role': 'Client #7', 'Round': 1, 'Results_raw': {'train_loss': 37.406941, 'val_loss': 33.84831, 'test_loss': 34.391431}}
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2024-11-14 12:20:59,855 (client:354) INFO: {'Role': 'Client #9', 'Round': 1, 'Results_raw': {'train_loss': 40.746624, 'val_loss': 36.18986, 'test_loss': 36.902732}}
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2024-11-14 12:22:09,731 (client:354) INFO: {'Role': 'Client #10', 'Round': 1, 'Results_raw': {'train_loss': 39.01719, 'val_loss': 34.41296, 'test_loss': 35.123734}}
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2024-11-14 12:23:18,558 (client:354) INFO: {'Role': 'Client #3', 'Round': 1, 'Results_raw': {'train_loss': 37.340418, 'val_loss': 35.728045, 'test_loss': 35.317533}}
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2024-11-14 12:24:27,640 (client:354) INFO: {'Role': 'Client #2', 'Round': 1, 'Results_raw': {'train_loss': 34.387349, 'val_loss': 31.009053, 'test_loss': 31.515943}}
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2024-11-14 12:25:38,392 (client:354) INFO: {'Role': 'Client #1', 'Round': 1, 'Results_raw': {'train_loss': 39.268965, 'val_loss': 35.637398, 'test_loss': 36.066444}}
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2024-11-14 12:25:38,396 (server:615) INFO: {'Role': 'Server #', 'Round': 0, 'Results_weighted_avg': {'test_avg_loss': np.float64(62.465753), 'test_loss': np.float64(351807.118823), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(62.02242), 'val_loss': np.float64(349310.269849), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(62.465753), 'test_loss': np.float64(351807.118823), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(62.02242), 'val_loss': np.float64(349310.269849), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(2.093126), 'test_avg_loss_bottom_decile': np.float64(59.462675), 'test_avg_loss_top_decile': np.float64(65.589721), 'test_avg_loss_min': np.float64(59.135069), 'test_avg_loss_max': np.float64(65.589721), 'test_avg_loss_bottom10%': np.float64(59.135069), 'test_avg_loss_top10%': np.float64(65.589721), 'test_avg_loss_cos1': np.float64(0.999439), 'test_avg_loss_entropy': np.float64(2.302022), 'test_loss_std': np.float64(11788.487673), 'test_loss_bottom_decile': np.float64(334893.783203), 'test_loss_top_decile': np.float64(369401.309326), 'test_loss_min': np.float64(333048.708008), 'test_loss_max': np.float64(369401.309326), 'test_loss_bottom10%': np.float64(333048.708008), 'test_loss_top10%': np.float64(369401.309326), 'test_loss_cos1': np.float64(0.999439), 'test_loss_entropy': np.float64(2.302022), 'val_avg_loss_std': np.float64(2.404917), 'val_avg_loss_bottom_decile': np.float64(58.541015), 'val_avg_loss_top_decile': np.float64(65.678931), 'val_avg_loss_min': np.float64(58.228535), 'val_avg_loss_max': np.float64(65.678931), 'val_avg_loss_bottom10%': np.float64(58.228535), 'val_avg_loss_top10%': np.float64(65.678931), 'val_avg_loss_cos1': np.float64(0.999249), 'val_avg_loss_entropy': np.float64(2.301832), 'val_loss_std': np.float64(13544.491431), 'val_loss_bottom_decile': np.float64(329702.998291), 'val_loss_top_decile': np.float64(369903.739258), 'val_loss_min': np.float64(327943.106445), 'val_loss_max': np.float64(369903.739258), 'val_loss_bottom10%': np.float64(327943.106445), 'val_loss_top10%': np.float64(369903.739258), 'val_loss_cos1': np.float64(0.999249), 'val_loss_entropy': np.float64(2.301832)}}
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2024-11-14 12:25:38,440 (server:353) INFO: Server: Starting evaluation at the end of round 1.
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2024-11-14 12:25:38,441 (server:359) INFO: ----------- Starting a new training round (Round #2) -------------
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2024-11-14 12:29:18,773 (client:354) INFO: {'Role': 'Client #2', 'Round': 2, 'Results_raw': {'train_loss': 32.514877, 'val_loss': 30.264564, 'test_loss': 30.537325}}
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2024-11-14 12:30:28,167 (client:354) INFO: {'Role': 'Client #7', 'Round': 2, 'Results_raw': {'train_loss': 36.140662, 'val_loss': 32.668633, 'test_loss': 33.075147}}
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2024-11-14 12:31:38,836 (client:354) INFO: {'Role': 'Client #3', 'Round': 2, 'Results_raw': {'train_loss': 35.907093, 'val_loss': 35.138906, 'test_loss': 34.736051}}
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2024-11-14 12:32:48,571 (client:354) INFO: {'Role': 'Client #6', 'Round': 2, 'Results_raw': {'train_loss': 37.903983, 'val_loss': 33.116198, 'test_loss': 34.491085}}
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2024-11-14 12:33:58,698 (client:354) INFO: {'Role': 'Client #8', 'Round': 2, 'Results_raw': {'train_loss': 36.639776, 'val_loss': 33.487769, 'test_loss': 34.050598}}
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2024-11-14 12:35:09,837 (client:354) INFO: {'Role': 'Client #9', 'Round': 2, 'Results_raw': {'train_loss': 39.23415, 'val_loss': 35.695453, 'test_loss': 36.800876}}
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2024-11-14 12:36:20,910 (client:354) INFO: {'Role': 'Client #10', 'Round': 2, 'Results_raw': {'train_loss': 37.28967, 'val_loss': 34.125093, 'test_loss': 34.407355}}
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2024-11-14 12:37:32,069 (client:354) INFO: {'Role': 'Client #1', 'Round': 2, 'Results_raw': {'train_loss': 37.997292, 'val_loss': 35.530733, 'test_loss': 35.843819}}
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2024-11-14 12:38:42,817 (client:354) INFO: {'Role': 'Client #4', 'Round': 2, 'Results_raw': {'train_loss': 40.357799, 'val_loss': 37.429316, 'test_loss': 36.426}}
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2024-11-14 12:39:53,120 (client:354) INFO: {'Role': 'Client #5', 'Round': 2, 'Results_raw': {'train_loss': 36.679133, 'val_loss': 34.356954, 'test_loss': 35.31947}}
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2024-11-14 12:39:53,123 (server:615) INFO: {'Role': 'Server #', 'Round': 1, 'Results_weighted_avg': {'test_avg_loss': np.float64(47.789062), 'test_loss': np.float64(269147.998584), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(47.475556), 'val_loss': np.float64(267382.330469), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(47.789062), 'test_loss': np.float64(269147.998584), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(47.475556), 'val_loss': np.float64(267382.330469), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.917542), 'test_avg_loss_bottom_decile': np.float64(46.605748), 'test_avg_loss_top_decile': np.float64(50.41626), 'test_avg_loss_min': np.float64(43.440247), 'test_avg_loss_max': np.float64(50.41626), 'test_avg_loss_bottom10%': np.float64(43.440247), 'test_avg_loss_top10%': np.float64(50.41626), 'test_avg_loss_cos1': np.float64(0.999196), 'test_avg_loss_entropy': np.float64(2.301772), 'test_loss_std': np.float64(10799.594552), 'test_loss_bottom_decile': np.float64(262483.572754), 'test_loss_top_decile': np.float64(283944.37561), 'test_loss_min': np.float64(244655.469482), 'test_loss_max': np.float64(283944.37561), 'test_loss_bottom10%': np.float64(244655.469482), 'test_loss_top10%': np.float64(283944.37561), 'test_loss_cos1': np.float64(0.999196), 'test_loss_entropy': np.float64(2.301772), 'val_avg_loss_std': np.float64(2.18805), 'val_avg_loss_bottom_decile': np.float64(45.800846), 'val_avg_loss_top_decile': np.float64(51.127811), 'val_avg_loss_min': np.float64(42.941007), 'val_avg_loss_max': np.float64(51.127811), 'val_avg_loss_bottom10%': np.float64(42.941007), 'val_avg_loss_top10%': np.float64(51.127811), 'val_avg_loss_cos1': np.float64(0.99894), 'val_avg_loss_entropy': np.float64(2.301516), 'val_loss_std': np.float64(12323.097787), 'val_loss_bottom_decile': np.float64(257950.36499), 'val_loss_top_decile': np.float64(287951.828979), 'val_loss_min': np.float64(241843.753662), 'val_loss_max': np.float64(287951.828979), 'val_loss_bottom10%': np.float64(241843.753662), 'val_loss_top10%': np.float64(287951.828979), 'val_loss_cos1': np.float64(0.99894), 'val_loss_entropy': np.float64(2.301516)}}
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2024-11-14 12:39:53,160 (server:353) INFO: Server: Starting evaluation at the end of round 2.
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2024-11-14 12:39:53,160 (server:359) INFO: ----------- Starting a new training round (Round #3) -------------
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2024-11-14 12:43:32,658 (client:354) INFO: {'Role': 'Client #1', 'Round': 3, 'Results_raw': {'train_loss': 36.66443, 'val_loss': 34.856378, 'test_loss': 35.385745}}
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2024-11-14 12:44:48,106 (client:354) INFO: {'Role': 'Client #8', 'Round': 3, 'Results_raw': {'train_loss': 35.41588, 'val_loss': 33.333435, 'test_loss': 34.123488}}
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2024-11-14 12:46:03,536 (client:354) INFO: {'Role': 'Client #6', 'Round': 3, 'Results_raw': {'train_loss': 36.756995, 'val_loss': 32.674068, 'test_loss': 34.668701}}
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2024-11-14 12:47:17,766 (client:354) INFO: {'Role': 'Client #3', 'Round': 3, 'Results_raw': {'train_loss': 34.374485, 'val_loss': 34.808091, 'test_loss': 34.87608}}
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2024-11-14 12:48:27,553 (client:354) INFO: {'Role': 'Client #4', 'Round': 3, 'Results_raw': {'train_loss': 39.238132, 'val_loss': 37.16827, 'test_loss': 36.097889}}
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2024-11-14 12:49:36,268 (client:354) INFO: {'Role': 'Client #10', 'Round': 3, 'Results_raw': {'train_loss': 36.241772, 'val_loss': 33.799938, 'test_loss': 34.31472}}
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2024-11-14 12:50:49,166 (client:354) INFO: {'Role': 'Client #7', 'Round': 3, 'Results_raw': {'train_loss': 35.04686, 'val_loss': 32.60299, 'test_loss': 33.093689}}
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2024-11-14 12:51:59,156 (client:354) INFO: {'Role': 'Client #5', 'Round': 3, 'Results_raw': {'train_loss': 35.457576, 'val_loss': 33.731047, 'test_loss': 34.754645}}
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2024-11-14 12:53:10,106 (client:354) INFO: {'Role': 'Client #9', 'Round': 3, 'Results_raw': {'train_loss': 38.013485, 'val_loss': 35.402825, 'test_loss': 36.295243}}
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2024-11-14 12:54:21,589 (client:354) INFO: {'Role': 'Client #2', 'Round': 3, 'Results_raw': {'train_loss': 31.50378, 'val_loss': 29.553148, 'test_loss': 29.965905}}
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2024-11-14 12:54:21,594 (server:615) INFO: {'Role': 'Server #', 'Round': 2, 'Results_weighted_avg': {'test_avg_loss': np.float64(45.512054), 'test_loss': np.float64(256323.888635), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(45.162815), 'val_loss': np.float64(254356.972083), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(45.512054), 'test_loss': np.float64(256323.888635), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(45.162815), 'val_loss': np.float64(254356.972083), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.970168), 'test_avg_loss_bottom_decile': np.float64(44.251267), 'test_avg_loss_top_decile': np.float64(48.191273), 'test_avg_loss_min': np.float64(40.968226), 'test_avg_loss_max': np.float64(48.191273), 'test_avg_loss_bottom10%': np.float64(40.968226), 'test_avg_loss_top10%': np.float64(48.191273), 'test_avg_loss_cos1': np.float64(0.999064), 'test_avg_loss_entropy': np.float64(2.301636), 'test_loss_std': np.float64(11095.985922), 'test_loss_bottom_decile': np.float64(249223.136719), 'test_loss_top_decile': np.float64(271413.248413), 'test_loss_min': np.float64(230733.050049), 'test_loss_max': np.float64(271413.248413), 'test_loss_bottom10%': np.float64(230733.050049), 'test_loss_top10%': np.float64(271413.248413), 'test_loss_cos1': np.float64(0.999064), 'test_loss_entropy': np.float64(2.301636), 'val_avg_loss_std': np.float64(2.227856), 'val_avg_loss_bottom_decile': np.float64(43.410599), 'val_avg_loss_top_decile': np.float64(48.921038), 'val_avg_loss_min': np.float64(40.388795), 'val_avg_loss_max': np.float64(48.921038), 'val_avg_loss_bottom10%': np.float64(40.388795), 'val_avg_loss_top10%': np.float64(48.921038), 'val_avg_loss_cos1': np.float64(0.998786), 'val_avg_loss_entropy': np.float64(2.301358), 'val_loss_std': np.float64(12547.282835), 'val_loss_bottom_decile': np.float64(244488.49231), 'val_loss_top_decile': np.float64(275523.283325), 'val_loss_min': np.float64(227469.692749), 'val_loss_max': np.float64(275523.283325), 'val_loss_bottom10%': np.float64(227469.692749), 'val_loss_top10%': np.float64(275523.283325), 'val_loss_cos1': np.float64(0.998786), 'val_loss_entropy': np.float64(2.301358)}}
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2024-11-14 12:54:21,647 (server:353) INFO: Server: Starting evaluation at the end of round 3.
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2024-11-14 12:54:21,647 (server:359) INFO: ----------- Starting a new training round (Round #4) -------------
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2024-11-14 12:57:59,680 (client:354) INFO: {'Role': 'Client #3', 'Round': 4, 'Results_raw': {'train_loss': 33.889023, 'val_loss': 34.58782, 'test_loss': 34.850207}}
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2024-11-14 12:59:09,861 (client:354) INFO: {'Role': 'Client #1', 'Round': 4, 'Results_raw': {'train_loss': 35.8925, 'val_loss': 34.290638, 'test_loss': 35.055957}}
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2024-11-14 13:00:21,791 (client:354) INFO: {'Role': 'Client #10', 'Round': 4, 'Results_raw': {'train_loss': 35.492486, 'val_loss': 33.34334, 'test_loss': 34.129046}}
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2024-11-14 13:01:31,697 (client:354) INFO: {'Role': 'Client #6', 'Round': 4, 'Results_raw': {'train_loss': 35.944353, 'val_loss': 32.361588, 'test_loss': 34.30013}}
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2024-11-14 13:02:42,405 (client:354) INFO: {'Role': 'Client #5', 'Round': 4, 'Results_raw': {'train_loss': 34.785025, 'val_loss': 33.659166, 'test_loss': 34.745823}}
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2024-11-14 13:03:52,101 (client:354) INFO: {'Role': 'Client #2', 'Round': 4, 'Results_raw': {'train_loss': 30.62467, 'val_loss': 29.068181, 'test_loss': 29.795313}}
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2024-11-14 13:05:03,622 (client:354) INFO: {'Role': 'Client #8', 'Round': 4, 'Results_raw': {'train_loss': 34.74467, 'val_loss': 33.265908, 'test_loss': 34.057606}}
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2024-11-14 13:06:15,434 (client:354) INFO: {'Role': 'Client #9', 'Round': 4, 'Results_raw': {'train_loss': 37.262109, 'val_loss': 35.007659, 'test_loss': 36.05342}}
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2024-11-14 13:07:32,574 (client:354) INFO: {'Role': 'Client #7', 'Round': 4, 'Results_raw': {'train_loss': 34.274644, 'val_loss': 33.251892, 'test_loss': 33.676744}}
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2024-11-14 13:08:47,654 (client:354) INFO: {'Role': 'Client #4', 'Round': 4, 'Results_raw': {'train_loss': 38.670098, 'val_loss': 37.028785, 'test_loss': 35.910913}}
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2024-11-14 13:08:47,658 (server:615) INFO: {'Role': 'Server #', 'Round': 3, 'Results_weighted_avg': {'test_avg_loss': np.float64(44.552656), 'test_loss': np.float64(250920.56012), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(44.142713), 'val_loss': np.float64(248611.761267), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(44.552656), 'test_loss': np.float64(250920.56012), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(44.142713), 'val_loss': np.float64(248611.761267), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.940176), 'test_avg_loss_bottom_decile': np.float64(43.423962), 'test_avg_loss_top_decile': np.float64(47.111311), 'test_avg_loss_min': np.float64(39.986191), 'test_avg_loss_max': np.float64(47.111311), 'test_avg_loss_bottom10%': np.float64(39.986191), 'test_avg_loss_top10%': np.float64(47.111311), 'test_avg_loss_cos1': np.float64(0.999053), 'test_avg_loss_entropy': np.float64(2.301624), 'test_loss_std': np.float64(10927.071559), 'test_loss_bottom_decile': np.float64(244563.751343), 'test_loss_top_decile': np.float64(265330.904053), 'test_loss_min': np.float64(225202.228149), 'test_loss_max': np.float64(265330.904053), 'test_loss_bottom10%': np.float64(225202.228149), 'test_loss_top10%': np.float64(265330.904053), 'test_loss_cos1': np.float64(0.999053), 'test_loss_entropy': np.float64(2.301624), 'val_avg_loss_std': np.float64(2.227654), 'val_avg_loss_bottom_decile': np.float64(42.543458), 'val_avg_loss_top_decile': np.float64(47.957975), 'val_avg_loss_min': np.float64(39.34389), 'val_avg_loss_max': np.float64(47.957975), 'val_avg_loss_bottom10%': np.float64(39.34389), 'val_avg_loss_top10%': np.float64(47.957975), 'val_avg_loss_cos1': np.float64(0.998729), 'val_avg_loss_entropy': np.float64(2.301301), 'val_loss_std': np.float64(12546.144729), 'val_loss_bottom_decile': np.float64(239604.755005), 'val_loss_top_decile': np.float64(270099.313843), 'val_loss_min': np.float64(221584.789062), 'val_loss_max': np.float64(270099.313843), 'val_loss_bottom10%': np.float64(221584.789062), 'val_loss_top10%': np.float64(270099.313843), 'val_loss_cos1': np.float64(0.998729), 'val_loss_entropy': np.float64(2.301301)}}
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2024-11-14 13:08:47,716 (server:353) INFO: Server: Starting evaluation at the end of round 4.
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2024-11-14 13:08:47,717 (server:359) INFO: ----------- Starting a new training round (Round #5) -------------
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2024-11-14 13:12:32,097 (client:354) INFO: {'Role': 'Client #2', 'Round': 5, 'Results_raw': {'train_loss': 30.232468, 'val_loss': 28.84496, 'test_loss': 29.37221}}
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2024-11-14 13:13:49,036 (client:354) INFO: {'Role': 'Client #5', 'Round': 5, 'Results_raw': {'train_loss': 34.231254, 'val_loss': 32.85264, 'test_loss': 33.778112}}
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2024-11-14 13:15:04,333 (client:354) INFO: {'Role': 'Client #10', 'Round': 5, 'Results_raw': {'train_loss': 34.959177, 'val_loss': 33.137304, 'test_loss': 33.546468}}
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2024-11-14 13:16:24,524 (client:354) INFO: {'Role': 'Client #6', 'Round': 5, 'Results_raw': {'train_loss': 35.562, 'val_loss': 32.17564, 'test_loss': 34.291074}}
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2024-11-14 13:17:35,261 (client:354) INFO: {'Role': 'Client #4', 'Round': 5, 'Results_raw': {'train_loss': 38.253674, 'val_loss': 36.292295, 'test_loss': 35.34311}}
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2024-11-14 13:18:44,904 (client:354) INFO: {'Role': 'Client #1', 'Round': 5, 'Results_raw': {'train_loss': 35.367539, 'val_loss': 34.258496, 'test_loss': 34.884407}}
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2024-11-14 13:19:55,239 (client:354) INFO: {'Role': 'Client #9', 'Round': 5, 'Results_raw': {'train_loss': 36.813986, 'val_loss': 34.949037, 'test_loss': 35.695917}}
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2024-11-14 13:21:06,861 (client:354) INFO: {'Role': 'Client #8', 'Round': 5, 'Results_raw': {'train_loss': 34.300424, 'val_loss': 32.8816, 'test_loss': 33.494089}}
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2024-11-14 13:22:20,858 (client:354) INFO: {'Role': 'Client #7', 'Round': 5, 'Results_raw': {'train_loss': 33.843041, 'val_loss': 31.658459, 'test_loss': 31.981514}}
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2024-11-14 13:23:31,620 (client:354) INFO: {'Role': 'Client #3', 'Round': 5, 'Results_raw': {'train_loss': 33.389279, 'val_loss': 33.975252, 'test_loss': 34.204361}}
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2024-11-14 13:23:31,623 (server:615) INFO: {'Role': 'Server #', 'Round': 4, 'Results_weighted_avg': {'test_avg_loss': np.float64(43.809333), 'test_loss': np.float64(246734.164636), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(43.362122), 'val_loss': np.float64(244215.469238), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(43.809333), 'test_loss': np.float64(246734.164636), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(43.362122), 'val_loss': np.float64(244215.469238), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.911613), 'test_avg_loss_bottom_decile': np.float64(42.833331), 'test_avg_loss_top_decile': np.float64(46.29989), 'test_avg_loss_min': np.float64(39.311109), 'test_avg_loss_max': np.float64(46.29989), 'test_avg_loss_bottom10%': np.float64(39.311109), 'test_avg_loss_top10%': np.float64(46.29989), 'test_avg_loss_cos1': np.float64(0.999049), 'test_avg_loss_entropy': np.float64(2.30162), 'test_loss_std': np.float64(10766.204825), 'test_loss_bottom_decile': np.float64(241237.320801), 'test_loss_top_decile': np.float64(260760.980469), 'test_loss_min': np.float64(221400.167969), 'test_loss_max': np.float64(260760.980469), 'test_loss_bottom10%': np.float64(221400.167969), 'test_loss_top10%': np.float64(260760.980469), 'test_loss_cos1': np.float64(0.999049), 'test_loss_entropy': np.float64(2.30162), 'val_avg_loss_std': np.float64(2.178144), 'val_avg_loss_bottom_decile': np.float64(41.951236), 'val_avg_loss_top_decile': np.float64(47.221869), 'val_avg_loss_min': np.float64(38.659677), 'val_avg_loss_max': np.float64(47.221869), 'val_avg_loss_bottom10%': np.float64(38.659677), 'val_avg_loss_top10%': np.float64(47.221869), 'val_avg_loss_cos1': np.float64(0.998741), 'val_avg_loss_entropy': np.float64(2.301314), 'val_loss_std': np.float64(12267.308337), 'val_loss_bottom_decile': np.float64(236269.362549), 'val_loss_top_decile': np.float64(265953.563965), 'val_loss_min': np.float64(217731.299561), 'val_loss_max': np.float64(265953.563965), 'val_loss_bottom10%': np.float64(217731.299561), 'val_loss_top10%': np.float64(265953.563965), 'val_loss_cos1': np.float64(0.998741), 'val_loss_entropy': np.float64(2.301314)}}
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2024-11-14 13:23:31,668 (server:353) INFO: Server: Starting evaluation at the end of round 5.
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2024-11-14 13:23:31,669 (server:359) INFO: ----------- Starting a new training round (Round #6) -------------
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2024-11-14 13:27:04,181 (client:354) INFO: {'Role': 'Client #6', 'Round': 6, 'Results_raw': {'train_loss': 35.119955, 'val_loss': 32.080753, 'test_loss': 33.686716}}
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2024-11-14 13:28:15,388 (client:354) INFO: {'Role': 'Client #5', 'Round': 6, 'Results_raw': {'train_loss': 33.84094, 'val_loss': 32.797653, 'test_loss': 33.899609}}
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2024-11-14 13:29:28,216 (client:354) INFO: {'Role': 'Client #9', 'Round': 6, 'Results_raw': {'train_loss': 36.501658, 'val_loss': 34.628333, 'test_loss': 35.958062}}
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2024-11-14 13:30:40,562 (client:354) INFO: {'Role': 'Client #2', 'Round': 6, 'Results_raw': {'train_loss': 29.837735, 'val_loss': 28.373812, 'test_loss': 28.944147}}
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2024-11-14 13:31:55,278 (client:354) INFO: {'Role': 'Client #4', 'Round': 6, 'Results_raw': {'train_loss': 37.681857, 'val_loss': 36.16587, 'test_loss': 35.304887}}
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2024-11-14 13:33:09,199 (client:354) INFO: {'Role': 'Client #8', 'Round': 6, 'Results_raw': {'train_loss': 33.888053, 'val_loss': 32.429091, 'test_loss': 33.262064}}
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2024-11-14 13:34:21,892 (client:354) INFO: {'Role': 'Client #7', 'Round': 6, 'Results_raw': {'train_loss': 33.465739, 'val_loss': 31.576533, 'test_loss': 32.015733}}
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2024-11-14 13:35:36,057 (client:354) INFO: {'Role': 'Client #1', 'Round': 6, 'Results_raw': {'train_loss': 35.028178, 'val_loss': 33.97532, 'test_loss': 34.641031}}
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2024-11-14 13:36:49,480 (client:354) INFO: {'Role': 'Client #3', 'Round': 6, 'Results_raw': {'train_loss': 33.20419, 'val_loss': 33.986063, 'test_loss': 34.311368}}
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2024-11-14 13:38:03,016 (client:354) INFO: {'Role': 'Client #10', 'Round': 6, 'Results_raw': {'train_loss': 34.508026, 'val_loss': 32.95471, 'test_loss': 33.518033}}
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2024-11-14 13:38:03,019 (server:615) INFO: {'Role': 'Server #', 'Round': 5, 'Results_weighted_avg': {'test_avg_loss': np.float64(43.330476), 'test_loss': np.float64(244037.241565), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.890235), 'val_loss': np.float64(241557.804858), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(43.330476), 'test_loss': np.float64(244037.241565), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.890235), 'val_loss': np.float64(241557.804858), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.861191), 'test_avg_loss_bottom_decile': np.float64(42.390807), 'test_avg_loss_top_decile': np.float64(45.84631), 'test_avg_loss_min': np.float64(38.988113), 'test_avg_loss_max': np.float64(45.84631), 'test_avg_loss_bottom10%': np.float64(38.988113), 'test_avg_loss_top10%': np.float64(45.84631), 'test_avg_loss_cos1': np.float64(0.999079), 'test_avg_loss_entropy': np.float64(2.301651), 'test_loss_std': np.float64(10482.227206), 'test_loss_bottom_decile': np.float64(238745.025269), 'test_loss_top_decile': np.float64(258206.415527), 'test_loss_min': np.float64(219581.051514), 'test_loss_max': np.float64(258206.415527), 'test_loss_bottom10%': np.float64(219581.051514), 'test_loss_top10%': np.float64(258206.415527), 'test_loss_cos1': np.float64(0.999079), 'test_loss_entropy': np.float64(2.301651), 'val_avg_loss_std': np.float64(2.129343), 'val_avg_loss_bottom_decile': np.float64(41.597692), 'val_avg_loss_top_decile': np.float64(46.623555), 'val_avg_loss_min': np.float64(38.325763), 'val_avg_loss_max': np.float64(46.623555), 'val_avg_loss_bottom10%': np.float64(38.325763), 'val_avg_loss_top10%': np.float64(46.623555), 'val_avg_loss_cos1': np.float64(0.99877), 'val_avg_loss_entropy': np.float64(2.301344), 'val_loss_std': np.float64(11992.458659), 'val_loss_bottom_decile': np.float64(234278.203125), 'val_loss_top_decile': np.float64(262583.861816), 'val_loss_min': np.float64(215850.698608), 'val_loss_max': np.float64(262583.861816), 'val_loss_bottom10%': np.float64(215850.698608), 'val_loss_top10%': np.float64(262583.861816), 'val_loss_cos1': np.float64(0.99877), 'val_loss_entropy': np.float64(2.301344)}}
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2024-11-14 13:38:03,051 (server:353) INFO: Server: Starting evaluation at the end of round 6.
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2024-11-14 13:38:03,052 (server:359) INFO: ----------- Starting a new training round (Round #7) -------------
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2024-11-14 13:41:48,774 (client:354) INFO: {'Role': 'Client #8', 'Round': 7, 'Results_raw': {'train_loss': 33.807648, 'val_loss': 32.603298, 'test_loss': 33.54523}}
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2024-11-14 13:43:02,208 (client:354) INFO: {'Role': 'Client #4', 'Round': 7, 'Results_raw': {'train_loss': 37.496107, 'val_loss': 36.306347, 'test_loss': 35.387162}}
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2024-11-14 13:44:17,055 (client:354) INFO: {'Role': 'Client #6', 'Round': 7, 'Results_raw': {'train_loss': 34.925108, 'val_loss': 32.040192, 'test_loss': 34.728101}}
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2024-11-14 13:45:30,816 (client:354) INFO: {'Role': 'Client #10', 'Round': 7, 'Results_raw': {'train_loss': 34.230713, 'val_loss': 32.679598, 'test_loss': 33.550819}}
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2024-11-14 13:46:43,648 (client:354) INFO: {'Role': 'Client #9', 'Round': 7, 'Results_raw': {'train_loss': 36.18366, 'val_loss': 34.630257, 'test_loss': 35.435347}}
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2024-11-14 13:47:56,643 (client:354) INFO: {'Role': 'Client #7', 'Round': 7, 'Results_raw': {'train_loss': 33.134586, 'val_loss': 31.683777, 'test_loss': 32.148236}}
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2024-11-14 13:49:09,206 (client:354) INFO: {'Role': 'Client #1', 'Round': 7, 'Results_raw': {'train_loss': 34.715274, 'val_loss': 33.807783, 'test_loss': 34.38909}}
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2024-11-14 13:50:23,164 (client:354) INFO: {'Role': 'Client #3', 'Round': 7, 'Results_raw': {'train_loss': 32.872622, 'val_loss': 34.496361, 'test_loss': 34.827457}}
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2024-11-14 13:51:36,155 (client:354) INFO: {'Role': 'Client #5', 'Round': 7, 'Results_raw': {'train_loss': 33.479071, 'val_loss': 32.638777, 'test_loss': 33.822832}}
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2024-11-14 13:52:54,626 (client:354) INFO: {'Role': 'Client #2', 'Round': 7, 'Results_raw': {'train_loss': 29.548873, 'val_loss': 28.380039, 'test_loss': 29.043868}}
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2024-11-14 13:52:54,629 (server:615) INFO: {'Role': 'Server #', 'Round': 6, 'Results_weighted_avg': {'test_avg_loss': np.float64(43.188329), 'test_loss': np.float64(243236.670178), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.724089), 'val_loss': np.float64(240622.068713), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(43.188329), 'test_loss': np.float64(243236.670178), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.724089), 'val_loss': np.float64(240622.068713), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.916879), 'test_avg_loss_bottom_decile': np.float64(42.305459), 'test_avg_loss_top_decile': np.float64(45.830726), 'test_avg_loss_min': np.float64(38.683303), 'test_avg_loss_max': np.float64(45.830726), 'test_avg_loss_bottom10%': np.float64(38.683303), 'test_avg_loss_top10%': np.float64(45.830726), 'test_avg_loss_cos1': np.float64(0.999016), 'test_avg_loss_entropy': np.float64(2.301587), 'test_loss_std': np.float64(10795.859912), 'test_loss_bottom_decile': np.float64(238264.345825), 'test_loss_top_decile': np.float64(258118.64856), 'test_loss_min': np.float64(217864.362915), 'test_loss_max': np.float64(258118.64856), 'test_loss_bottom10%': np.float64(217864.362915), 'test_loss_top10%': np.float64(258118.64856), 'test_loss_cos1': np.float64(0.999016), 'test_loss_entropy': np.float64(2.301587), 'val_avg_loss_std': np.float64(2.199228), 'val_avg_loss_bottom_decile': np.float64(41.469468), 'val_avg_loss_top_decile': np.float64(46.547299), 'val_avg_loss_min': np.float64(37.995203), 'val_avg_loss_max': np.float64(46.547299), 'val_avg_loss_bottom10%': np.float64(37.995203), 'val_avg_loss_top10%': np.float64(46.547299), 'val_avg_loss_cos1': np.float64(0.998678), 'val_avg_loss_entropy': np.float64(2.30125), 'val_loss_std': np.float64(12386.053495), 'val_loss_bottom_decile': np.float64(233556.045532), 'val_loss_top_decile': np.float64(262154.385254), 'val_loss_min': np.float64(213988.980957), 'val_loss_max': np.float64(262154.385254), 'val_loss_bottom10%': np.float64(213988.980957), 'val_loss_top10%': np.float64(262154.385254), 'val_loss_cos1': np.float64(0.998678), 'val_loss_entropy': np.float64(2.30125)}}
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2024-11-14 13:52:54,671 (server:353) INFO: Server: Starting evaluation at the end of round 7.
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2024-11-14 13:52:54,671 (server:359) INFO: ----------- Starting a new training round (Round #8) -------------
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2024-11-14 13:56:42,347 (client:354) INFO: {'Role': 'Client #6', 'Round': 8, 'Results_raw': {'train_loss': 34.768934, 'val_loss': 31.883327, 'test_loss': 33.310028}}
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2024-11-14 13:57:55,000 (client:354) INFO: {'Role': 'Client #7', 'Round': 8, 'Results_raw': {'train_loss': 32.871692, 'val_loss': 31.343315, 'test_loss': 31.802605}}
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2024-11-14 13:59:08,172 (client:354) INFO: {'Role': 'Client #2', 'Round': 8, 'Results_raw': {'train_loss': 29.323793, 'val_loss': 28.39856, 'test_loss': 29.04418}}
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2024-11-14 14:00:21,829 (client:354) INFO: {'Role': 'Client #4', 'Round': 8, 'Results_raw': {'train_loss': 37.294884, 'val_loss': 36.376967, 'test_loss': 35.564408}}
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2024-11-14 14:01:33,811 (client:354) INFO: {'Role': 'Client #1', 'Round': 8, 'Results_raw': {'train_loss': 34.503094, 'val_loss': 33.801282, 'test_loss': 34.612747}}
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2024-11-14 14:02:46,845 (client:354) INFO: {'Role': 'Client #8', 'Round': 8, 'Results_raw': {'train_loss': 33.551619, 'val_loss': 32.610954, 'test_loss': 33.47124}}
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2024-11-14 14:04:00,428 (client:354) INFO: {'Role': 'Client #5', 'Round': 8, 'Results_raw': {'train_loss': 33.355099, 'val_loss': 32.64126, 'test_loss': 33.950675}}
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2024-11-14 14:05:12,884 (client:354) INFO: {'Role': 'Client #3', 'Round': 8, 'Results_raw': {'train_loss': 32.696083, 'val_loss': 33.618578, 'test_loss': 33.95034}}
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2024-11-14 14:06:26,641 (client:354) INFO: {'Role': 'Client #10', 'Round': 8, 'Results_raw': {'train_loss': 34.103327, 'val_loss': 33.108815, 'test_loss': 33.654209}}
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2024-11-14 14:07:40,225 (client:354) INFO: {'Role': 'Client #9', 'Round': 8, 'Results_raw': {'train_loss': 35.99273, 'val_loss': 34.525737, 'test_loss': 35.617332}}
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2024-11-14 14:07:40,229 (server:615) INFO: {'Role': 'Server #', 'Round': 7, 'Results_weighted_avg': {'test_avg_loss': np.float64(43.097732), 'test_loss': np.float64(242726.427905), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.620468), 'val_loss': np.float64(240038.475366), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(43.097732), 'test_loss': np.float64(242726.427905), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.620468), 'val_loss': np.float64(240038.475366), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.935012), 'test_avg_loss_bottom_decile': np.float64(42.024997), 'test_avg_loss_top_decile': np.float64(45.77258), 'test_avg_loss_min': np.float64(38.64683), 'test_avg_loss_max': np.float64(45.77258), 'test_avg_loss_bottom10%': np.float64(38.64683), 'test_avg_loss_top10%': np.float64(45.77258), 'test_avg_loss_cos1': np.float64(0.998994), 'test_avg_loss_entropy': np.float64(2.301564), 'test_loss_std': np.float64(10897.986477), 'test_loss_bottom_decile': np.float64(236684.785034), 'test_loss_top_decile': np.float64(257791.171509), 'test_loss_min': np.float64(217658.944458), 'test_loss_max': np.float64(257791.171509), 'test_loss_bottom10%': np.float64(217658.944458), 'test_loss_top10%': np.float64(257791.171509), 'test_loss_cos1': np.float64(0.998994), 'test_loss_entropy': np.float64(2.301564), 'val_avg_loss_std': np.float64(2.219758), 'val_avg_loss_bottom_decile': np.float64(41.186712), 'val_avg_loss_top_decile': np.float64(46.490446), 'val_avg_loss_min': np.float64(37.934626), 'val_avg_loss_max': np.float64(46.490446), 'val_avg_loss_bottom10%': np.float64(37.934626), 'val_avg_loss_top10%': np.float64(46.490446), 'val_avg_loss_cos1': np.float64(0.998646), 'val_avg_loss_entropy': np.float64(2.301219), 'val_loss_std': np.float64(12501.675943), 'val_loss_bottom_decile': np.float64(231963.561035), 'val_loss_top_decile': np.float64(261834.189209), 'val_loss_min': np.float64(213647.811401), 'val_loss_max': np.float64(261834.189209), 'val_loss_bottom10%': np.float64(213647.811401), 'val_loss_top10%': np.float64(261834.189209), 'val_loss_cos1': np.float64(0.998646), 'val_loss_entropy': np.float64(2.301219)}}
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2024-11-14 14:07:40,274 (server:353) INFO: Server: Starting evaluation at the end of round 8.
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2024-11-14 14:07:40,275 (server:359) INFO: ----------- Starting a new training round (Round #9) -------------
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2024-11-14 14:11:24,987 (client:354) INFO: {'Role': 'Client #2', 'Round': 9, 'Results_raw': {'train_loss': 29.093235, 'val_loss': 28.060002, 'test_loss': 28.695494}}
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2024-11-14 14:12:38,927 (client:354) INFO: {'Role': 'Client #9', 'Round': 9, 'Results_raw': {'train_loss': 35.790404, 'val_loss': 34.243637, 'test_loss': 36.002658}}
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2024-11-14 14:13:51,588 (client:354) INFO: {'Role': 'Client #8', 'Round': 9, 'Results_raw': {'train_loss': 33.370828, 'val_loss': 32.782122, 'test_loss': 33.791863}}
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2024-11-14 14:15:04,500 (client:354) INFO: {'Role': 'Client #5', 'Round': 9, 'Results_raw': {'train_loss': 33.125932, 'val_loss': 32.333736, 'test_loss': 33.38148}}
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2024-11-14 14:16:17,918 (client:354) INFO: {'Role': 'Client #1', 'Round': 9, 'Results_raw': {'train_loss': 34.306569, 'val_loss': 33.66984, 'test_loss': 34.256579}}
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2024-11-14 14:17:32,090 (client:354) INFO: {'Role': 'Client #4', 'Round': 9, 'Results_raw': {'train_loss': 37.042261, 'val_loss': 35.89784, 'test_loss': 35.114072}}
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2024-11-14 14:18:43,990 (client:354) INFO: {'Role': 'Client #3', 'Round': 9, 'Results_raw': {'train_loss': 32.446333, 'val_loss': 33.414458, 'test_loss': 33.688366}}
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2024-11-14 14:20:00,398 (client:354) INFO: {'Role': 'Client #6', 'Round': 9, 'Results_raw': {'train_loss': 34.502649, 'val_loss': 31.806179, 'test_loss': 33.454578}}
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2024-11-14 14:21:14,165 (client:354) INFO: {'Role': 'Client #10', 'Round': 9, 'Results_raw': {'train_loss': 33.802368, 'val_loss': 32.707409, 'test_loss': 33.240522}}
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2024-11-14 14:22:27,535 (client:354) INFO: {'Role': 'Client #7', 'Round': 9, 'Results_raw': {'train_loss': 32.720018, 'val_loss': 31.344601, 'test_loss': 31.771379}}
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2024-11-14 14:22:27,538 (server:615) INFO: {'Role': 'Server #', 'Round': 8, 'Results_weighted_avg': {'test_avg_loss': np.float64(42.721052), 'test_loss': np.float64(240604.965112), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.2651), 'val_loss': np.float64(238037.040674), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(42.721052), 'test_loss': np.float64(240604.965112), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.2651), 'val_loss': np.float64(238037.040674), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.949983), 'test_avg_loss_bottom_decile': np.float64(41.527547), 'test_avg_loss_top_decile': np.float64(45.675132), 'test_avg_loss_min': np.float64(38.313959), 'test_avg_loss_max': np.float64(45.675132), 'test_avg_loss_bottom10%': np.float64(38.313959), 'test_avg_loss_top10%': np.float64(45.675132), 'test_avg_loss_cos1': np.float64(0.99896), 'test_avg_loss_entropy': np.float64(2.301532), 'test_loss_std': np.float64(10982.302389), 'test_loss_bottom_decile': np.float64(233883.143066), 'test_loss_top_decile': np.float64(257242.344727), 'test_loss_min': np.float64(215784.215942), 'test_loss_max': np.float64(257242.344727), 'test_loss_bottom10%': np.float64(215784.215942), 'test_loss_top10%': np.float64(257242.344727), 'test_loss_cos1': np.float64(0.99896), 'test_loss_entropy': np.float64(2.301532), 'val_avg_loss_std': np.float64(2.216004), 'val_avg_loss_bottom_decile': np.float64(41.032039), 'val_avg_loss_top_decile': np.float64(45.978608), 'val_avg_loss_min': np.float64(37.632039), 'val_avg_loss_max': np.float64(45.978608), 'val_avg_loss_bottom10%': np.float64(37.632039), 'val_avg_loss_top10%': np.float64(45.978608), 'val_avg_loss_cos1': np.float64(0.998628), 'val_avg_loss_entropy': np.float64(2.301201), 'val_loss_std': np.float64(12480.537044), 'val_loss_bottom_decile': np.float64(231092.443481), 'val_loss_top_decile': np.float64(258951.521362), 'val_loss_min': np.float64(211943.64209), 'val_loss_max': np.float64(258951.521362), 'val_loss_bottom10%': np.float64(211943.64209), 'val_loss_top10%': np.float64(258951.521362), 'val_loss_cos1': np.float64(0.998628), 'val_loss_entropy': np.float64(2.301201)}}
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2024-11-14 14:22:27,586 (server:353) INFO: Server: Starting evaluation at the end of round 9.
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2024-11-14 14:22:27,586 (server:359) INFO: ----------- Starting a new training round (Round #10) -------------
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2024-11-14 14:26:06,234 (client:354) INFO: {'Role': 'Client #6', 'Round': 10, 'Results_raw': {'train_loss': 34.284211, 'val_loss': 31.504839, 'test_loss': 33.549621}}
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2024-11-14 14:27:22,624 (client:354) INFO: {'Role': 'Client #10', 'Round': 10, 'Results_raw': {'train_loss': 33.716915, 'val_loss': 32.286442, 'test_loss': 33.012173}}
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2024-11-14 14:28:40,066 (client:354) INFO: {'Role': 'Client #4', 'Round': 10, 'Results_raw': {'train_loss': 36.936511, 'val_loss': 35.747406, 'test_loss': 34.970344}}
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2024-11-14 14:29:54,727 (client:354) INFO: {'Role': 'Client #9', 'Round': 10, 'Results_raw': {'train_loss': 35.587815, 'val_loss': 33.999434, 'test_loss': 35.313044}}
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2024-11-14 14:31:05,670 (client:354) INFO: {'Role': 'Client #3', 'Round': 10, 'Results_raw': {'train_loss': 32.320384, 'val_loss': 33.312744, 'test_loss': 33.917802}}
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2024-11-14 14:32:12,342 (client:354) INFO: {'Role': 'Client #1', 'Round': 10, 'Results_raw': {'train_loss': 34.089495, 'val_loss': 33.415521, 'test_loss': 34.120991}}
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2024-11-14 14:33:18,166 (client:354) INFO: {'Role': 'Client #5', 'Round': 10, 'Results_raw': {'train_loss': 32.879552, 'val_loss': 32.247323, 'test_loss': 33.532155}}
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2024-11-14 14:34:25,961 (client:354) INFO: {'Role': 'Client #8', 'Round': 10, 'Results_raw': {'train_loss': 33.23495, 'val_loss': 32.143475, 'test_loss': 33.023283}}
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2024-11-14 14:35:33,468 (client:354) INFO: {'Role': 'Client #2', 'Round': 10, 'Results_raw': {'train_loss': 28.981428, 'val_loss': 27.95103, 'test_loss': 28.472299}}
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2024-11-14 14:36:45,486 (client:354) INFO: {'Role': 'Client #7', 'Round': 10, 'Results_raw': {'train_loss': 32.407317, 'val_loss': 30.919757, 'test_loss': 31.572168}}
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2024-11-14 14:36:45,489 (server:615) INFO: {'Role': 'Server #', 'Round': 9, 'Results_weighted_avg': {'test_avg_loss': np.float64(42.759883), 'test_loss': np.float64(240823.660388), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.284534), 'val_loss': np.float64(238146.493579), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(42.759883), 'test_loss': np.float64(240823.660388), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.284534), 'val_loss': np.float64(238146.493579), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.935154), 'test_avg_loss_bottom_decile': np.float64(41.454331), 'test_avg_loss_top_decile': np.float64(45.753344), 'test_avg_loss_min': np.float64(38.4737), 'test_avg_loss_max': np.float64(45.753344), 'test_avg_loss_bottom10%': np.float64(38.4737), 'test_avg_loss_top10%': np.float64(45.753344), 'test_avg_loss_cos1': np.float64(0.998978), 'test_avg_loss_entropy': np.float64(2.301551), 'test_loss_std': np.float64(10898.785457), 'test_loss_bottom_decile': np.float64(233470.792236), 'test_loss_top_decile': np.float64(257682.835815), 'test_loss_min': np.float64(216683.879028), 'test_loss_max': np.float64(257682.835815), 'test_loss_bottom10%': np.float64(216683.879028), 'test_loss_top10%': np.float64(257682.835815), 'test_loss_cos1': np.float64(0.998978), 'test_loss_entropy': np.float64(2.301551), 'val_avg_loss_std': np.float64(2.188341), 'val_avg_loss_bottom_decile': np.float64(41.029081), 'val_avg_loss_top_decile': np.float64(45.974607), 'val_avg_loss_min': np.float64(37.784188), 'val_avg_loss_max': np.float64(45.974607), 'val_avg_loss_bottom10%': np.float64(37.784188), 'val_avg_loss_top10%': np.float64(45.974607), 'val_avg_loss_cos1': np.float64(0.998664), 'val_avg_loss_entropy': np.float64(2.301238), 'val_loss_std': np.float64(12324.733761), 'val_loss_bottom_decile': np.float64(231075.784912), 'val_loss_top_decile': np.float64(258928.986328), 'val_loss_min': np.float64(212800.545898), 'val_loss_max': np.float64(258928.986328), 'val_loss_bottom10%': np.float64(212800.545898), 'val_loss_top10%': np.float64(258928.986328), 'val_loss_cos1': np.float64(0.998664), 'val_loss_entropy': np.float64(2.301238)}}
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2024-11-14 14:36:45,546 (server:353) INFO: Server: Starting evaluation at the end of round 10.
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2024-11-14 14:36:45,547 (server:359) INFO: ----------- Starting a new training round (Round #11) -------------
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2024-11-14 14:40:25,189 (client:354) INFO: {'Role': 'Client #4', 'Round': 11, 'Results_raw': {'train_loss': 36.857726, 'val_loss': 35.846857, 'test_loss': 35.034233}}
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2024-11-14 14:41:33,417 (client:354) INFO: {'Role': 'Client #9', 'Round': 11, 'Results_raw': {'train_loss': 35.435227, 'val_loss': 34.396348, 'test_loss': 35.8663}}
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2024-11-14 14:42:47,046 (client:354) INFO: {'Role': 'Client #7', 'Round': 11, 'Results_raw': {'train_loss': 32.351986, 'val_loss': 31.157699, 'test_loss': 31.672326}}
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2024-11-14 14:44:02,482 (client:354) INFO: {'Role': 'Client #5', 'Round': 11, 'Results_raw': {'train_loss': 32.757439, 'val_loss': 32.248045, 'test_loss': 33.417656}}
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2024-11-14 14:45:17,882 (client:354) INFO: {'Role': 'Client #8', 'Round': 11, 'Results_raw': {'train_loss': 33.131033, 'val_loss': 32.273376, 'test_loss': 33.135485}}
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2024-11-14 14:46:34,937 (client:354) INFO: {'Role': 'Client #10', 'Round': 11, 'Results_raw': {'train_loss': 33.495123, 'val_loss': 32.458533, 'test_loss': 33.131192}}
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2024-11-14 14:47:49,266 (client:354) INFO: {'Role': 'Client #1', 'Round': 11, 'Results_raw': {'train_loss': 33.928968, 'val_loss': 33.341879, 'test_loss': 33.871325}}
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2024-11-14 14:49:02,487 (client:354) INFO: {'Role': 'Client #6', 'Round': 11, 'Results_raw': {'train_loss': 34.281054, 'val_loss': 31.660731, 'test_loss': 33.483749}}
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2024-11-14 14:50:14,602 (client:354) INFO: {'Role': 'Client #3', 'Round': 11, 'Results_raw': {'train_loss': 32.164642, 'val_loss': 33.385336, 'test_loss': 33.881301}}
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2024-11-14 14:51:29,105 (client:354) INFO: {'Role': 'Client #2', 'Round': 11, 'Results_raw': {'train_loss': 28.850476, 'val_loss': 27.982579, 'test_loss': 28.519304}}
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2024-11-14 14:51:29,110 (server:615) INFO: {'Role': 'Server #', 'Round': 10, 'Results_weighted_avg': {'test_avg_loss': np.float64(42.559273), 'test_loss': np.float64(239693.825757), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.088828), 'val_loss': np.float64(237044.280884), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(42.559273), 'test_loss': np.float64(239693.825757), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(42.088828), 'val_loss': np.float64(237044.280884), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.941443), 'test_avg_loss_bottom_decile': np.float64(41.147628), 'test_avg_loss_top_decile': np.float64(45.581214), 'test_avg_loss_min': np.float64(38.252469), 'test_avg_loss_max': np.float64(45.581214), 'test_avg_loss_bottom10%': np.float64(38.252469), 'test_avg_loss_top10%': np.float64(45.581214), 'test_avg_loss_cos1': np.float64(0.998961), 'test_avg_loss_entropy': np.float64(2.301534), 'test_loss_std': np.float64(10934.209783), 'test_loss_bottom_decile': np.float64(231743.441406), 'test_loss_top_decile': np.float64(256713.397583), 'test_loss_min': np.float64(215437.90564), 'test_loss_max': np.float64(256713.397583), 'test_loss_bottom10%': np.float64(215437.90564), 'test_loss_top10%': np.float64(256713.397583), 'test_loss_cos1': np.float64(0.998961), 'test_loss_entropy': np.float64(2.301534), 'val_avg_loss_std': np.float64(2.188992), 'val_avg_loss_bottom_decile': np.float64(40.83013), 'val_avg_loss_top_decile': np.float64(45.688548), 'val_avg_loss_min': np.float64(37.551982), 'val_avg_loss_max': np.float64(45.688548), 'val_avg_loss_bottom10%': np.float64(37.551982), 'val_avg_loss_top10%': np.float64(45.688548), 'val_avg_loss_cos1': np.float64(0.99865), 'val_avg_loss_entropy': np.float64(2.301223), 'val_loss_std': np.float64(12328.40036), 'val_loss_bottom_decile': np.float64(229955.294922), 'val_loss_top_decile': np.float64(257317.903198), 'val_loss_min': np.float64(211492.76416), 'val_loss_max': np.float64(257317.903198), 'val_loss_bottom10%': np.float64(211492.76416), 'val_loss_top10%': np.float64(257317.903198), 'val_loss_cos1': np.float64(0.99865), 'val_loss_entropy': np.float64(2.301223)}}
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2024-11-14 14:51:29,159 (server:353) INFO: Server: Starting evaluation at the end of round 11.
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2024-11-14 14:51:29,160 (server:359) INFO: ----------- Starting a new training round (Round #12) -------------
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2024-11-14 14:55:17,867 (client:354) INFO: {'Role': 'Client #9', 'Round': 12, 'Results_raw': {'train_loss': 35.311166, 'val_loss': 33.920123, 'test_loss': 35.468427}}
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2024-11-14 14:56:32,537 (client:354) INFO: {'Role': 'Client #2', 'Round': 12, 'Results_raw': {'train_loss': 28.755085, 'val_loss': 28.120855, 'test_loss': 28.680928}}
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2024-11-14 14:57:48,388 (client:354) INFO: {'Role': 'Client #10', 'Round': 12, 'Results_raw': {'train_loss': 33.358069, 'val_loss': 32.186629, 'test_loss': 32.932921}}
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2024-11-14 14:59:00,989 (client:354) INFO: {'Role': 'Client #7', 'Round': 12, 'Results_raw': {'train_loss': 32.132337, 'val_loss': 30.868175, 'test_loss': 31.576149}}
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2024-11-14 15:00:16,682 (client:354) INFO: {'Role': 'Client #3', 'Round': 12, 'Results_raw': {'train_loss': 32.051572, 'val_loss': 33.663837, 'test_loss': 33.872646}}
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2024-11-14 15:01:36,797 (client:354) INFO: {'Role': 'Client #5', 'Round': 12, 'Results_raw': {'train_loss': 32.63598, 'val_loss': 32.197901, 'test_loss': 33.526504}}
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2024-11-14 15:02:56,941 (client:354) INFO: {'Role': 'Client #1', 'Round': 12, 'Results_raw': {'train_loss': 33.854918, 'val_loss': 33.264224, 'test_loss': 33.95766}}
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2024-11-14 15:04:17,564 (client:354) INFO: {'Role': 'Client #6', 'Round': 12, 'Results_raw': {'train_loss': 34.050527, 'val_loss': 31.811077, 'test_loss': 34.761189}}
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2024-11-14 15:05:39,668 (client:354) INFO: {'Role': 'Client #4', 'Round': 12, 'Results_raw': {'train_loss': 36.623011, 'val_loss': 35.759626, 'test_loss': 34.904598}}
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2024-11-14 15:07:00,345 (client:354) INFO: {'Role': 'Client #8', 'Round': 12, 'Results_raw': {'train_loss': 32.934748, 'val_loss': 32.232351, 'test_loss': 33.225996}}
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2024-11-14 15:07:00,353 (server:615) INFO: {'Role': 'Server #', 'Round': 11, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.99993), 'test_loss': np.float64(236543.607996), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.530736), 'val_loss': np.float64(233901.106226), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.99993), 'test_loss': np.float64(236543.607996), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.530736), 'val_loss': np.float64(233901.106226), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.999108), 'test_avg_loss_bottom_decile': np.float64(40.611885), 'test_avg_loss_top_decile': np.float64(45.14312), 'test_avg_loss_min': np.float64(37.492864), 'test_avg_loss_max': np.float64(45.14312), 'test_avg_loss_bottom10%': np.float64(37.492864), 'test_avg_loss_top10%': np.float64(45.14312), 'test_avg_loss_cos1': np.float64(0.998869), 'test_avg_loss_entropy': np.float64(2.301439), 'test_loss_std': np.float64(11258.975213), 'test_loss_bottom_decile': np.float64(228726.134644), 'test_loss_top_decile': np.float64(254246.054199), 'test_loss_min': np.float64(211159.808594), 'test_loss_max': np.float64(254246.054199), 'test_loss_bottom10%': np.float64(211159.808594), 'test_loss_top10%': np.float64(254246.054199), 'test_loss_cos1': np.float64(0.998869), 'test_loss_entropy': np.float64(2.301439), 'val_avg_loss_std': np.float64(2.234362), 'val_avg_loss_bottom_decile': np.float64(40.323819), 'val_avg_loss_top_decile': np.float64(45.205026), 'val_avg_loss_min': np.float64(36.800096), 'val_avg_loss_max': np.float64(45.205026), 'val_avg_loss_bottom10%': np.float64(36.800096), 'val_avg_loss_top10%': np.float64(45.205026), 'val_avg_loss_cos1': np.float64(0.998556), 'val_avg_loss_entropy': np.float64(2.301126), 'val_loss_std': np.float64(12583.92845), 'val_loss_bottom_decile': np.float64(227103.748047), 'val_loss_top_decile': np.float64(254594.703979), 'val_loss_min': np.float64(207258.140869), 'val_loss_max': np.float64(254594.703979), 'val_loss_bottom10%': np.float64(207258.140869), 'val_loss_top10%': np.float64(254594.703979), 'val_loss_cos1': np.float64(0.998556), 'val_loss_entropy': np.float64(2.301126)}}
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2024-11-14 15:07:00,410 (server:353) INFO: Server: Starting evaluation at the end of round 12.
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2024-11-14 15:07:00,413 (server:359) INFO: ----------- Starting a new training round (Round #13) -------------
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2024-11-14 15:10:50,925 (client:354) INFO: {'Role': 'Client #10', 'Round': 13, 'Results_raw': {'train_loss': 33.109986, 'val_loss': 32.261358, 'test_loss': 32.795555}}
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2024-11-14 15:12:06,323 (client:354) INFO: {'Role': 'Client #5', 'Round': 13, 'Results_raw': {'train_loss': 32.451655, 'val_loss': 32.065986, 'test_loss': 33.297058}}
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2024-11-14 15:13:21,999 (client:354) INFO: {'Role': 'Client #7', 'Round': 13, 'Results_raw': {'train_loss': 32.005408, 'val_loss': 30.853934, 'test_loss': 31.577424}}
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2024-11-14 15:14:40,867 (client:354) INFO: {'Role': 'Client #8', 'Round': 13, 'Results_raw': {'train_loss': 32.934347, 'val_loss': 32.138817, 'test_loss': 33.05887}}
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2024-11-14 15:15:58,010 (client:354) INFO: {'Role': 'Client #1', 'Round': 13, 'Results_raw': {'train_loss': 33.613922, 'val_loss': 33.031724, 'test_loss': 33.66888}}
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2024-11-14 15:17:13,689 (client:354) INFO: {'Role': 'Client #9', 'Round': 13, 'Results_raw': {'train_loss': 35.199841, 'val_loss': 34.166892, 'test_loss': 35.606127}}
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2024-11-14 15:18:29,863 (client:354) INFO: {'Role': 'Client #6', 'Round': 13, 'Results_raw': {'train_loss': 33.849546, 'val_loss': 31.328312, 'test_loss': 34.537492}}
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2024-11-14 15:19:45,940 (client:354) INFO: {'Role': 'Client #3', 'Round': 13, 'Results_raw': {'train_loss': 31.985619, 'val_loss': 33.309541, 'test_loss': 33.62622}}
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2024-11-14 15:21:02,264 (client:354) INFO: {'Role': 'Client #2', 'Round': 13, 'Results_raw': {'train_loss': 28.637191, 'val_loss': 27.826139, 'test_loss': 28.577025}}
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2024-11-14 15:22:16,914 (client:354) INFO: {'Role': 'Client #4', 'Round': 13, 'Results_raw': {'train_loss': 36.496981, 'val_loss': 35.759505, 'test_loss': 35.175793}}
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2024-11-14 15:22:16,917 (server:615) INFO: {'Role': 'Server #', 'Round': 12, 'Results_weighted_avg': {'test_avg_loss': np.float64(42.141459), 'test_loss': np.float64(237340.696606), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.692556), 'val_loss': np.float64(234812.475012), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(42.141459), 'test_loss': np.float64(237340.696606), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.692556), 'val_loss': np.float64(234812.475012), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.924926), 'test_avg_loss_bottom_decile': np.float64(40.814434), 'test_avg_loss_top_decile': np.float64(45.133877), 'test_avg_loss_min': np.float64(37.820456), 'test_avg_loss_max': np.float64(45.133877), 'test_avg_loss_bottom10%': np.float64(37.820456), 'test_avg_loss_top10%': np.float64(45.133877), 'test_avg_loss_cos1': np.float64(0.998958), 'test_avg_loss_entropy': np.float64(2.30153), 'test_loss_std': np.float64(10841.183731), 'test_loss_bottom_decile': np.float64(229866.890503), 'test_loss_top_decile': np.float64(254193.996948), 'test_loss_min': np.float64(213004.806763), 'test_loss_max': np.float64(254193.996948), 'test_loss_bottom10%': np.float64(213004.806763), 'test_loss_top10%': np.float64(254193.996948), 'test_loss_cos1': np.float64(0.998958), 'test_loss_entropy': np.float64(2.30153), 'val_avg_loss_std': np.float64(2.185227), 'val_avg_loss_bottom_decile': np.float64(40.543411), 'val_avg_loss_top_decile': np.float64(45.240806), 'val_avg_loss_min': np.float64(37.122774), 'val_avg_loss_max': np.float64(45.240806), 'val_avg_loss_bottom10%': np.float64(37.122774), 'val_avg_loss_top10%': np.float64(45.240806), 'val_avg_loss_cos1': np.float64(0.998629), 'val_avg_loss_entropy': np.float64(2.301201), 'val_loss_std': np.float64(12307.201264), 'val_loss_bottom_decile': np.float64(228340.491699), 'val_loss_top_decile': np.float64(254796.219482), 'val_loss_min': np.float64(209075.463745), 'val_loss_max': np.float64(254796.219482), 'val_loss_bottom10%': np.float64(209075.463745), 'val_loss_top10%': np.float64(254796.219482), 'val_loss_cos1': np.float64(0.998629), 'val_loss_entropy': np.float64(2.301201)}}
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2024-11-14 15:22:16,960 (server:353) INFO: Server: Starting evaluation at the end of round 13.
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2024-11-14 15:22:16,961 (server:359) INFO: ----------- Starting a new training round (Round #14) -------------
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2024-11-14 15:25:46,039 (client:354) INFO: {'Role': 'Client #9', 'Round': 14, 'Results_raw': {'train_loss': 35.137264, 'val_loss': 34.134542, 'test_loss': 35.777993}}
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2024-11-14 15:26:54,617 (client:354) INFO: {'Role': 'Client #4', 'Round': 14, 'Results_raw': {'train_loss': 36.329045, 'val_loss': 35.467703, 'test_loss': 34.601273}}
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2024-11-14 15:28:09,513 (client:354) INFO: {'Role': 'Client #8', 'Round': 14, 'Results_raw': {'train_loss': 32.784796, 'val_loss': 32.194326, 'test_loss': 33.237125}}
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2024-11-14 15:29:23,831 (client:354) INFO: {'Role': 'Client #6', 'Round': 14, 'Results_raw': {'train_loss': 33.840635, 'val_loss': 31.287237, 'test_loss': 33.748765}}
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2024-11-14 15:30:37,028 (client:354) INFO: {'Role': 'Client #7', 'Round': 14, 'Results_raw': {'train_loss': 31.84807, 'val_loss': 30.967237, 'test_loss': 31.505578}}
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2024-11-14 15:31:50,392 (client:354) INFO: {'Role': 'Client #5', 'Round': 14, 'Results_raw': {'train_loss': 32.359256, 'val_loss': 32.031887, 'test_loss': 33.322227}}
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2024-11-14 15:33:08,283 (client:354) INFO: {'Role': 'Client #3', 'Round': 14, 'Results_raw': {'train_loss': 31.84966, 'val_loss': 33.267548, 'test_loss': 33.81216}}
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2024-11-14 15:34:24,271 (client:354) INFO: {'Role': 'Client #10', 'Round': 14, 'Results_raw': {'train_loss': 33.102701, 'val_loss': 32.287407, 'test_loss': 32.808508}}
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2024-11-14 15:35:38,536 (client:354) INFO: {'Role': 'Client #1', 'Round': 14, 'Results_raw': {'train_loss': 33.535053, 'val_loss': 33.161972, 'test_loss': 33.697891}}
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2024-11-14 15:36:52,601 (client:354) INFO: {'Role': 'Client #2', 'Round': 14, 'Results_raw': {'train_loss': 28.530904, 'val_loss': 27.905752, 'test_loss': 28.61452}}
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2024-11-14 15:36:52,609 (server:615) INFO: {'Role': 'Server #', 'Round': 13, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.890804), 'test_loss': np.float64(235929.008472), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.450885), 'val_loss': np.float64(233451.382654), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.890804), 'test_loss': np.float64(235929.008472), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.450885), 'val_loss': np.float64(233451.382654), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.903999), 'test_avg_loss_bottom_decile': np.float64(40.603974), 'test_avg_loss_top_decile': np.float64(44.877326), 'test_avg_loss_min': np.float64(37.599923), 'test_avg_loss_max': np.float64(44.877326), 'test_avg_loss_bottom10%': np.float64(37.599923), 'test_avg_loss_top10%': np.float64(44.877326), 'test_avg_loss_cos1': np.float64(0.998969), 'test_avg_loss_entropy': np.float64(2.301541), 'test_loss_std': np.float64(10723.321072), 'test_loss_bottom_decile': np.float64(228681.581177), 'test_loss_top_decile': np.float64(252749.099121), 'test_loss_min': np.float64(211762.765503), 'test_loss_max': np.float64(252749.099121), 'test_loss_bottom10%': np.float64(211762.765503), 'test_loss_top10%': np.float64(252749.099121), 'test_loss_cos1': np.float64(0.998969), 'test_loss_entropy': np.float64(2.301541), 'val_avg_loss_std': np.float64(2.168687), 'val_avg_loss_bottom_decile': np.float64(40.302924), 'val_avg_loss_top_decile': np.float64(44.967645), 'val_avg_loss_min': np.float64(36.929173), 'val_avg_loss_max': np.float64(44.967645), 'val_avg_loss_bottom10%': np.float64(36.929173), 'val_avg_loss_top10%': np.float64(44.967645), 'val_avg_loss_cos1': np.float64(0.998634), 'val_avg_loss_entropy': np.float64(2.301207), 'val_loss_std': np.float64(12214.043176), 'val_loss_bottom_decile': np.float64(226986.070435), 'val_loss_top_decile': np.float64(253257.775757), 'val_loss_min': np.float64(207985.10498), 'val_loss_max': np.float64(253257.775757), 'val_loss_bottom10%': np.float64(207985.10498), 'val_loss_top10%': np.float64(253257.775757), 'val_loss_cos1': np.float64(0.998634), 'val_loss_entropy': np.float64(2.301207)}}
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2024-11-14 15:36:52,645 (server:353) INFO: Server: Starting evaluation at the end of round 14.
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2024-11-14 15:36:52,646 (server:359) INFO: ----------- Starting a new training round (Round #15) -------------
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2024-11-14 15:40:31,590 (client:354) INFO: {'Role': 'Client #7', 'Round': 15, 'Results_raw': {'train_loss': 31.785205, 'val_loss': 30.685515, 'test_loss': 31.316832}}
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2024-11-14 15:41:47,665 (client:354) INFO: {'Role': 'Client #3', 'Round': 15, 'Results_raw': {'train_loss': 31.766042, 'val_loss': 33.354896, 'test_loss': 33.76871}}
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2024-11-14 15:43:05,025 (client:354) INFO: {'Role': 'Client #4', 'Round': 15, 'Results_raw': {'train_loss': 36.336715, 'val_loss': 35.539348, 'test_loss': 34.807487}}
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2024-11-14 15:44:21,916 (client:354) INFO: {'Role': 'Client #5', 'Round': 15, 'Results_raw': {'train_loss': 32.248729, 'val_loss': 32.025856, 'test_loss': 33.349526}}
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2024-11-14 15:45:36,203 (client:354) INFO: {'Role': 'Client #9', 'Round': 15, 'Results_raw': {'train_loss': 34.95433, 'val_loss': 33.810734, 'test_loss': 34.96799}}
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2024-11-14 15:46:49,982 (client:354) INFO: {'Role': 'Client #2', 'Round': 15, 'Results_raw': {'train_loss': 28.443245, 'val_loss': 27.901938, 'test_loss': 28.629162}}
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2024-11-14 15:48:03,330 (client:354) INFO: {'Role': 'Client #6', 'Round': 15, 'Results_raw': {'train_loss': 33.733974, 'val_loss': 31.633339, 'test_loss': 34.470927}}
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2024-11-14 15:49:22,403 (client:354) INFO: {'Role': 'Client #8', 'Round': 15, 'Results_raw': {'train_loss': 32.708852, 'val_loss': 32.00578, 'test_loss': 32.98606}}
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2024-11-14 15:50:33,964 (client:354) INFO: {'Role': 'Client #10', 'Round': 15, 'Results_raw': {'train_loss': 32.975252, 'val_loss': 32.102151, 'test_loss': 32.74824}}
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2024-11-14 15:51:45,228 (client:354) INFO: {'Role': 'Client #1', 'Round': 15, 'Results_raw': {'train_loss': 33.375148, 'val_loss': 33.199491, 'test_loss': 34.041068}}
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2024-11-14 15:51:45,231 (server:615) INFO: {'Role': 'Server #', 'Round': 14, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.641897), 'test_loss': np.float64(234527.162073), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.16995), 'val_loss': np.float64(231869.158411), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.641897), 'test_loss': np.float64(234527.162073), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.16995), 'val_loss': np.float64(231869.158411), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.926283), 'test_avg_loss_bottom_decile': np.float64(40.314812), 'test_avg_loss_top_decile': np.float64(44.681336), 'test_avg_loss_min': np.float64(37.312054), 'test_avg_loss_max': np.float64(44.681336), 'test_avg_loss_bottom10%': np.float64(37.312054), 'test_avg_loss_top10%': np.float64(44.681336), 'test_avg_loss_cos1': np.float64(0.998932), 'test_avg_loss_entropy': np.float64(2.301504), 'test_loss_std': np.float64(10848.827542), 'test_loss_bottom_decile': np.float64(227053.019165), 'test_loss_top_decile': np.float64(251645.284058), 'test_loss_min': np.float64(210141.486694), 'test_loss_max': np.float64(251645.284058), 'test_loss_bottom10%': np.float64(210141.486694), 'test_loss_top10%': np.float64(251645.284058), 'test_loss_cos1': np.float64(0.998932), 'test_loss_entropy': np.float64(2.301504), 'val_avg_loss_std': np.float64(2.180925), 'val_avg_loss_bottom_decile': np.float64(40.018205), 'val_avg_loss_top_decile': np.float64(44.773447), 'val_avg_loss_min': np.float64(36.589844), 'val_avg_loss_max': np.float64(44.773447), 'val_avg_loss_bottom10%': np.float64(36.589844), 'val_avg_loss_top10%': np.float64(44.773447), 'val_avg_loss_cos1': np.float64(0.9986), 'val_avg_loss_entropy': np.float64(2.301172), 'val_loss_std': np.float64(12282.969844), 'val_loss_bottom_decile': np.float64(225382.529663), 'val_loss_top_decile': np.float64(252164.051025), 'val_loss_min': np.float64(206074.000488), 'val_loss_max': np.float64(252164.051025), 'val_loss_bottom10%': np.float64(206074.000488), 'val_loss_top10%': np.float64(252164.051025), 'val_loss_cos1': np.float64(0.9986), 'val_loss_entropy': np.float64(2.301172)}}
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2024-11-14 15:51:45,274 (server:353) INFO: Server: Starting evaluation at the end of round 15.
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2024-11-14 15:51:45,275 (server:359) INFO: ----------- Starting a new training round (Round #16) -------------
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2024-11-14 15:55:16,029 (client:354) INFO: {'Role': 'Client #2', 'Round': 16, 'Results_raw': {'train_loss': 28.293073, 'val_loss': 27.541576, 'test_loss': 28.18054}}
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2024-11-14 15:56:25,346 (client:354) INFO: {'Role': 'Client #9', 'Round': 16, 'Results_raw': {'train_loss': 34.82553, 'val_loss': 33.85765, 'test_loss': 35.217285}}
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2024-11-14 15:57:39,640 (client:354) INFO: {'Role': 'Client #5', 'Round': 16, 'Results_raw': {'train_loss': 32.119514, 'val_loss': 31.777138, 'test_loss': 33.061147}}
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2024-11-14 15:58:50,709 (client:354) INFO: {'Role': 'Client #8', 'Round': 16, 'Results_raw': {'train_loss': 32.580399, 'val_loss': 32.008128, 'test_loss': 33.156247}}
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2024-11-14 16:00:00,815 (client:354) INFO: {'Role': 'Client #6', 'Round': 16, 'Results_raw': {'train_loss': 33.72865, 'val_loss': 31.269925, 'test_loss': 33.860244}}
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2024-11-14 16:01:08,565 (client:354) INFO: {'Role': 'Client #10', 'Round': 16, 'Results_raw': {'train_loss': 32.833764, 'val_loss': 32.241357, 'test_loss': 33.006953}}
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2024-11-14 16:02:18,835 (client:354) INFO: {'Role': 'Client #4', 'Round': 16, 'Results_raw': {'train_loss': 36.232699, 'val_loss': 35.508879, 'test_loss': 34.776613}}
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2024-11-14 16:03:28,933 (client:354) INFO: {'Role': 'Client #3', 'Round': 16, 'Results_raw': {'train_loss': 31.669234, 'val_loss': 33.358335, 'test_loss': 33.857855}}
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2024-11-14 16:04:39,762 (client:354) INFO: {'Role': 'Client #1', 'Round': 16, 'Results_raw': {'train_loss': 33.320381, 'val_loss': 32.97181, 'test_loss': 33.775175}}
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2024-11-14 16:05:51,190 (client:354) INFO: {'Role': 'Client #7', 'Round': 16, 'Results_raw': {'train_loss': 31.637057, 'val_loss': 30.638438, 'test_loss': 31.319888}}
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2024-11-14 16:05:51,194 (server:615) INFO: {'Role': 'Server #', 'Round': 15, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.609728), 'test_loss': np.float64(234345.985693), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.164229), 'val_loss': np.float64(231836.940417), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.609728), 'test_loss': np.float64(234345.985693), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.164229), 'val_loss': np.float64(231836.940417), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.890844), 'test_avg_loss_bottom_decile': np.float64(40.260939), 'test_avg_loss_top_decile': np.float64(44.516487), 'test_avg_loss_min': np.float64(37.29006), 'test_avg_loss_max': np.float64(44.516487), 'test_avg_loss_bottom10%': np.float64(37.29006), 'test_avg_loss_top10%': np.float64(44.516487), 'test_avg_loss_cos1': np.float64(0.998969), 'test_avg_loss_entropy': np.float64(2.30154), 'test_loss_std': np.float64(10649.233538), 'test_loss_bottom_decile': np.float64(226749.610229), 'test_loss_top_decile': np.float64(250716.853516), 'test_loss_min': np.float64(210017.618652), 'test_loss_max': np.float64(250716.853516), 'test_loss_bottom10%': np.float64(210017.618652), 'test_loss_top10%': np.float64(250716.853516), 'test_loss_cos1': np.float64(0.998969), 'test_loss_entropy': np.float64(2.30154), 'val_avg_loss_std': np.float64(2.163686), 'val_avg_loss_bottom_decile': np.float64(39.98196), 'val_avg_loss_top_decile': np.float64(44.660942), 'val_avg_loss_min': np.float64(36.568611), 'val_avg_loss_max': np.float64(44.660942), 'val_avg_loss_bottom10%': np.float64(36.568611), 'val_avg_loss_top10%': np.float64(44.660942), 'val_avg_loss_cos1': np.float64(0.998621), 'val_avg_loss_entropy': np.float64(2.301192), 'val_loss_std': np.float64(12185.878628), 'val_loss_bottom_decile': np.float64(225178.397461), 'val_loss_top_decile': np.float64(251530.426147), 'val_loss_min': np.float64(205954.416504), 'val_loss_max': np.float64(251530.426147), 'val_loss_bottom10%': np.float64(205954.416504), 'val_loss_top10%': np.float64(251530.426147), 'val_loss_cos1': np.float64(0.998621), 'val_loss_entropy': np.float64(2.301192)}}
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2024-11-14 16:05:51,238 (server:353) INFO: Server: Starting evaluation at the end of round 16.
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2024-11-14 16:05:51,239 (server:359) INFO: ----------- Starting a new training round (Round #17) -------------
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2024-11-14 16:09:29,693 (client:354) INFO: {'Role': 'Client #10', 'Round': 17, 'Results_raw': {'train_loss': 32.822973, 'val_loss': 32.338243, 'test_loss': 32.941207}}
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2024-11-14 16:10:40,959 (client:354) INFO: {'Role': 'Client #5', 'Round': 17, 'Results_raw': {'train_loss': 32.105172, 'val_loss': 31.807863, 'test_loss': 33.165895}}
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2024-11-14 16:11:51,290 (client:354) INFO: {'Role': 'Client #3', 'Round': 17, 'Results_raw': {'train_loss': 31.601631, 'val_loss': 33.119359, 'test_loss': 33.610062}}
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2024-11-14 16:13:06,874 (client:354) INFO: {'Role': 'Client #7', 'Round': 17, 'Results_raw': {'train_loss': 31.571989, 'val_loss': 30.875974, 'test_loss': 31.540852}}
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2024-11-14 16:14:24,228 (client:354) INFO: {'Role': 'Client #2', 'Round': 17, 'Results_raw': {'train_loss': 28.296468, 'val_loss': 27.707334, 'test_loss': 28.369334}}
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2024-11-14 16:15:44,410 (client:354) INFO: {'Role': 'Client #4', 'Round': 17, 'Results_raw': {'train_loss': 36.118868, 'val_loss': 35.66447, 'test_loss': 35.007343}}
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2024-11-14 16:16:59,734 (client:354) INFO: {'Role': 'Client #8', 'Round': 17, 'Results_raw': {'train_loss': 32.507564, 'val_loss': 32.047812, 'test_loss': 33.046114}}
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2024-11-14 16:18:07,570 (client:354) INFO: {'Role': 'Client #9', 'Round': 17, 'Results_raw': {'train_loss': 34.797895, 'val_loss': 34.190904, 'test_loss': 35.424242}}
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2024-11-14 16:19:17,846 (client:354) INFO: {'Role': 'Client #6', 'Round': 17, 'Results_raw': {'train_loss': 33.550777, 'val_loss': 31.120449, 'test_loss': 34.013014}}
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2024-11-14 16:20:25,652 (client:354) INFO: {'Role': 'Client #1', 'Round': 17, 'Results_raw': {'train_loss': 33.214582, 'val_loss': 32.966809, 'test_loss': 33.682902}}
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2024-11-14 16:20:25,656 (server:615) INFO: {'Role': 'Server #', 'Round': 16, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.640959), 'test_loss': np.float64(234521.88335), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.182954), 'val_loss': np.float64(231942.399207), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.640959), 'test_loss': np.float64(234521.88335), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.182954), 'val_loss': np.float64(231942.399207), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.9384), 'test_avg_loss_bottom_decile': np.float64(40.281913), 'test_avg_loss_top_decile': np.float64(44.566607), 'test_avg_loss_min': np.float64(37.184062), 'test_avg_loss_max': np.float64(44.566607), 'test_avg_loss_bottom10%': np.float64(37.184062), 'test_avg_loss_top10%': np.float64(44.566607), 'test_avg_loss_cos1': np.float64(0.998918), 'test_avg_loss_entropy': np.float64(2.301487), 'test_loss_std': np.float64(10917.067545), 'test_loss_bottom_decile': np.float64(226867.733643), 'test_loss_top_decile': np.float64(250999.131348), 'test_loss_min': np.float64(209420.638062), 'test_loss_max': np.float64(250999.131348), 'test_loss_bottom10%': np.float64(209420.638062), 'test_loss_top10%': np.float64(250999.131348), 'test_loss_cos1': np.float64(0.998918), 'test_loss_entropy': np.float64(2.301487), 'val_avg_loss_std': np.float64(2.206055), 'val_avg_loss_bottom_decile': np.float64(40.009391), 'val_avg_loss_top_decile': np.float64(44.764156), 'val_avg_loss_min': np.float64(36.487338), 'val_avg_loss_max': np.float64(44.764156), 'val_avg_loss_bottom10%': np.float64(36.487338), 'val_avg_loss_top10%': np.float64(44.764156), 'val_avg_loss_cos1': np.float64(0.998568), 'val_avg_loss_entropy': np.float64(2.301138), 'val_loss_std': np.float64(12424.501314), 'val_loss_bottom_decile': np.float64(225332.890381), 'val_loss_top_decile': np.float64(252111.723877), 'val_loss_min': np.float64(205496.687012), 'val_loss_max': np.float64(252111.723877), 'val_loss_bottom10%': np.float64(205496.687012), 'val_loss_top10%': np.float64(252111.723877), 'val_loss_cos1': np.float64(0.998568), 'val_loss_entropy': np.float64(2.301138)}}
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2024-11-14 16:20:25,712 (server:353) INFO: Server: Starting evaluation at the end of round 17.
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2024-11-14 16:20:25,713 (server:359) INFO: ----------- Starting a new training round (Round #18) -------------
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2024-11-14 16:24:14,906 (client:354) INFO: {'Role': 'Client #3', 'Round': 18, 'Results_raw': {'train_loss': 31.558656, 'val_loss': 33.208869, 'test_loss': 33.562541}}
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2024-11-14 16:25:26,393 (client:354) INFO: {'Role': 'Client #5', 'Round': 18, 'Results_raw': {'train_loss': 31.930315, 'val_loss': 31.872396, 'test_loss': 33.307184}}
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2024-11-14 16:26:35,246 (client:354) INFO: {'Role': 'Client #7', 'Round': 18, 'Results_raw': {'train_loss': 31.56598, 'val_loss': 30.659677, 'test_loss': 31.137725}}
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2024-11-14 16:27:46,904 (client:354) INFO: {'Role': 'Client #9', 'Round': 18, 'Results_raw': {'train_loss': 34.750097, 'val_loss': 33.830324, 'test_loss': 35.290111}}
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2024-11-14 16:28:58,590 (client:354) INFO: {'Role': 'Client #10', 'Round': 18, 'Results_raw': {'train_loss': 32.674629, 'val_loss': 32.300444, 'test_loss': 32.846378}}
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2024-11-14 16:30:07,428 (client:354) INFO: {'Role': 'Client #1', 'Round': 18, 'Results_raw': {'train_loss': 33.141333, 'val_loss': 32.854396, 'test_loss': 33.593255}}
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2024-11-14 16:31:15,433 (client:354) INFO: {'Role': 'Client #4', 'Round': 18, 'Results_raw': {'train_loss': 36.12441, 'val_loss': 35.392891, 'test_loss': 34.727186}}
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2024-11-14 16:32:22,869 (client:354) INFO: {'Role': 'Client #8', 'Round': 18, 'Results_raw': {'train_loss': 32.615208, 'val_loss': 31.937246, 'test_loss': 32.872101}}
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2024-11-14 16:33:34,636 (client:354) INFO: {'Role': 'Client #2', 'Round': 18, 'Results_raw': {'train_loss': 28.240528, 'val_loss': 27.377467, 'test_loss': 28.087427}}
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2024-11-14 16:34:46,805 (client:354) INFO: {'Role': 'Client #6', 'Round': 18, 'Results_raw': {'train_loss': 33.533257, 'val_loss': 31.223965, 'test_loss': 34.340629}}
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2024-11-14 16:34:46,809 (server:615) INFO: {'Role': 'Server #', 'Round': 17, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.440796), 'test_loss': np.float64(233394.561243), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.996984), 'val_loss': np.float64(230895.011426), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.440796), 'test_loss': np.float64(233394.561243), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.996984), 'val_loss': np.float64(230895.011426), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.889693), 'test_avg_loss_bottom_decile': np.float64(40.111577), 'test_avg_loss_top_decile': np.float64(44.311176), 'test_avg_loss_min': np.float64(37.077893), 'test_avg_loss_max': np.float64(44.311176), 'test_avg_loss_bottom10%': np.float64(37.077893), 'test_avg_loss_top10%': np.float64(44.311176), 'test_avg_loss_cos1': np.float64(0.998962), 'test_avg_loss_entropy': np.float64(2.301532), 'test_loss_std': np.float64(10642.753362), 'test_loss_bottom_decile': np.float64(225908.39978), 'test_loss_top_decile': np.float64(249560.541626), 'test_loss_min': np.float64(208822.691528), 'test_loss_max': np.float64(249560.541626), 'test_loss_bottom10%': np.float64(208822.691528), 'test_loss_top10%': np.float64(249560.541626), 'test_loss_cos1': np.float64(0.998962), 'test_loss_entropy': np.float64(2.301532), 'val_avg_loss_std': np.float64(2.175085), 'val_avg_loss_bottom_decile': np.float64(39.8449), 'val_avg_loss_top_decile': np.float64(44.444962), 'val_avg_loss_min': np.float64(36.376589), 'val_avg_loss_max': np.float64(44.444962), 'val_avg_loss_bottom10%': np.float64(36.376589), 'val_avg_loss_top10%': np.float64(44.444962), 'val_avg_loss_cos1': np.float64(0.998596), 'val_avg_loss_entropy': np.float64(2.301166), 'val_loss_std': np.float64(12250.07864), 'val_loss_bottom_decile': np.float64(224406.47522), 'val_loss_top_decile': np.float64(250314.023926), 'val_loss_min': np.float64(204872.950806), 'val_loss_max': np.float64(250314.023926), 'val_loss_bottom10%': np.float64(204872.950806), 'val_loss_top10%': np.float64(250314.023926), 'val_loss_cos1': np.float64(0.998596), 'val_loss_entropy': np.float64(2.301166)}}
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2024-11-14 16:34:46,856 (server:353) INFO: Server: Starting evaluation at the end of round 18.
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2024-11-14 16:34:46,857 (server:359) INFO: ----------- Starting a new training round (Round #19) -------------
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2024-11-14 16:38:31,825 (client:354) INFO: {'Role': 'Client #3', 'Round': 19, 'Results_raw': {'train_loss': 31.462708, 'val_loss': 33.071497, 'test_loss': 33.403921}}
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2024-11-14 16:39:42,387 (client:354) INFO: {'Role': 'Client #7', 'Round': 19, 'Results_raw': {'train_loss': 31.400982, 'val_loss': 30.482558, 'test_loss': 31.086081}}
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2024-11-14 16:40:52,381 (client:354) INFO: {'Role': 'Client #2', 'Round': 19, 'Results_raw': {'train_loss': 28.138284, 'val_loss': 27.661694, 'test_loss': 28.237647}}
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2024-11-14 16:42:02,001 (client:354) INFO: {'Role': 'Client #4', 'Round': 19, 'Results_raw': {'train_loss': 35.984924, 'val_loss': 35.34433, 'test_loss': 34.624916}}
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2024-11-14 16:43:12,347 (client:354) INFO: {'Role': 'Client #5', 'Round': 19, 'Results_raw': {'train_loss': 31.813575, 'val_loss': 31.804382, 'test_loss': 33.051333}}
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2024-11-14 16:44:29,352 (client:354) INFO: {'Role': 'Client #9', 'Round': 19, 'Results_raw': {'train_loss': 34.719926, 'val_loss': 34.026356, 'test_loss': 35.561147}}
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2024-11-14 16:45:48,824 (client:354) INFO: {'Role': 'Client #10', 'Round': 19, 'Results_raw': {'train_loss': 32.612537, 'val_loss': 32.276346, 'test_loss': 32.787764}}
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2024-11-14 16:47:17,590 (client:354) INFO: {'Role': 'Client #1', 'Round': 19, 'Results_raw': {'train_loss': 33.044637, 'val_loss': 33.101843, 'test_loss': 33.79651}}
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2024-11-14 16:48:29,872 (client:354) INFO: {'Role': 'Client #8', 'Round': 19, 'Results_raw': {'train_loss': 32.448581, 'val_loss': 32.124736, 'test_loss': 33.119605}}
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2024-11-14 16:49:39,863 (client:354) INFO: {'Role': 'Client #6', 'Round': 19, 'Results_raw': {'train_loss': 33.527476, 'val_loss': 31.180903, 'test_loss': 34.724426}}
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2024-11-14 16:49:39,867 (server:615) INFO: {'Role': 'Server #', 'Round': 18, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.518851), 'test_loss': np.float64(233834.169031), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.083694), 'val_loss': np.float64(231383.365759), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.518851), 'test_loss': np.float64(233834.169031), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(41.083694), 'val_loss': np.float64(231383.365759), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.90594), 'test_avg_loss_bottom_decile': np.float64(40.227489), 'test_avg_loss_top_decile': np.float64(44.315577), 'test_avg_loss_min': np.float64(37.07643), 'test_avg_loss_max': np.float64(44.315577), 'test_avg_loss_bottom10%': np.float64(37.07643), 'test_avg_loss_top10%': np.float64(44.315577), 'test_avg_loss_cos1': np.float64(0.998948), 'test_avg_loss_entropy': np.float64(2.301516), 'test_loss_std': np.float64(10734.251919), 'test_loss_bottom_decile': np.float64(226561.215576), 'test_loss_top_decile': np.float64(249585.331909), 'test_loss_min': np.float64(208814.454956), 'test_loss_max': np.float64(249585.331909), 'test_loss_bottom10%': np.float64(208814.454956), 'test_loss_top10%': np.float64(249585.331909), 'test_loss_cos1': np.float64(0.998948), 'test_loss_entropy': np.float64(2.301516), 'val_avg_loss_std': np.float64(2.22016), 'val_avg_loss_bottom_decile': np.float64(39.895165), 'val_avg_loss_top_decile': np.float64(44.550527), 'val_avg_loss_min': np.float64(36.365585), 'val_avg_loss_max': np.float64(44.550527), 'val_avg_loss_bottom10%': np.float64(36.365585), 'val_avg_loss_top10%': np.float64(44.550527), 'val_avg_loss_cos1': np.float64(0.998543), 'val_avg_loss_entropy': np.float64(2.301112), 'val_loss_std': np.float64(12503.941957), 'val_loss_bottom_decile': np.float64(224689.567505), 'val_loss_top_decile': np.float64(250908.569824), 'val_loss_min': np.float64(204810.974243), 'val_loss_max': np.float64(250908.569824), 'val_loss_bottom10%': np.float64(204810.974243), 'val_loss_top10%': np.float64(250908.569824), 'val_loss_cos1': np.float64(0.998543), 'val_loss_entropy': np.float64(2.301112)}}
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2024-11-14 16:49:39,920 (server:353) INFO: Server: Starting evaluation at the end of round 19.
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2024-11-14 16:49:39,920 (server:359) INFO: ----------- Starting a new training round (Round #20) -------------
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2024-11-14 16:53:12,712 (client:354) INFO: {'Role': 'Client #3', 'Round': 20, 'Results_raw': {'train_loss': 31.459867, 'val_loss': 33.223305, 'test_loss': 33.828692}}
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2024-11-14 16:54:23,588 (client:354) INFO: {'Role': 'Client #4', 'Round': 20, 'Results_raw': {'train_loss': 35.891005, 'val_loss': 35.194642, 'test_loss': 34.507296}}
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2024-11-14 16:55:31,809 (client:354) INFO: {'Role': 'Client #1', 'Round': 20, 'Results_raw': {'train_loss': 32.946974, 'val_loss': 32.967906, 'test_loss': 33.903286}}
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2024-11-14 16:56:43,916 (client:354) INFO: {'Role': 'Client #5', 'Round': 20, 'Results_raw': {'train_loss': 31.796892, 'val_loss': 31.853913, 'test_loss': 33.293508}}
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2024-11-14 16:57:57,247 (client:354) INFO: {'Role': 'Client #9', 'Round': 20, 'Results_raw': {'train_loss': 34.588782, 'val_loss': 34.073084, 'test_loss': 35.482298}}
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2024-11-14 16:59:11,364 (client:354) INFO: {'Role': 'Client #8', 'Round': 20, 'Results_raw': {'train_loss': 32.369279, 'val_loss': 31.640216, 'test_loss': 32.726605}}
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2024-11-14 17:00:18,737 (client:354) INFO: {'Role': 'Client #7', 'Round': 20, 'Results_raw': {'train_loss': 31.369997, 'val_loss': 30.489367, 'test_loss': 31.049822}}
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2024-11-14 17:01:26,051 (client:354) INFO: {'Role': 'Client #6', 'Round': 20, 'Results_raw': {'train_loss': 33.356908, 'val_loss': 31.131055, 'test_loss': 33.371812}}
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2024-11-14 17:02:34,750 (client:354) INFO: {'Role': 'Client #2', 'Round': 20, 'Results_raw': {'train_loss': 28.164894, 'val_loss': 27.43063, 'test_loss': 28.347132}}
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2024-11-14 17:03:44,359 (client:354) INFO: {'Role': 'Client #10', 'Round': 20, 'Results_raw': {'train_loss': 32.553343, 'val_loss': 32.22851, 'test_loss': 32.923161}}
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2024-11-14 17:03:44,362 (server:615) INFO: {'Role': 'Server #', 'Round': 19, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.161468), 'test_loss': np.float64(231821.387415), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.722013), 'val_loss': np.float64(229346.376819), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.161468), 'test_loss': np.float64(231821.387415), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.722013), 'val_loss': np.float64(229346.376819), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.852514), 'test_avg_loss_bottom_decile': np.float64(39.871705), 'test_avg_loss_top_decile': np.float64(43.945421), 'test_avg_loss_min': np.float64(36.887285), 'test_avg_loss_max': np.float64(43.945421), 'test_avg_loss_bottom10%': np.float64(36.887285), 'test_avg_loss_top10%': np.float64(43.945421), 'test_avg_loss_cos1': np.float64(0.998989), 'test_avg_loss_entropy': np.float64(2.301559), 'test_loss_std': np.float64(10433.3563), 'test_loss_bottom_decile': np.float64(224557.442139), 'test_loss_top_decile': np.float64(247500.612061), 'test_loss_min': np.float64(207749.191528), 'test_loss_max': np.float64(247500.612061), 'test_loss_bottom10%': np.float64(207749.191528), 'test_loss_top10%': np.float64(247500.612061), 'test_loss_cos1': np.float64(0.998989), 'test_loss_entropy': np.float64(2.301559), 'val_avg_loss_std': np.float64(2.133716), 'val_avg_loss_bottom_decile': np.float64(39.604741), 'val_avg_loss_top_decile': np.float64(44.169308), 'val_avg_loss_min': np.float64(36.178854), 'val_avg_loss_max': np.float64(44.169308), 'val_avg_loss_bottom10%': np.float64(36.178854), 'val_avg_loss_top10%': np.float64(44.169308), 'val_avg_loss_cos1': np.float64(0.99863), 'val_avg_loss_entropy': np.float64(2.301201), 'val_loss_std': np.float64(12017.09016), 'val_loss_bottom_decile': np.float64(223053.900024), 'val_loss_top_decile': np.float64(248761.544434), 'val_loss_min': np.float64(203759.303711), 'val_loss_max': np.float64(248761.544434), 'val_loss_bottom10%': np.float64(203759.303711), 'val_loss_top10%': np.float64(248761.544434), 'val_loss_cos1': np.float64(0.99863), 'val_loss_entropy': np.float64(2.301201)}}
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2024-11-14 17:03:44,401 (server:353) INFO: Server: Starting evaluation at the end of round 20.
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2024-11-14 17:03:44,402 (server:359) INFO: ----------- Starting a new training round (Round #21) -------------
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2024-11-14 17:07:24,104 (client:354) INFO: {'Role': 'Client #5', 'Round': 21, 'Results_raw': {'train_loss': 31.77186, 'val_loss': 31.741339, 'test_loss': 33.053897}}
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2024-11-14 17:08:33,443 (client:354) INFO: {'Role': 'Client #3', 'Round': 21, 'Results_raw': {'train_loss': 31.349311, 'val_loss': 33.250193, 'test_loss': 33.863079}}
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2024-11-14 17:09:43,484 (client:354) INFO: {'Role': 'Client #6', 'Round': 21, 'Results_raw': {'train_loss': 33.282705, 'val_loss': 31.076158, 'test_loss': 34.611539}}
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2024-11-14 17:10:54,643 (client:354) INFO: {'Role': 'Client #9', 'Round': 21, 'Results_raw': {'train_loss': 34.4902, 'val_loss': 33.863981, 'test_loss': 34.933988}}
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2024-11-14 17:12:05,220 (client:354) INFO: {'Role': 'Client #4', 'Round': 21, 'Results_raw': {'train_loss': 35.773267, 'val_loss': 35.12645, 'test_loss': 34.444464}}
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2024-11-14 17:13:14,482 (client:354) INFO: {'Role': 'Client #1', 'Round': 21, 'Results_raw': {'train_loss': 32.920692, 'val_loss': 32.983665, 'test_loss': 33.608327}}
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2024-11-14 17:14:25,048 (client:354) INFO: {'Role': 'Client #7', 'Round': 21, 'Results_raw': {'train_loss': 31.311248, 'val_loss': 30.6906, 'test_loss': 31.441003}}
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2024-11-14 17:15:36,018 (client:354) INFO: {'Role': 'Client #2', 'Round': 21, 'Results_raw': {'train_loss': 27.97557, 'val_loss': 27.612773, 'test_loss': 28.484211}}
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2024-11-14 17:16:46,404 (client:354) INFO: {'Role': 'Client #10', 'Round': 21, 'Results_raw': {'train_loss': 32.41905, 'val_loss': 31.992157, 'test_loss': 32.842795}}
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2024-11-14 17:17:55,693 (client:354) INFO: {'Role': 'Client #8', 'Round': 21, 'Results_raw': {'train_loss': 32.284243, 'val_loss': 32.160054, 'test_loss': 33.211306}}
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2024-11-14 17:17:55,697 (server:615) INFO: {'Role': 'Server #', 'Round': 20, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.273038), 'test_loss': np.float64(232449.751978), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.797781), 'val_loss': np.float64(229773.100415), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.273038), 'test_loss': np.float64(232449.751978), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.797781), 'val_loss': np.float64(229773.100415), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.916508), 'test_avg_loss_bottom_decile': np.float64(40.000901), 'test_avg_loss_top_decile': np.float64(44.111955), 'test_avg_loss_min': np.float64(36.820284), 'test_avg_loss_max': np.float64(44.111955), 'test_avg_loss_bottom10%': np.float64(36.820284), 'test_avg_loss_top10%': np.float64(44.111955), 'test_avg_loss_cos1': np.float64(0.998924), 'test_avg_loss_entropy': np.float64(2.301492), 'test_loss_std': np.float64(10793.773543), 'test_loss_bottom_decile': np.float64(225285.073608), 'test_loss_top_decile': np.float64(248438.530396), 'test_loss_min': np.float64(207371.841919), 'test_loss_max': np.float64(248438.530396), 'test_loss_bottom10%': np.float64(207371.841919), 'test_loss_top10%': np.float64(248438.530396), 'test_loss_cos1': np.float64(0.998924), 'test_loss_entropy': np.float64(2.301492), 'val_avg_loss_std': np.float64(2.217243), 'val_avg_loss_bottom_decile': np.float64(39.667993), 'val_avg_loss_top_decile': np.float64(44.366347), 'val_avg_loss_min': np.float64(36.073523), 'val_avg_loss_max': np.float64(44.366347), 'val_avg_loss_bottom10%': np.float64(36.073523), 'val_avg_loss_top10%': np.float64(44.366347), 'val_avg_loss_cos1': np.float64(0.998526), 'val_avg_loss_entropy': np.float64(2.301096), 'val_loss_std': np.float64(12487.511357), 'val_loss_bottom_decile': np.float64(223410.134888), 'val_loss_top_decile': np.float64(249871.265991), 'val_loss_min': np.float64(203166.081177), 'val_loss_max': np.float64(249871.265991), 'val_loss_bottom10%': np.float64(203166.081177), 'val_loss_top10%': np.float64(249871.265991), 'val_loss_cos1': np.float64(0.998526), 'val_loss_entropy': np.float64(2.301096)}}
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2024-11-14 17:17:55,727 (server:353) INFO: Server: Starting evaluation at the end of round 21.
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2024-11-14 17:17:55,728 (server:359) INFO: ----------- Starting a new training round (Round #22) -------------
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2024-11-14 17:21:34,104 (client:354) INFO: {'Role': 'Client #4', 'Round': 22, 'Results_raw': {'train_loss': 35.694437, 'val_loss': 35.311368, 'test_loss': 34.614431}}
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2024-11-14 17:22:45,270 (client:354) INFO: {'Role': 'Client #8', 'Round': 22, 'Results_raw': {'train_loss': 32.283729, 'val_loss': 31.873534, 'test_loss': 33.053219}}
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2024-11-14 17:23:55,139 (client:354) INFO: {'Role': 'Client #2', 'Round': 22, 'Results_raw': {'train_loss': 28.031915, 'val_loss': 27.386786, 'test_loss': 28.272932}}
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2024-11-14 17:25:06,041 (client:354) INFO: {'Role': 'Client #6', 'Round': 22, 'Results_raw': {'train_loss': 33.265108, 'val_loss': 31.265677, 'test_loss': 34.926916}}
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2024-11-14 17:26:18,144 (client:354) INFO: {'Role': 'Client #3', 'Round': 22, 'Results_raw': {'train_loss': 31.342713, 'val_loss': 33.002755, 'test_loss': 33.742139}}
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2024-11-14 17:27:28,058 (client:354) INFO: {'Role': 'Client #10', 'Round': 22, 'Results_raw': {'train_loss': 32.41225, 'val_loss': 31.948387, 'test_loss': 32.752742}}
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2024-11-14 17:28:38,594 (client:354) INFO: {'Role': 'Client #1', 'Round': 22, 'Results_raw': {'train_loss': 32.851717, 'val_loss': 32.729433, 'test_loss': 33.597804}}
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2024-11-14 17:29:48,490 (client:354) INFO: {'Role': 'Client #9', 'Round': 22, 'Results_raw': {'train_loss': 34.392066, 'val_loss': 33.818883, 'test_loss': 35.670925}}
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2024-11-14 17:31:01,677 (client:354) INFO: {'Role': 'Client #7', 'Round': 22, 'Results_raw': {'train_loss': 31.226821, 'val_loss': 30.799368, 'test_loss': 31.404776}}
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2024-11-14 17:32:15,585 (client:354) INFO: {'Role': 'Client #5', 'Round': 22, 'Results_raw': {'train_loss': 31.666313, 'val_loss': 32.048692, 'test_loss': 33.627108}}
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2024-11-14 17:32:15,588 (server:615) INFO: {'Role': 'Server #', 'Round': 21, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.146439), 'test_loss': np.float64(231736.74165), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.701226), 'val_loss': np.float64(229229.302856), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.146439), 'test_loss': np.float64(231736.74165), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.701226), 'val_loss': np.float64(229229.302856), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.852662), 'test_avg_loss_bottom_decile': np.float64(39.874452), 'test_avg_loss_top_decile': np.float64(43.826577), 'test_avg_loss_min': np.float64(36.798995), 'test_avg_loss_max': np.float64(43.826577), 'test_avg_loss_bottom10%': np.float64(36.798995), 'test_avg_loss_top10%': np.float64(43.826577), 'test_avg_loss_cos1': np.float64(0.998988), 'test_avg_loss_entropy': np.float64(2.301557), 'test_loss_std': np.float64(10434.193041), 'test_loss_bottom_decile': np.float64(224572.916382), 'test_loss_top_decile': np.float64(246831.278931), 'test_loss_min': np.float64(207251.942505), 'test_loss_max': np.float64(246831.278931), 'test_loss_bottom10%': np.float64(207251.942505), 'test_loss_top10%': np.float64(246831.278931), 'test_loss_cos1': np.float64(0.998988), 'test_loss_entropy': np.float64(2.301557), 'val_avg_loss_std': np.float64(2.143408), 'val_avg_loss_bottom_decile': np.float64(39.628747), 'val_avg_loss_top_decile': np.float64(44.124023), 'val_avg_loss_min': np.float64(36.104537), 'val_avg_loss_max': np.float64(44.124023), 'val_avg_loss_bottom10%': np.float64(36.104537), 'val_avg_loss_top10%': np.float64(44.124023), 'val_avg_loss_cos1': np.float64(0.998616), 'val_avg_loss_entropy': np.float64(2.301186), 'val_loss_std': np.float64(12071.672692), 'val_loss_bottom_decile': np.float64(223189.103882), 'val_loss_top_decile': np.float64(248506.494995), 'val_loss_min': np.float64(203340.75), 'val_loss_max': np.float64(248506.494995), 'val_loss_bottom10%': np.float64(203340.75), 'val_loss_top10%': np.float64(248506.494995), 'val_loss_cos1': np.float64(0.998616), 'val_loss_entropy': np.float64(2.301186)}}
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2024-11-14 17:32:15,636 (server:353) INFO: Server: Starting evaluation at the end of round 22.
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2024-11-14 17:32:15,636 (server:359) INFO: ----------- Starting a new training round (Round #23) -------------
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2024-11-14 17:36:02,668 (client:354) INFO: {'Role': 'Client #5', 'Round': 23, 'Results_raw': {'train_loss': 31.592206, 'val_loss': 31.624656, 'test_loss': 32.91284}}
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2024-11-14 17:37:13,917 (client:354) INFO: {'Role': 'Client #6', 'Round': 23, 'Results_raw': {'train_loss': 33.140483, 'val_loss': 31.323133, 'test_loss': 34.854655}}
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2024-11-14 17:38:24,306 (client:354) INFO: {'Role': 'Client #9', 'Round': 23, 'Results_raw': {'train_loss': 34.399458, 'val_loss': 33.968465, 'test_loss': 35.386654}}
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2024-11-14 17:39:33,702 (client:354) INFO: {'Role': 'Client #1', 'Round': 23, 'Results_raw': {'train_loss': 32.742194, 'val_loss': 32.826094, 'test_loss': 33.833451}}
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2024-11-14 17:40:43,303 (client:354) INFO: {'Role': 'Client #8', 'Round': 23, 'Results_raw': {'train_loss': 32.208547, 'val_loss': 31.554159, 'test_loss': 32.635348}}
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2024-11-14 17:41:52,878 (client:354) INFO: {'Role': 'Client #3', 'Round': 23, 'Results_raw': {'train_loss': 31.235688, 'val_loss': 33.098996, 'test_loss': 33.707121}}
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2024-11-14 17:43:03,207 (client:354) INFO: {'Role': 'Client #4', 'Round': 23, 'Results_raw': {'train_loss': 35.676855, 'val_loss': 35.356642, 'test_loss': 35.008101}}
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2024-11-14 17:44:13,155 (client:354) INFO: {'Role': 'Client #7', 'Round': 23, 'Results_raw': {'train_loss': 31.147411, 'val_loss': 30.493991, 'test_loss': 31.19102}}
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2024-11-14 17:45:23,313 (client:354) INFO: {'Role': 'Client #10', 'Round': 23, 'Results_raw': {'train_loss': 32.367325, 'val_loss': 32.118205, 'test_loss': 32.777216}}
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2024-11-14 17:46:33,176 (client:354) INFO: {'Role': 'Client #2', 'Round': 23, 'Results_raw': {'train_loss': 27.869893, 'val_loss': 27.438074, 'test_loss': 28.173411}}
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2024-11-14 17:46:33,180 (server:615) INFO: {'Role': 'Server #', 'Round': 22, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.922588), 'test_loss': np.float64(230476.014148), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.479536), 'val_loss': np.float64(227980.747534), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.922588), 'test_loss': np.float64(230476.014148), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.479536), 'val_loss': np.float64(227980.747534), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.898012), 'test_avg_loss_bottom_decile': np.float64(39.650929), 'test_avg_loss_top_decile': np.float64(43.737975), 'test_avg_loss_min': np.float64(36.464777), 'test_avg_loss_max': np.float64(43.737975), 'test_avg_loss_bottom10%': np.float64(36.464777), 'test_avg_loss_top10%': np.float64(43.737975), 'test_avg_loss_cos1': np.float64(0.998926), 'test_avg_loss_entropy': np.float64(2.301494), 'test_loss_std': np.float64(10689.602355), 'test_loss_bottom_decile': np.float64(223314.032471), 'test_loss_top_decile': np.float64(246332.276855), 'test_loss_min': np.float64(205369.623413), 'test_loss_max': np.float64(246332.276855), 'test_loss_bottom10%': np.float64(205369.623413), 'test_loss_top10%': np.float64(246332.276855), 'test_loss_cos1': np.float64(0.998926), 'test_loss_entropy': np.float64(2.301494), 'val_avg_loss_std': np.float64(2.191773), 'val_avg_loss_bottom_decile': np.float64(39.408335), 'val_avg_loss_top_decile': np.float64(43.92028), 'val_avg_loss_min': np.float64(35.788681), 'val_avg_loss_max': np.float64(43.92028), 'val_avg_loss_bottom10%': np.float64(35.788681), 'val_avg_loss_top10%': np.float64(43.92028), 'val_avg_loss_cos1': np.float64(0.998537), 'val_avg_loss_entropy': np.float64(2.301106), 'val_loss_std': np.float64(12344.065982), 'val_loss_bottom_decile': np.float64(221947.744751), 'val_loss_top_decile': np.float64(247359.017456), 'val_loss_min': np.float64(201561.849365), 'val_loss_max': np.float64(247359.017456), 'val_loss_bottom10%': np.float64(201561.849365), 'val_loss_top10%': np.float64(247359.017456), 'val_loss_cos1': np.float64(0.998537), 'val_loss_entropy': np.float64(2.301106)}}
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2024-11-14 17:46:33,232 (server:353) INFO: Server: Starting evaluation at the end of round 23.
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2024-11-14 17:46:33,233 (server:359) INFO: ----------- Starting a new training round (Round #24) -------------
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2024-11-14 17:50:09,765 (client:354) INFO: {'Role': 'Client #4', 'Round': 24, 'Results_raw': {'train_loss': 35.680759, 'val_loss': 35.392111, 'test_loss': 34.751016}}
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2024-11-14 17:51:20,509 (client:354) INFO: {'Role': 'Client #2', 'Round': 24, 'Results_raw': {'train_loss': 27.934338, 'val_loss': 27.474547, 'test_loss': 28.232217}}
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2024-11-14 17:52:30,310 (client:354) INFO: {'Role': 'Client #10', 'Round': 24, 'Results_raw': {'train_loss': 32.334985, 'val_loss': 31.645167, 'test_loss': 32.441018}}
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2024-11-14 17:53:40,728 (client:354) INFO: {'Role': 'Client #6', 'Round': 24, 'Results_raw': {'train_loss': 33.189357, 'val_loss': 31.29252, 'test_loss': 35.519866}}
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2024-11-14 17:54:50,551 (client:354) INFO: {'Role': 'Client #9', 'Round': 24, 'Results_raw': {'train_loss': 34.314771, 'val_loss': 33.900137, 'test_loss': 35.374727}}
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2024-11-14 17:56:00,443 (client:354) INFO: {'Role': 'Client #8', 'Round': 24, 'Results_raw': {'train_loss': 32.110114, 'val_loss': 31.71431, 'test_loss': 32.63206}}
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2024-11-14 17:57:10,922 (client:354) INFO: {'Role': 'Client #3', 'Round': 24, 'Results_raw': {'train_loss': 31.201328, 'val_loss': 32.998954, 'test_loss': 33.505289}}
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2024-11-14 17:58:19,645 (client:354) INFO: {'Role': 'Client #7', 'Round': 24, 'Results_raw': {'train_loss': 31.11357, 'val_loss': 30.595179, 'test_loss': 31.377532}}
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2024-11-14 17:59:29,699 (client:354) INFO: {'Role': 'Client #1', 'Round': 24, 'Results_raw': {'train_loss': 32.732172, 'val_loss': 33.429137, 'test_loss': 34.828137}}
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2024-11-14 18:00:34,539 (client:354) INFO: {'Role': 'Client #5', 'Round': 24, 'Results_raw': {'train_loss': 31.595847, 'val_loss': 31.954905, 'test_loss': 33.340153}}
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2024-11-14 18:00:34,543 (server:615) INFO: {'Role': 'Server #', 'Round': 23, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.112299), 'test_loss': np.float64(231544.467688), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.67266), 'val_loss': np.float64(229068.422351), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.112299), 'test_loss': np.float64(231544.467688), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.67266), 'val_loss': np.float64(229068.422351), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.881506), 'test_avg_loss_bottom_decile': np.float64(39.793324), 'test_avg_loss_top_decile': np.float64(43.863739), 'test_avg_loss_min': np.float64(36.69784), 'test_avg_loss_max': np.float64(43.863739), 'test_avg_loss_bottom10%': np.float64(36.69784), 'test_avg_loss_top10%': np.float64(43.863739), 'test_avg_loss_cos1': np.float64(0.998954), 'test_avg_loss_entropy': np.float64(2.301522), 'test_loss_std': np.float64(10596.642207), 'test_loss_bottom_decile': np.float64(224116.000732), 'test_loss_top_decile': np.float64(247040.579834), 'test_loss_min': np.float64(206682.235352), 'test_loss_max': np.float64(247040.579834), 'test_loss_bottom10%': np.float64(206682.235352), 'test_loss_top10%': np.float64(247040.579834), 'test_loss_cos1': np.float64(0.998954), 'test_loss_entropy': np.float64(2.301522), 'val_avg_loss_std': np.float64(2.201193), 'val_avg_loss_bottom_decile': np.float64(39.542328), 'val_avg_loss_top_decile': np.float64(44.041452), 'val_avg_loss_min': np.float64(35.999381), 'val_avg_loss_max': np.float64(44.041452), 'val_avg_loss_bottom10%': np.float64(35.999381), 'val_avg_loss_top10%': np.float64(44.041452), 'val_avg_loss_cos1': np.float64(0.998539), 'val_avg_loss_entropy': np.float64(2.301108), 'val_loss_std': np.float64(12397.120737), 'val_loss_bottom_decile': np.float64(222702.388672), 'val_loss_top_decile': np.float64(248041.459717), 'val_loss_min': np.float64(202748.515747), 'val_loss_max': np.float64(248041.459717), 'val_loss_bottom10%': np.float64(202748.515747), 'val_loss_top10%': np.float64(248041.459717), 'val_loss_cos1': np.float64(0.998539), 'val_loss_entropy': np.float64(2.301108)}}
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2024-11-14 18:00:34,580 (server:353) INFO: Server: Starting evaluation at the end of round 24.
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2024-11-14 18:00:34,581 (server:359) INFO: ----------- Starting a new training round (Round #25) -------------
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2024-11-14 18:04:10,072 (client:354) INFO: {'Role': 'Client #5', 'Round': 25, 'Results_raw': {'train_loss': 31.489455, 'val_loss': 31.597628, 'test_loss': 33.001049}}
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2024-11-14 18:05:16,480 (client:354) INFO: {'Role': 'Client #2', 'Round': 25, 'Results_raw': {'train_loss': 27.877019, 'val_loss': 27.42188, 'test_loss': 28.087666}}
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2024-11-14 18:06:21,596 (client:354) INFO: {'Role': 'Client #4', 'Round': 25, 'Results_raw': {'train_loss': 35.607476, 'val_loss': 35.420249, 'test_loss': 34.832393}}
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2024-11-14 18:07:29,900 (client:354) INFO: {'Role': 'Client #8', 'Round': 25, 'Results_raw': {'train_loss': 32.062841, 'val_loss': 31.744943, 'test_loss': 32.839058}}
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2024-11-14 18:08:38,357 (client:354) INFO: {'Role': 'Client #10', 'Round': 25, 'Results_raw': {'train_loss': 32.257923, 'val_loss': 32.076198, 'test_loss': 32.839048}}
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2024-11-14 18:09:48,606 (client:354) INFO: {'Role': 'Client #3', 'Round': 25, 'Results_raw': {'train_loss': 31.12925, 'val_loss': 33.12936, 'test_loss': 33.609113}}
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2024-11-14 18:10:58,261 (client:354) INFO: {'Role': 'Client #9', 'Round': 25, 'Results_raw': {'train_loss': 34.291548, 'val_loss': 34.259816, 'test_loss': 36.438411}}
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2024-11-14 18:12:06,658 (client:354) INFO: {'Role': 'Client #1', 'Round': 25, 'Results_raw': {'train_loss': 32.66729, 'val_loss': 32.651418, 'test_loss': 33.628647}}
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2024-11-14 18:13:13,564 (client:354) INFO: {'Role': 'Client #6', 'Round': 25, 'Results_raw': {'train_loss': 33.046645, 'val_loss': 31.164282, 'test_loss': 34.106387}}
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2024-11-14 18:14:18,895 (client:354) INFO: {'Role': 'Client #7', 'Round': 25, 'Results_raw': {'train_loss': 31.053607, 'val_loss': 30.548758, 'test_loss': 31.320795}}
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2024-11-14 18:14:18,898 (server:615) INFO: {'Role': 'Server #', 'Round': 24, 'Results_weighted_avg': {'test_avg_loss': np.float64(41.02948), 'test_loss': np.float64(231078.031909), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.566288), 'val_loss': np.float64(228469.332178), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(41.02948), 'test_loss': np.float64(231078.031909), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.566288), 'val_loss': np.float64(228469.332178), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.909671), 'test_avg_loss_bottom_decile': np.float64(39.692057), 'test_avg_loss_top_decile': np.float64(43.747963), 'test_avg_loss_min': np.float64(36.562366), 'test_avg_loss_max': np.float64(43.747963), 'test_avg_loss_bottom10%': np.float64(36.562366), 'test_avg_loss_top10%': np.float64(43.747963), 'test_avg_loss_cos1': np.float64(0.998919), 'test_avg_loss_entropy': np.float64(2.301485), 'test_loss_std': np.float64(10755.264667), 'test_loss_bottom_decile': np.float64(223545.667236), 'test_loss_top_decile': np.float64(246388.528931), 'test_loss_min': np.float64(205919.244629), 'test_loss_max': np.float64(246388.528931), 'test_loss_bottom10%': np.float64(205919.244629), 'test_loss_top10%': np.float64(246388.528931), 'test_loss_cos1': np.float64(0.998919), 'test_loss_entropy': np.float64(2.301485), 'val_avg_loss_std': np.float64(2.21792), 'val_avg_loss_bottom_decile': np.float64(39.403842), 'val_avg_loss_top_decile': np.float64(44.051356), 'val_avg_loss_min': np.float64(35.844712), 'val_avg_loss_max': np.float64(44.051356), 'val_avg_loss_bottom10%': np.float64(35.844712), 'val_avg_loss_top10%': np.float64(44.051356), 'val_avg_loss_cos1': np.float64(0.998509), 'val_avg_loss_entropy': np.float64(2.301077), 'val_loss_std': np.float64(12491.323565), 'val_loss_bottom_decile': np.float64(221922.438965), 'val_loss_top_decile': np.float64(248097.238647), 'val_loss_min': np.float64(201877.417969), 'val_loss_max': np.float64(248097.238647), 'val_loss_bottom10%': np.float64(201877.417969), 'val_loss_top10%': np.float64(248097.238647), 'val_loss_cos1': np.float64(0.998509), 'val_loss_entropy': np.float64(2.301077)}}
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2024-11-14 18:14:18,937 (server:353) INFO: Server: Starting evaluation at the end of round 25.
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2024-11-14 18:14:18,938 (server:359) INFO: ----------- Starting a new training round (Round #26) -------------
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2024-11-14 18:17:46,163 (client:354) INFO: {'Role': 'Client #8', 'Round': 26, 'Results_raw': {'train_loss': 32.066551, 'val_loss': 31.590282, 'test_loss': 32.651182}}
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2024-11-14 18:18:53,145 (client:354) INFO: {'Role': 'Client #9', 'Round': 26, 'Results_raw': {'train_loss': 34.232459, 'val_loss': 33.638157, 'test_loss': 35.40865}}
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2024-11-14 18:19:58,013 (client:354) INFO: {'Role': 'Client #7', 'Round': 26, 'Results_raw': {'train_loss': 30.976453, 'val_loss': 30.634828, 'test_loss': 31.218094}}
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2024-11-14 18:21:04,433 (client:354) INFO: {'Role': 'Client #10', 'Round': 26, 'Results_raw': {'train_loss': 32.203513, 'val_loss': 31.906409, 'test_loss': 32.904047}}
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2024-11-14 18:22:09,815 (client:354) INFO: {'Role': 'Client #2', 'Round': 26, 'Results_raw': {'train_loss': 27.868982, 'val_loss': 27.41234, 'test_loss': 27.978631}}
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2024-11-14 18:23:15,659 (client:354) INFO: {'Role': 'Client #5', 'Round': 26, 'Results_raw': {'train_loss': 31.505273, 'val_loss': 31.782604, 'test_loss': 32.95822}}
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2024-11-14 18:24:22,657 (client:354) INFO: {'Role': 'Client #1', 'Round': 26, 'Results_raw': {'train_loss': 32.565109, 'val_loss': 32.639214, 'test_loss': 33.648931}}
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2024-11-14 18:25:28,417 (client:354) INFO: {'Role': 'Client #6', 'Round': 26, 'Results_raw': {'train_loss': 33.010201, 'val_loss': 31.246401, 'test_loss': 35.277725}}
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2024-11-14 18:26:35,618 (client:354) INFO: {'Role': 'Client #4', 'Round': 26, 'Results_raw': {'train_loss': 35.502398, 'val_loss': 35.451724, 'test_loss': 34.762848}}
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2024-11-14 18:27:43,286 (client:354) INFO: {'Role': 'Client #3', 'Round': 26, 'Results_raw': {'train_loss': 31.06236, 'val_loss': 33.023729, 'test_loss': 33.549876}}
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2024-11-14 18:27:43,289 (server:615) INFO: {'Role': 'Server #', 'Round': 25, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.731652), 'test_loss': np.float64(229400.666846), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.28622), 'val_loss': np.float64(226891.992529), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.731652), 'test_loss': np.float64(229400.666846), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.28622), 'val_loss': np.float64(226891.992529), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.871313), 'test_avg_loss_bottom_decile': np.float64(39.368611), 'test_avg_loss_top_decile': np.float64(43.38475), 'test_avg_loss_min': np.float64(36.304163), 'test_avg_loss_max': np.float64(43.38475), 'test_avg_loss_bottom10%': np.float64(36.304163), 'test_avg_loss_top10%': np.float64(43.38475), 'test_avg_loss_cos1': np.float64(0.998946), 'test_avg_loss_entropy': np.float64(2.301513), 'test_loss_std': np.float64(10539.23536), 'test_loss_bottom_decile': np.float64(221724.019043), 'test_loss_top_decile': np.float64(244342.911377), 'test_loss_min': np.float64(204465.04541), 'test_loss_max': np.float64(244342.911377), 'test_loss_bottom10%': np.float64(204465.04541), 'test_loss_top10%': np.float64(244342.911377), 'test_loss_cos1': np.float64(0.998946), 'test_loss_entropy': np.float64(2.301513), 'val_avg_loss_std': np.float64(2.166593), 'val_avg_loss_bottom_decile': np.float64(39.122672), 'val_avg_loss_top_decile': np.float64(43.699584), 'val_avg_loss_min': np.float64(35.634333), 'val_avg_loss_max': np.float64(43.699584), 'val_avg_loss_bottom10%': np.float64(35.634333), 'val_avg_loss_top10%': np.float64(43.699584), 'val_avg_loss_cos1': np.float64(0.998557), 'val_avg_loss_entropy': np.float64(2.301126), 'val_loss_std': np.float64(12202.251397), 'val_loss_bottom_decile': np.float64(220338.888916), 'val_loss_top_decile': np.float64(246116.057007), 'val_loss_min': np.float64(200692.56189), 'val_loss_max': np.float64(246116.057007), 'val_loss_bottom10%': np.float64(200692.56189), 'val_loss_top10%': np.float64(246116.057007), 'val_loss_cos1': np.float64(0.998557), 'val_loss_entropy': np.float64(2.301126)}}
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2024-11-14 18:27:43,324 (server:353) INFO: Server: Starting evaluation at the end of round 26.
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2024-11-14 18:27:43,325 (server:359) INFO: ----------- Starting a new training round (Round #27) -------------
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2024-11-14 18:31:13,407 (client:354) INFO: {'Role': 'Client #9', 'Round': 27, 'Results_raw': {'train_loss': 34.203604, 'val_loss': 33.795229, 'test_loss': 35.25307}}
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2024-11-14 18:32:20,781 (client:354) INFO: {'Role': 'Client #8', 'Round': 27, 'Results_raw': {'train_loss': 32.017677, 'val_loss': 31.59694, 'test_loss': 32.76742}}
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2024-11-14 18:33:27,731 (client:354) INFO: {'Role': 'Client #4', 'Round': 27, 'Results_raw': {'train_loss': 35.487417, 'val_loss': 35.739399, 'test_loss': 35.04754}}
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2024-11-14 18:34:35,097 (client:354) INFO: {'Role': 'Client #1', 'Round': 27, 'Results_raw': {'train_loss': 32.566344, 'val_loss': 32.73805, 'test_loss': 33.645938}}
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2024-11-14 18:35:42,432 (client:354) INFO: {'Role': 'Client #2', 'Round': 27, 'Results_raw': {'train_loss': 27.749708, 'val_loss': 27.47673, 'test_loss': 28.143392}}
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2024-11-14 18:36:49,444 (client:354) INFO: {'Role': 'Client #3', 'Round': 27, 'Results_raw': {'train_loss': 31.086746, 'val_loss': 33.164214, 'test_loss': 33.644448}}
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2024-11-14 18:37:56,039 (client:354) INFO: {'Role': 'Client #7', 'Round': 27, 'Results_raw': {'train_loss': 30.989434, 'val_loss': 30.58062, 'test_loss': 31.278927}}
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2024-11-14 18:39:03,724 (client:354) INFO: {'Role': 'Client #10', 'Round': 27, 'Results_raw': {'train_loss': 32.145334, 'val_loss': 31.950152, 'test_loss': 32.749431}}
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2024-11-14 18:40:14,200 (client:354) INFO: {'Role': 'Client #6', 'Round': 27, 'Results_raw': {'train_loss': 33.060938, 'val_loss': 31.108323, 'test_loss': 34.682963}}
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2024-11-14 18:41:26,316 (client:354) INFO: {'Role': 'Client #5', 'Round': 27, 'Results_raw': {'train_loss': 31.417115, 'val_loss': 31.94449, 'test_loss': 33.58678}}
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2024-11-14 18:41:26,319 (server:615) INFO: {'Role': 'Server #', 'Round': 26, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.857413), 'test_loss': np.float64(230108.949438), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.409848), 'val_loss': np.float64(227588.261316), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.857413), 'test_loss': np.float64(230108.949438), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.409848), 'val_loss': np.float64(227588.261316), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.90496), 'test_avg_loss_bottom_decile': np.float64(39.52453), 'test_avg_loss_top_decile': np.float64(43.499462), 'test_avg_loss_min': np.float64(36.318895), 'test_avg_loss_max': np.float64(43.499462), 'test_avg_loss_bottom10%': np.float64(36.318895), 'test_avg_loss_top10%': np.float64(43.499462), 'test_avg_loss_cos1': np.float64(0.998915), 'test_avg_loss_entropy': np.float64(2.30148), 'test_loss_std': np.float64(10728.735155), 'test_loss_bottom_decile': np.float64(222602.154419), 'test_loss_top_decile': np.float64(244988.972534), 'test_loss_min': np.float64(204548.017822), 'test_loss_max': np.float64(244988.972534), 'test_loss_bottom10%': np.float64(204548.017822), 'test_loss_top10%': np.float64(244988.972534), 'test_loss_cos1': np.float64(0.998915), 'test_loss_entropy': np.float64(2.30148), 'val_avg_loss_std': np.float64(2.229355), 'val_avg_loss_bottom_decile': np.float64(39.263031), 'val_avg_loss_top_decile': np.float64(43.84086), 'val_avg_loss_min': np.float64(35.650945), 'val_avg_loss_max': np.float64(43.84086), 'val_avg_loss_bottom10%': np.float64(35.650945), 'val_avg_loss_top10%': np.float64(43.84086), 'val_avg_loss_cos1': np.float64(0.998482), 'val_avg_loss_entropy': np.float64(2.301049), 'val_loss_std': np.float64(12555.727463), 'val_loss_bottom_decile': np.float64(221129.392944), 'val_loss_top_decile': np.float64(246911.724243), 'val_loss_min': np.float64(200786.124634), 'val_loss_max': np.float64(246911.724243), 'val_loss_bottom10%': np.float64(200786.124634), 'val_loss_top10%': np.float64(246911.724243), 'val_loss_cos1': np.float64(0.998482), 'val_loss_entropy': np.float64(2.301049)}}
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2024-11-14 18:41:26,359 (server:353) INFO: Server: Starting evaluation at the end of round 27.
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2024-11-14 18:41:26,360 (server:359) INFO: ----------- Starting a new training round (Round #28) -------------
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2024-11-14 18:45:04,604 (client:354) INFO: {'Role': 'Client #8', 'Round': 28, 'Results_raw': {'train_loss': 31.951255, 'val_loss': 31.69827, 'test_loss': 32.952787}}
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2024-11-14 18:46:11,243 (client:354) INFO: {'Role': 'Client #9', 'Round': 28, 'Results_raw': {'train_loss': 34.135131, 'val_loss': 33.589974, 'test_loss': 35.225892}}
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2024-11-14 18:47:16,631 (client:354) INFO: {'Role': 'Client #3', 'Round': 28, 'Results_raw': {'train_loss': 31.00538, 'val_loss': 33.365013, 'test_loss': 34.021628}}
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2024-11-14 18:48:23,593 (client:354) INFO: {'Role': 'Client #4', 'Round': 28, 'Results_raw': {'train_loss': 35.437431, 'val_loss': 35.257049, 'test_loss': 34.708199}}
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2024-11-14 18:49:30,525 (client:354) INFO: {'Role': 'Client #6', 'Round': 28, 'Results_raw': {'train_loss': 32.967812, 'val_loss': 31.388977, 'test_loss': 35.296243}}
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2024-11-14 18:50:38,621 (client:354) INFO: {'Role': 'Client #1', 'Round': 28, 'Results_raw': {'train_loss': 32.474607, 'val_loss': 32.696366, 'test_loss': 33.550396}}
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2024-11-14 18:51:46,125 (client:354) INFO: {'Role': 'Client #2', 'Round': 28, 'Results_raw': {'train_loss': 27.742232, 'val_loss': 27.306254, 'test_loss': 28.25908}}
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2024-11-14 18:52:53,916 (client:354) INFO: {'Role': 'Client #10', 'Round': 28, 'Results_raw': {'train_loss': 32.135058, 'val_loss': 32.058754, 'test_loss': 32.873166}}
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2024-11-14 18:54:01,889 (client:354) INFO: {'Role': 'Client #7', 'Round': 28, 'Results_raw': {'train_loss': 30.914525, 'val_loss': 30.473219, 'test_loss': 31.239459}}
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2024-11-14 18:55:10,326 (client:354) INFO: {'Role': 'Client #5', 'Round': 28, 'Results_raw': {'train_loss': 31.386074, 'val_loss': 31.823391, 'test_loss': 33.343125}}
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2024-11-14 18:55:10,329 (server:615) INFO: {'Role': 'Server #', 'Round': 27, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.736359), 'test_loss': np.float64(229427.17533), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.272786), 'val_loss': np.float64(226816.328809), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.736359), 'test_loss': np.float64(229427.17533), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.272786), 'val_loss': np.float64(226816.328809), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.90049), 'test_avg_loss_bottom_decile': np.float64(39.466821), 'test_avg_loss_top_decile': np.float64(43.390703), 'test_avg_loss_min': np.float64(36.20683), 'test_avg_loss_max': np.float64(43.390703), 'test_avg_loss_bottom10%': np.float64(36.20683), 'test_avg_loss_top10%': np.float64(43.390703), 'test_avg_loss_cos1': np.float64(0.998914), 'test_avg_loss_entropy': np.float64(2.301479), 'test_loss_std': np.float64(10703.56158), 'test_loss_bottom_decile': np.float64(222277.13562), 'test_loss_top_decile': np.float64(244376.436768), 'test_loss_min': np.float64(203916.864014), 'test_loss_max': np.float64(244376.436768), 'test_loss_bottom10%': np.float64(203916.864014), 'test_loss_top10%': np.float64(244376.436768), 'test_loss_cos1': np.float64(0.998914), 'test_loss_entropy': np.float64(2.301479), 'val_avg_loss_std': np.float64(2.218247), 'val_avg_loss_bottom_decile': np.float64(39.121017), 'val_avg_loss_top_decile': np.float64(43.768859), 'val_avg_loss_min': np.float64(35.508595), 'val_avg_loss_max': np.float64(43.768859), 'val_avg_loss_bottom10%': np.float64(35.508595), 'val_avg_loss_top10%': np.float64(43.768859), 'val_avg_loss_cos1': np.float64(0.998487), 'val_avg_loss_entropy': np.float64(2.301054), 'val_loss_std': np.float64(12493.164928), 'val_loss_bottom_decile': np.float64(220329.56897), 'val_loss_top_decile': np.float64(246506.211182), 'val_loss_min': np.float64(199984.40625), 'val_loss_max': np.float64(246506.211182), 'val_loss_bottom10%': np.float64(199984.40625), 'val_loss_top10%': np.float64(246506.211182), 'val_loss_cos1': np.float64(0.998487), 'val_loss_entropy': np.float64(2.301054)}}
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2024-11-14 18:55:10,367 (server:353) INFO: Server: Starting evaluation at the end of round 28.
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2024-11-14 18:55:10,367 (server:359) INFO: ----------- Starting a new training round (Round #29) -------------
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2024-11-14 18:58:37,796 (client:354) INFO: {'Role': 'Client #1', 'Round': 29, 'Results_raw': {'train_loss': 32.452558, 'val_loss': 32.813896, 'test_loss': 33.624439}}
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2024-11-14 18:59:44,927 (client:354) INFO: {'Role': 'Client #5', 'Round': 29, 'Results_raw': {'train_loss': 31.302629, 'val_loss': 31.669341, 'test_loss': 33.166506}}
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2024-11-14 19:00:52,418 (client:354) INFO: {'Role': 'Client #7', 'Round': 29, 'Results_raw': {'train_loss': 30.812064, 'val_loss': 30.476178, 'test_loss': 31.349345}}
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2024-11-14 19:01:59,081 (client:354) INFO: {'Role': 'Client #6', 'Round': 29, 'Results_raw': {'train_loss': 32.97719, 'val_loss': 30.939053, 'test_loss': 34.009049}}
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2024-11-14 19:03:05,807 (client:354) INFO: {'Role': 'Client #3', 'Round': 29, 'Results_raw': {'train_loss': 30.90428, 'val_loss': 33.321202, 'test_loss': 33.767462}}
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2024-11-14 19:04:12,625 (client:354) INFO: {'Role': 'Client #4', 'Round': 29, 'Results_raw': {'train_loss': 35.422312, 'val_loss': 35.361322, 'test_loss': 34.844605}}
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2024-11-14 19:05:16,565 (client:354) INFO: {'Role': 'Client #9', 'Round': 29, 'Results_raw': {'train_loss': 34.134348, 'val_loss': 33.472445, 'test_loss': 35.393707}}
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2024-11-14 19:06:23,073 (client:354) INFO: {'Role': 'Client #10', 'Round': 29, 'Results_raw': {'train_loss': 32.036131, 'val_loss': 31.862703, 'test_loss': 32.52923}}
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2024-11-14 19:07:30,312 (client:354) INFO: {'Role': 'Client #2', 'Round': 29, 'Results_raw': {'train_loss': 27.700156, 'val_loss': 27.425368, 'test_loss': 28.345667}}
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2024-11-14 19:08:37,900 (client:354) INFO: {'Role': 'Client #8', 'Round': 29, 'Results_raw': {'train_loss': 31.948258, 'val_loss': 31.88761, 'test_loss': 33.188088}}
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2024-11-14 19:08:37,903 (server:615) INFO: {'Role': 'Server #', 'Round': 28, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.794812), 'test_loss': np.float64(229756.381274), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.327477), 'val_loss': np.float64(227124.352966), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.794812), 'test_loss': np.float64(229756.381274), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.327477), 'val_loss': np.float64(227124.352966), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.885501), 'test_avg_loss_bottom_decile': np.float64(39.397085), 'test_avg_loss_top_decile': np.float64(43.528804), 'test_avg_loss_min': np.float64(36.405254), 'test_avg_loss_max': np.float64(43.528804), 'test_avg_loss_bottom10%': np.float64(36.405254), 'test_avg_loss_top10%': np.float64(43.528804), 'test_avg_loss_cos1': np.float64(0.998934), 'test_avg_loss_entropy': np.float64(2.301501), 'test_loss_std': np.float64(10619.13944), 'test_loss_bottom_decile': np.float64(221884.38208), 'test_loss_top_decile': np.float64(245154.223022), 'test_loss_min': np.float64(205034.38855), 'test_loss_max': np.float64(245154.223022), 'test_loss_bottom10%': np.float64(205034.38855), 'test_loss_top10%': np.float64(245154.223022), 'test_loss_cos1': np.float64(0.998934), 'test_loss_entropy': np.float64(2.301501), 'val_avg_loss_std': np.float64(2.196947), 'val_avg_loss_bottom_decile': np.float64(39.156581), 'val_avg_loss_top_decile': np.float64(43.760919), 'val_avg_loss_min': np.float64(35.701965), 'val_avg_loss_max': np.float64(43.760919), 'val_avg_loss_bottom10%': np.float64(35.701965), 'val_avg_loss_top10%': np.float64(43.760919), 'val_avg_loss_cos1': np.float64(0.998519), 'val_avg_loss_entropy': np.float64(2.301089), 'val_loss_std': np.float64(12373.202726), 'val_loss_bottom_decile': np.float64(220529.861816), 'val_loss_top_decile': np.float64(246461.495117), 'val_loss_min': np.float64(201073.469482), 'val_loss_max': np.float64(246461.495117), 'val_loss_bottom10%': np.float64(201073.469482), 'val_loss_top10%': np.float64(246461.495117), 'val_loss_cos1': np.float64(0.998519), 'val_loss_entropy': np.float64(2.301089)}}
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2024-11-14 19:08:37,936 (server:353) INFO: Server: Starting evaluation at the end of round 29.
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2024-11-14 19:08:37,937 (server:359) INFO: ----------- Starting a new training round (Round #30) -------------
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2024-11-14 19:12:11,562 (client:354) INFO: {'Role': 'Client #7', 'Round': 30, 'Results_raw': {'train_loss': 30.846686, 'val_loss': 30.334305, 'test_loss': 31.056403}}
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2024-11-14 19:13:18,664 (client:354) INFO: {'Role': 'Client #6', 'Round': 30, 'Results_raw': {'train_loss': 33.003492, 'val_loss': 31.035985, 'test_loss': 34.611053}}
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2024-11-14 19:14:26,699 (client:354) INFO: {'Role': 'Client #10', 'Round': 30, 'Results_raw': {'train_loss': 31.971212, 'val_loss': 31.854603, 'test_loss': 32.777894}}
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2024-11-14 19:15:34,240 (client:354) INFO: {'Role': 'Client #8', 'Round': 30, 'Results_raw': {'train_loss': 31.890903, 'val_loss': 31.856163, 'test_loss': 32.911169}}
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2024-11-14 19:16:43,359 (client:354) INFO: {'Role': 'Client #3', 'Round': 30, 'Results_raw': {'train_loss': 30.931919, 'val_loss': 33.16632, 'test_loss': 33.999366}}
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2024-11-14 19:17:50,604 (client:354) INFO: {'Role': 'Client #2', 'Round': 30, 'Results_raw': {'train_loss': 27.642197, 'val_loss': 27.316518, 'test_loss': 27.978402}}
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2024-11-14 19:18:56,860 (client:354) INFO: {'Role': 'Client #5', 'Round': 30, 'Results_raw': {'train_loss': 31.319342, 'val_loss': 31.512696, 'test_loss': 32.926587}}
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2024-11-14 19:20:04,576 (client:354) INFO: {'Role': 'Client #4', 'Round': 30, 'Results_raw': {'train_loss': 35.341434, 'val_loss': 35.263763, 'test_loss': 34.707435}}
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2024-11-14 19:21:09,604 (client:354) INFO: {'Role': 'Client #9', 'Round': 30, 'Results_raw': {'train_loss': 34.119009, 'val_loss': 34.085754, 'test_loss': 35.045088}}
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2024-11-14 19:22:16,693 (client:354) INFO: {'Role': 'Client #1', 'Round': 30, 'Results_raw': {'train_loss': 32.381109, 'val_loss': 32.847239, 'test_loss': 33.908605}}
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2024-11-14 19:22:16,695 (server:615) INFO: {'Role': 'Server #', 'Round': 29, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.53614), 'test_loss': np.float64(228299.541418), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.077099), 'val_loss': np.float64(225714.219446), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.53614), 'test_loss': np.float64(228299.541418), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.077099), 'val_loss': np.float64(225714.219446), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.902146), 'test_avg_loss_bottom_decile': np.float64(39.178822), 'test_avg_loss_top_decile': np.float64(43.180965), 'test_avg_loss_min': np.float64(36.06119), 'test_avg_loss_max': np.float64(43.180965), 'test_avg_loss_bottom10%': np.float64(36.06119), 'test_avg_loss_top10%': np.float64(43.180965), 'test_avg_loss_cos1': np.float64(0.998901), 'test_avg_loss_entropy': np.float64(2.301467), 'test_loss_std': np.float64(10712.886727), 'test_loss_bottom_decile': np.float64(220655.12439), 'test_loss_top_decile': np.float64(243195.195923), 'test_loss_min': np.float64(203096.620483), 'test_loss_max': np.float64(243195.195923), 'test_loss_bottom10%': np.float64(203096.620483), 'test_loss_top10%': np.float64(243195.195923), 'test_loss_cos1': np.float64(0.998901), 'test_loss_entropy': np.float64(2.301467), 'val_avg_loss_std': np.float64(2.213833), 'val_avg_loss_bottom_decile': np.float64(38.933783), 'val_avg_loss_top_decile': np.float64(43.607808), 'val_avg_loss_min': np.float64(35.385506), 'val_avg_loss_max': np.float64(43.607808), 'val_avg_loss_bottom10%': np.float64(35.385506), 'val_avg_loss_top10%': np.float64(43.607808), 'val_avg_loss_cos1': np.float64(0.998478), 'val_avg_loss_entropy': np.float64(2.301047), 'val_loss_std': np.float64(12468.308078), 'val_loss_bottom_decile': np.float64(219275.066406), 'val_loss_top_decile': np.float64(245599.172607), 'val_loss_min': np.float64(199291.169556), 'val_loss_max': np.float64(245599.172607), 'val_loss_bottom10%': np.float64(199291.169556), 'val_loss_top10%': np.float64(245599.172607), 'val_loss_cos1': np.float64(0.998478), 'val_loss_entropy': np.float64(2.301047)}}
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2024-11-14 19:22:16,733 (server:353) INFO: Server: Starting evaluation at the end of round 30.
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2024-11-14 19:22:16,733 (server:359) INFO: ----------- Starting a new training round (Round #31) -------------
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2024-11-14 19:25:51,806 (client:354) INFO: {'Role': 'Client #4', 'Round': 31, 'Results_raw': {'train_loss': 35.310485, 'val_loss': 35.146755, 'test_loss': 34.659392}}
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2024-11-14 19:26:58,189 (client:354) INFO: {'Role': 'Client #2', 'Round': 31, 'Results_raw': {'train_loss': 27.684703, 'val_loss': 27.247345, 'test_loss': 28.105845}}
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2024-11-14 19:28:04,923 (client:354) INFO: {'Role': 'Client #5', 'Round': 31, 'Results_raw': {'train_loss': 31.247137, 'val_loss': 31.951895, 'test_loss': 33.440426}}
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2024-11-14 19:29:10,313 (client:354) INFO: {'Role': 'Client #10', 'Round': 31, 'Results_raw': {'train_loss': 31.970169, 'val_loss': 31.957077, 'test_loss': 32.744568}}
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2024-11-14 19:30:26,559 (client:354) INFO: {'Role': 'Client #3', 'Round': 31, 'Results_raw': {'train_loss': 30.91677, 'val_loss': 32.896275, 'test_loss': 33.583893}}
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2024-11-14 19:31:34,328 (client:354) INFO: {'Role': 'Client #9', 'Round': 31, 'Results_raw': {'train_loss': 34.005509, 'val_loss': 33.59759, 'test_loss': 35.599078}}
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2024-11-14 19:32:42,193 (client:354) INFO: {'Role': 'Client #1', 'Round': 31, 'Results_raw': {'train_loss': 32.343563, 'val_loss': 32.728533, 'test_loss': 33.616842}}
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2024-11-14 19:33:50,586 (client:354) INFO: {'Role': 'Client #6', 'Round': 31, 'Results_raw': {'train_loss': 32.911538, 'val_loss': 30.977451, 'test_loss': 33.66902}}
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2024-11-14 19:34:59,430 (client:354) INFO: {'Role': 'Client #7', 'Round': 31, 'Results_raw': {'train_loss': 30.8124, 'val_loss': 30.33856, 'test_loss': 30.955332}}
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2024-11-14 19:36:08,979 (client:354) INFO: {'Role': 'Client #8', 'Round': 31, 'Results_raw': {'train_loss': 31.936174, 'val_loss': 31.721237, 'test_loss': 32.964183}}
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2024-11-14 19:36:08,982 (server:615) INFO: {'Role': 'Server #', 'Round': 30, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.405025), 'test_loss': np.float64(227561.099304), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.943496), 'val_loss': np.float64(224961.770911), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.405025), 'test_loss': np.float64(227561.099304), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.943496), 'val_loss': np.float64(224961.770911), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.898922), 'test_avg_loss_bottom_decile': np.float64(39.042141), 'test_avg_loss_top_decile': np.float64(43.013391), 'test_avg_loss_min': np.float64(35.910314), 'test_avg_loss_max': np.float64(43.013391), 'test_avg_loss_bottom10%': np.float64(35.910314), 'test_avg_loss_top10%': np.float64(43.013391), 'test_avg_loss_cos1': np.float64(0.998897), 'test_avg_loss_entropy': np.float64(2.301462), 'test_loss_std': np.float64(10694.730543), 'test_loss_bottom_decile': np.float64(219885.335327), 'test_loss_top_decile': np.float64(242251.417969), 'test_loss_min': np.float64(202246.887451), 'test_loss_max': np.float64(242251.417969), 'test_loss_bottom10%': np.float64(202246.887451), 'test_loss_top10%': np.float64(242251.417969), 'test_loss_cos1': np.float64(0.998897), 'test_loss_entropy': np.float64(2.301462), 'val_avg_loss_std': np.float64(2.211817), 'val_avg_loss_bottom_decile': np.float64(38.80599), 'val_avg_loss_top_decile': np.float64(43.467406), 'val_avg_loss_min': np.float64(35.207969), 'val_avg_loss_max': np.float64(43.467406), 'val_avg_loss_bottom10%': np.float64(35.207969), 'val_avg_loss_top10%': np.float64(43.467406), 'val_avg_loss_cos1': np.float64(0.99847), 'val_avg_loss_entropy': np.float64(2.301038), 'val_loss_std': np.float64(12456.955301), 'val_loss_bottom_decile': np.float64(218555.337036), 'val_loss_top_decile': np.float64(244808.428955), 'val_loss_min': np.float64(198291.280762), 'val_loss_max': np.float64(244808.428955), 'val_loss_bottom10%': np.float64(198291.280762), 'val_loss_top10%': np.float64(244808.428955), 'val_loss_cos1': np.float64(0.99847), 'val_loss_entropy': np.float64(2.301038)}}
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2024-11-14 19:36:09,020 (server:353) INFO: Server: Starting evaluation at the end of round 31.
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2024-11-14 19:36:09,021 (server:359) INFO: ----------- Starting a new training round (Round #32) -------------
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2024-11-14 19:39:42,454 (client:354) INFO: {'Role': 'Client #5', 'Round': 32, 'Results_raw': {'train_loss': 31.196108, 'val_loss': 31.753313, 'test_loss': 33.228438}}
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2024-11-14 19:40:52,609 (client:354) INFO: {'Role': 'Client #10', 'Round': 32, 'Results_raw': {'train_loss': 31.955733, 'val_loss': 31.858289, 'test_loss': 32.722412}}
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2024-11-14 19:42:04,002 (client:354) INFO: {'Role': 'Client #3', 'Round': 32, 'Results_raw': {'train_loss': 30.858822, 'val_loss': 33.17774, 'test_loss': 33.736203}}
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2024-11-14 19:43:13,507 (client:354) INFO: {'Role': 'Client #2', 'Round': 32, 'Results_raw': {'train_loss': 27.609951, 'val_loss': 27.338074, 'test_loss': 28.270705}}
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2024-11-14 19:44:28,233 (client:354) INFO: {'Role': 'Client #7', 'Round': 32, 'Results_raw': {'train_loss': 30.689509, 'val_loss': 30.503987, 'test_loss': 31.254556}}
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2024-11-14 19:45:43,392 (client:354) INFO: {'Role': 'Client #8', 'Round': 32, 'Results_raw': {'train_loss': 31.803628, 'val_loss': 31.728281, 'test_loss': 32.820343}}
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2024-11-14 19:46:57,356 (client:354) INFO: {'Role': 'Client #9', 'Round': 32, 'Results_raw': {'train_loss': 33.997817, 'val_loss': 33.786962, 'test_loss': 35.924784}}
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2024-11-14 19:48:13,123 (client:354) INFO: {'Role': 'Client #1', 'Round': 32, 'Results_raw': {'train_loss': 32.314202, 'val_loss': 32.576834, 'test_loss': 33.246993}}
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2024-11-14 19:49:28,249 (client:354) INFO: {'Role': 'Client #4', 'Round': 32, 'Results_raw': {'train_loss': 35.44062, 'val_loss': 35.496834, 'test_loss': 34.864157}}
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2024-11-14 19:50:38,665 (client:354) INFO: {'Role': 'Client #6', 'Round': 32, 'Results_raw': {'train_loss': 32.832349, 'val_loss': 31.085991, 'test_loss': 35.219138}}
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2024-11-14 19:50:38,668 (server:615) INFO: {'Role': 'Server #', 'Round': 31, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.605668), 'test_loss': np.float64(228691.124561), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.136578), 'val_loss': np.float64(226049.206274), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.605668), 'test_loss': np.float64(228691.124561), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.136578), 'val_loss': np.float64(226049.206274), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.904406), 'test_avg_loss_bottom_decile': np.float64(39.175062), 'test_avg_loss_top_decile': np.float64(43.292152), 'test_avg_loss_min': np.float64(36.122875), 'test_avg_loss_max': np.float64(43.292152), 'test_avg_loss_bottom10%': np.float64(36.122875), 'test_avg_loss_top10%': np.float64(43.292152), 'test_avg_loss_cos1': np.float64(0.998902), 'test_avg_loss_entropy': np.float64(2.301468), 'test_loss_std': np.float64(10725.614235), 'test_loss_bottom_decile': np.float64(220633.95166), 'test_loss_top_decile': np.float64(243821.402588), 'test_loss_min': np.float64(203444.030273), 'test_loss_max': np.float64(243821.402588), 'test_loss_bottom10%': np.float64(203444.030273), 'test_loss_top10%': np.float64(243821.402588), 'test_loss_cos1': np.float64(0.998902), 'test_loss_entropy': np.float64(2.301468), 'val_avg_loss_std': np.float64(2.215529), 'val_avg_loss_bottom_decile': np.float64(38.96316), 'val_avg_loss_top_decile': np.float64(43.627484), 'val_avg_loss_min': np.float64(35.422844), 'val_avg_loss_max': np.float64(43.627484), 'val_avg_loss_bottom10%': np.float64(35.422844), 'val_avg_loss_top10%': np.float64(43.627484), 'val_avg_loss_cos1': np.float64(0.99848), 'val_avg_loss_entropy': np.float64(2.301048), 'val_loss_std': np.float64(12477.860436), 'val_loss_bottom_decile': np.float64(219440.515503), 'val_loss_top_decile': np.float64(245709.990356), 'val_loss_min': np.float64(199501.456543), 'val_loss_max': np.float64(245709.990356), 'val_loss_bottom10%': np.float64(199501.456543), 'val_loss_top10%': np.float64(245709.990356), 'val_loss_cos1': np.float64(0.99848), 'val_loss_entropy': np.float64(2.301048)}}
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2024-11-14 19:50:38,708 (server:353) INFO: Server: Starting evaluation at the end of round 32.
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2024-11-14 19:50:38,708 (server:359) INFO: ----------- Starting a new training round (Round #33) -------------
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2024-11-14 19:54:17,865 (client:354) INFO: {'Role': 'Client #9', 'Round': 33, 'Results_raw': {'train_loss': 33.938131, 'val_loss': 33.580418, 'test_loss': 34.987475}}
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2024-11-14 19:55:26,777 (client:354) INFO: {'Role': 'Client #8', 'Round': 33, 'Results_raw': {'train_loss': 31.890895, 'val_loss': 31.692441, 'test_loss': 32.687464}}
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2024-11-14 19:56:36,652 (client:354) INFO: {'Role': 'Client #7', 'Round': 33, 'Results_raw': {'train_loss': 30.647202, 'val_loss': 30.381207, 'test_loss': 31.14498}}
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2024-11-14 19:57:47,310 (client:354) INFO: {'Role': 'Client #10', 'Round': 33, 'Results_raw': {'train_loss': 31.908671, 'val_loss': 31.878052, 'test_loss': 32.791399}}
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2024-11-14 19:58:57,840 (client:354) INFO: {'Role': 'Client #1', 'Round': 33, 'Results_raw': {'train_loss': 32.26754, 'val_loss': 32.657603, 'test_loss': 33.558002}}
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2024-11-14 20:00:07,221 (client:354) INFO: {'Role': 'Client #5', 'Round': 33, 'Results_raw': {'train_loss': 31.196462, 'val_loss': 31.534024, 'test_loss': 32.918468}}
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2024-11-14 20:01:16,996 (client:354) INFO: {'Role': 'Client #3', 'Round': 33, 'Results_raw': {'train_loss': 30.827251, 'val_loss': 32.849824, 'test_loss': 33.488872}}
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2024-11-14 20:02:27,477 (client:354) INFO: {'Role': 'Client #6', 'Round': 33, 'Results_raw': {'train_loss': 32.779091, 'val_loss': 31.126695, 'test_loss': 33.665001}}
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2024-11-14 20:03:35,804 (client:354) INFO: {'Role': 'Client #2', 'Round': 33, 'Results_raw': {'train_loss': 27.522291, 'val_loss': 27.634046, 'test_loss': 28.28231}}
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2024-11-14 20:04:45,180 (client:354) INFO: {'Role': 'Client #4', 'Round': 33, 'Results_raw': {'train_loss': 35.234176, 'val_loss': 35.218589, 'test_loss': 34.791325}}
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2024-11-14 20:04:45,184 (server:615) INFO: {'Role': 'Server #', 'Round': 32, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.519828), 'test_loss': np.float64(228207.670752), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.071112), 'val_loss': np.float64(225680.503674), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.519828), 'test_loss': np.float64(228207.670752), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.071112), 'val_loss': np.float64(225680.503674), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.899527), 'test_avg_loss_bottom_decile': np.float64(39.101021), 'test_avg_loss_top_decile': np.float64(43.073939), 'test_avg_loss_min': np.float64(35.994852), 'test_avg_loss_max': np.float64(43.073939), 'test_avg_loss_bottom10%': np.float64(35.994852), 'test_avg_loss_top10%': np.float64(43.073939), 'test_avg_loss_cos1': np.float64(0.998903), 'test_avg_loss_entropy': np.float64(2.301467), 'test_loss_std': np.float64(10698.134129), 'test_loss_bottom_decile': np.float64(220216.949219), 'test_loss_top_decile': np.float64(242592.424438), 'test_loss_min': np.float64(202723.005249), 'test_loss_max': np.float64(242592.424438), 'test_loss_bottom10%': np.float64(202723.005249), 'test_loss_top10%': np.float64(242592.424438), 'test_loss_cos1': np.float64(0.998903), 'test_loss_entropy': np.float64(2.301467), 'val_avg_loss_std': np.float64(2.234562), 'val_avg_loss_bottom_decile': np.float64(38.906288), 'val_avg_loss_top_decile': np.float64(43.467829), 'val_avg_loss_min': np.float64(35.291953), 'val_avg_loss_max': np.float64(43.467829), 'val_avg_loss_bottom10%': np.float64(35.291953), 'val_avg_loss_top10%': np.float64(43.467829), 'val_avg_loss_cos1': np.float64(0.998449), 'val_avg_loss_entropy': np.float64(2.301016), 'val_loss_std': np.float64(12585.051517), 'val_loss_bottom_decile': np.float64(219120.215088), 'val_loss_top_decile': np.float64(244810.811768), 'val_loss_min': np.float64(198764.278809), 'val_loss_max': np.float64(244810.811768), 'val_loss_bottom10%': np.float64(198764.278809), 'val_loss_top10%': np.float64(244810.811768), 'val_loss_cos1': np.float64(0.998449), 'val_loss_entropy': np.float64(2.301016)}}
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2024-11-14 20:04:45,229 (server:353) INFO: Server: Starting evaluation at the end of round 33.
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2024-11-14 20:04:45,230 (server:359) INFO: ----------- Starting a new training round (Round #34) -------------
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2024-11-14 20:08:29,140 (client:354) INFO: {'Role': 'Client #4', 'Round': 34, 'Results_raw': {'train_loss': 35.267096, 'val_loss': 35.225358, 'test_loss': 34.46034}}
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2024-11-14 20:09:41,055 (client:354) INFO: {'Role': 'Client #7', 'Round': 34, 'Results_raw': {'train_loss': 30.671165, 'val_loss': 30.127511, 'test_loss': 30.919558}}
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2024-11-14 20:10:51,383 (client:354) INFO: {'Role': 'Client #8', 'Round': 34, 'Results_raw': {'train_loss': 31.793372, 'val_loss': 31.74832, 'test_loss': 32.928176}}
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2024-11-14 20:11:59,765 (client:354) INFO: {'Role': 'Client #10', 'Round': 34, 'Results_raw': {'train_loss': 31.808904, 'val_loss': 31.933649, 'test_loss': 32.672074}}
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2024-11-14 20:13:09,102 (client:354) INFO: {'Role': 'Client #5', 'Round': 34, 'Results_raw': {'train_loss': 31.168807, 'val_loss': 31.785401, 'test_loss': 33.437556}}
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2024-11-14 20:14:18,869 (client:354) INFO: {'Role': 'Client #2', 'Round': 34, 'Results_raw': {'train_loss': 27.616113, 'val_loss': 27.258182, 'test_loss': 27.975148}}
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2024-11-14 20:15:29,678 (client:354) INFO: {'Role': 'Client #9', 'Round': 34, 'Results_raw': {'train_loss': 33.931761, 'val_loss': 33.51666, 'test_loss': 35.40752}}
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2024-11-14 20:16:38,802 (client:354) INFO: {'Role': 'Client #3', 'Round': 34, 'Results_raw': {'train_loss': 30.80417, 'val_loss': 32.949622, 'test_loss': 33.942805}}
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2024-11-14 20:17:48,634 (client:354) INFO: {'Role': 'Client #1', 'Round': 34, 'Results_raw': {'train_loss': 32.285664, 'val_loss': 32.596023, 'test_loss': 33.566882}}
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2024-11-14 20:18:59,037 (client:354) INFO: {'Role': 'Client #6', 'Round': 34, 'Results_raw': {'train_loss': 32.803457, 'val_loss': 31.305714, 'test_loss': 34.812156}}
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2024-11-14 20:18:59,040 (server:615) INFO: {'Role': 'Server #', 'Round': 33, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.560264), 'test_loss': np.float64(228435.406287), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.086284), 'val_loss': np.float64(225765.950342), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.560264), 'test_loss': np.float64(228435.406287), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(40.086284), 'val_loss': np.float64(225765.950342), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.906294), 'test_avg_loss_bottom_decile': np.float64(39.201276), 'test_avg_loss_top_decile': np.float64(43.198116), 'test_avg_loss_min': np.float64(36.004858), 'test_avg_loss_max': np.float64(43.198116), 'test_avg_loss_bottom10%': np.float64(36.004858), 'test_avg_loss_top10%': np.float64(43.198116), 'test_avg_loss_cos1': np.float64(0.998897), 'test_avg_loss_entropy': np.float64(2.301462), 'test_loss_std': np.float64(10736.248262), 'test_loss_bottom_decile': np.float64(220781.586914), 'test_loss_top_decile': np.float64(243291.787964), 'test_loss_min': np.float64(202779.360718), 'test_loss_max': np.float64(243291.787964), 'test_loss_bottom10%': np.float64(202779.360718), 'test_loss_top10%': np.float64(243291.787964), 'test_loss_cos1': np.float64(0.998897), 'test_loss_entropy': np.float64(2.301462), 'val_avg_loss_std': np.float64(2.228881), 'val_avg_loss_bottom_decile': np.float64(38.979538), 'val_avg_loss_top_decile': np.float64(43.49869), 'val_avg_loss_min': np.float64(35.292527), 'val_avg_loss_max': np.float64(43.49869), 'val_avg_loss_bottom10%': np.float64(35.292527), 'val_avg_loss_top10%': np.float64(43.49869), 'val_avg_loss_cos1': np.float64(0.998458), 'val_avg_loss_entropy': np.float64(2.301024), 'val_loss_std': np.float64(12553.056501), 'val_loss_bottom_decile': np.float64(219532.756592), 'val_loss_top_decile': np.float64(244984.621704), 'val_loss_min': np.float64(198767.509399), 'val_loss_max': np.float64(244984.621704), 'val_loss_bottom10%': np.float64(198767.509399), 'val_loss_top10%': np.float64(244984.621704), 'val_loss_cos1': np.float64(0.998458), 'val_loss_entropy': np.float64(2.301024)}}
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2024-11-14 20:18:59,079 (server:353) INFO: Server: Starting evaluation at the end of round 34.
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2024-11-14 20:18:59,079 (server:359) INFO: ----------- Starting a new training round (Round #35) -------------
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2024-11-14 20:22:31,896 (client:354) INFO: {'Role': 'Client #3', 'Round': 35, 'Results_raw': {'train_loss': 30.71997, 'val_loss': 32.978443, 'test_loss': 33.810743}}
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2024-11-14 20:23:46,313 (client:354) INFO: {'Role': 'Client #9', 'Round': 35, 'Results_raw': {'train_loss': 33.897913, 'val_loss': 33.846422, 'test_loss': 36.006342}}
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2024-11-14 20:25:01,854 (client:354) INFO: {'Role': 'Client #2', 'Round': 35, 'Results_raw': {'train_loss': 27.530302, 'val_loss': 27.209318, 'test_loss': 28.278764}}
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2024-11-14 20:26:16,317 (client:354) INFO: {'Role': 'Client #8', 'Round': 35, 'Results_raw': {'train_loss': 31.748858, 'val_loss': 31.551332, 'test_loss': 32.704713}}
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2024-11-14 20:27:31,517 (client:354) INFO: {'Role': 'Client #5', 'Round': 35, 'Results_raw': {'train_loss': 31.102186, 'val_loss': 31.637572, 'test_loss': 32.924698}}
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2024-11-14 20:28:44,579 (client:354) INFO: {'Role': 'Client #4', 'Round': 35, 'Results_raw': {'train_loss': 35.202456, 'val_loss': 35.339978, 'test_loss': 34.406263}}
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2024-11-14 20:29:55,695 (client:354) INFO: {'Role': 'Client #6', 'Round': 35, 'Results_raw': {'train_loss': 32.842876, 'val_loss': 31.080901, 'test_loss': 34.510129}}
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2024-11-14 20:31:06,191 (client:354) INFO: {'Role': 'Client #7', 'Round': 35, 'Results_raw': {'train_loss': 30.626136, 'val_loss': 30.17477, 'test_loss': 31.012606}}
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2024-11-14 20:32:16,494 (client:354) INFO: {'Role': 'Client #10', 'Round': 35, 'Results_raw': {'train_loss': 31.845643, 'val_loss': 31.933051, 'test_loss': 32.654539}}
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2024-11-14 20:33:28,172 (client:354) INFO: {'Role': 'Client #1', 'Round': 35, 'Results_raw': {'train_loss': 32.230954, 'val_loss': 32.629558, 'test_loss': 33.611673}}
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2024-11-14 20:33:28,180 (server:615) INFO: {'Role': 'Server #', 'Round': 34, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.422727), 'test_loss': np.float64(227660.796472), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.966895), 'val_loss': np.float64(225093.551099), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.422727), 'test_loss': np.float64(227660.796472), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.966895), 'val_loss': np.float64(225093.551099), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.883717), 'test_avg_loss_bottom_decile': np.float64(39.04978), 'test_avg_loss_top_decile': np.float64(43.020151), 'test_avg_loss_min': np.float64(35.945874), 'test_avg_loss_max': np.float64(43.020151), 'test_avg_loss_bottom10%': np.float64(35.945874), 'test_avg_loss_top10%': np.float64(43.020151), 'test_avg_loss_cos1': np.float64(0.998916), 'test_avg_loss_entropy': np.float64(2.301481), 'test_loss_std': np.float64(10609.096256), 'test_loss_bottom_decile': np.float64(219928.361816), 'test_loss_top_decile': np.float64(242289.491577), 'test_loss_min': np.float64(202447.160156), 'test_loss_max': np.float64(242289.491577), 'test_loss_bottom10%': np.float64(202447.160156), 'test_loss_top10%': np.float64(242289.491577), 'test_loss_cos1': np.float64(0.998916), 'test_loss_entropy': np.float64(2.301481), 'val_avg_loss_std': np.float64(2.222344), 'val_avg_loss_bottom_decile': np.float64(38.791384), 'val_avg_loss_top_decile': np.float64(43.450372), 'val_avg_loss_min': np.float64(35.256715), 'val_avg_loss_max': np.float64(43.450372), 'val_avg_loss_bottom10%': np.float64(35.256715), 'val_avg_loss_top10%': np.float64(43.450372), 'val_avg_loss_cos1': np.float64(0.998458), 'val_avg_loss_entropy': np.float64(2.301026), 'val_loss_std': np.float64(12516.239106), 'val_loss_bottom_decile': np.float64(218473.072998), 'val_loss_top_decile': np.float64(244712.492676), 'val_loss_min': np.float64(198565.821289), 'val_loss_max': np.float64(244712.492676), 'val_loss_bottom10%': np.float64(198565.821289), 'val_loss_top10%': np.float64(244712.492676), 'val_loss_cos1': np.float64(0.998458), 'val_loss_entropy': np.float64(2.301026)}}
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2024-11-14 20:33:28,226 (server:353) INFO: Server: Starting evaluation at the end of round 35.
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2024-11-14 20:33:28,227 (server:359) INFO: ----------- Starting a new training round (Round #36) -------------
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2024-11-14 20:37:10,268 (client:354) INFO: {'Role': 'Client #9', 'Round': 36, 'Results_raw': {'train_loss': 33.843905, 'val_loss': 33.667502, 'test_loss': 35.149335}}
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2024-11-14 20:38:22,565 (client:354) INFO: {'Role': 'Client #6', 'Round': 36, 'Results_raw': {'train_loss': 32.709682, 'val_loss': 31.112605, 'test_loss': 34.929288}}
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2024-11-14 20:39:41,292 (client:354) INFO: {'Role': 'Client #8', 'Round': 36, 'Results_raw': {'train_loss': 31.717604, 'val_loss': 31.631639, 'test_loss': 32.734884}}
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2024-11-14 20:40:57,541 (client:354) INFO: {'Role': 'Client #5', 'Round': 36, 'Results_raw': {'train_loss': 31.039959, 'val_loss': 31.632483, 'test_loss': 33.235997}}
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2024-11-14 20:42:10,294 (client:354) INFO: {'Role': 'Client #7', 'Round': 36, 'Results_raw': {'train_loss': 30.58303, 'val_loss': 30.3415, 'test_loss': 31.192594}}
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2024-11-14 20:43:27,848 (client:354) INFO: {'Role': 'Client #4', 'Round': 36, 'Results_raw': {'train_loss': 35.129475, 'val_loss': 35.376115, 'test_loss': 34.791674}}
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2024-11-14 20:44:47,905 (client:354) INFO: {'Role': 'Client #3', 'Round': 36, 'Results_raw': {'train_loss': 30.72208, 'val_loss': 33.135884, 'test_loss': 33.673976}}
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2024-11-14 20:46:03,133 (client:354) INFO: {'Role': 'Client #2', 'Round': 36, 'Results_raw': {'train_loss': 27.439447, 'val_loss': 27.250278, 'test_loss': 27.906064}}
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2024-11-14 20:47:16,089 (client:354) INFO: {'Role': 'Client #10', 'Round': 36, 'Results_raw': {'train_loss': 31.837205, 'val_loss': 31.817548, 'test_loss': 32.740018}}
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2024-11-14 20:48:30,692 (client:354) INFO: {'Role': 'Client #1', 'Round': 36, 'Results_raw': {'train_loss': 32.226461, 'val_loss': 32.723506, 'test_loss': 33.620117}}
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2024-11-14 20:48:30,699 (server:615) INFO: {'Role': 'Server #', 'Round': 35, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.230034), 'test_loss': np.float64(226575.551355), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.786613), 'val_loss': np.float64(224078.205005), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.230034), 'test_loss': np.float64(226575.551355), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.786613), 'val_loss': np.float64(224078.205005), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.855657), 'test_avg_loss_bottom_decile': np.float64(38.763696), 'test_avg_loss_top_decile': np.float64(42.804862), 'test_avg_loss_min': np.float64(35.883279), 'test_avg_loss_max': np.float64(42.804862), 'test_avg_loss_bottom10%': np.float64(35.883279), 'test_avg_loss_top10%': np.float64(42.804862), 'test_avg_loss_cos1': np.float64(0.998938), 'test_avg_loss_entropy': np.float64(2.301504), 'test_loss_std': np.float64(10451.05775), 'test_loss_bottom_decile': np.float64(218317.135986), 'test_loss_top_decile': np.float64(241076.981323), 'test_loss_min': np.float64(202094.624756), 'test_loss_max': np.float64(241076.981323), 'test_loss_bottom10%': np.float64(202094.624756), 'test_loss_top10%': np.float64(241076.981323), 'test_loss_cos1': np.float64(0.998938), 'test_loss_entropy': np.float64(2.301504), 'val_avg_loss_std': np.float64(2.19541), 'val_avg_loss_bottom_decile': np.float64(38.518331), 'val_avg_loss_top_decile': np.float64(43.180241), 'val_avg_loss_min': np.float64(35.196562), 'val_avg_loss_max': np.float64(43.180241), 'val_avg_loss_bottom10%': np.float64(35.196562), 'val_avg_loss_top10%': np.float64(43.180241), 'val_avg_loss_cos1': np.float64(0.998481), 'val_avg_loss_entropy': np.float64(2.301051), 'val_loss_std': np.float64(12364.55052), 'val_loss_bottom_decile': np.float64(216935.240479), 'val_loss_top_decile': np.float64(243191.115356), 'val_loss_min': np.float64(198227.039062), 'val_loss_max': np.float64(243191.115356), 'val_loss_bottom10%': np.float64(198227.039062), 'val_loss_top10%': np.float64(243191.115356), 'val_loss_cos1': np.float64(0.998481), 'val_loss_entropy': np.float64(2.301051)}}
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2024-11-14 20:48:30,745 (server:353) INFO: Server: Starting evaluation at the end of round 36.
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2024-11-14 20:48:30,747 (server:359) INFO: ----------- Starting a new training round (Round #37) -------------
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2024-11-14 20:52:40,333 (client:354) INFO: {'Role': 'Client #9', 'Round': 37, 'Results_raw': {'train_loss': 33.807655, 'val_loss': 33.768899, 'test_loss': 35.207228}}
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2024-11-14 20:53:57,182 (client:354) INFO: {'Role': 'Client #8', 'Round': 37, 'Results_raw': {'train_loss': 31.756128, 'val_loss': 31.716506, 'test_loss': 32.871288}}
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2024-11-14 20:55:08,302 (client:354) INFO: {'Role': 'Client #1', 'Round': 37, 'Results_raw': {'train_loss': 32.170215, 'val_loss': 32.830098, 'test_loss': 33.989503}}
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2024-11-14 20:56:20,385 (client:354) INFO: {'Role': 'Client #2', 'Round': 37, 'Results_raw': {'train_loss': 27.452431, 'val_loss': 27.301055, 'test_loss': 27.872223}}
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2024-11-14 20:57:31,028 (client:354) INFO: {'Role': 'Client #4', 'Round': 37, 'Results_raw': {'train_loss': 35.088875, 'val_loss': 35.355366, 'test_loss': 34.566049}}
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2024-11-14 20:58:42,231 (client:354) INFO: {'Role': 'Client #5', 'Round': 37, 'Results_raw': {'train_loss': 31.056312, 'val_loss': 31.501197, 'test_loss': 32.920742}}
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2024-11-14 20:59:53,251 (client:354) INFO: {'Role': 'Client #3', 'Round': 37, 'Results_raw': {'train_loss': 30.726965, 'val_loss': 33.170349, 'test_loss': 33.699149}}
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2024-11-14 21:01:10,521 (client:354) INFO: {'Role': 'Client #6', 'Round': 37, 'Results_raw': {'train_loss': 32.734627, 'val_loss': 31.052713, 'test_loss': 34.763686}}
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2024-11-14 21:02:29,120 (client:354) INFO: {'Role': 'Client #10', 'Round': 37, 'Results_raw': {'train_loss': 31.735169, 'val_loss': 31.806717, 'test_loss': 32.78428}}
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2024-11-14 21:03:44,899 (client:354) INFO: {'Role': 'Client #7', 'Round': 37, 'Results_raw': {'train_loss': 30.553699, 'val_loss': 30.295693, 'test_loss': 31.16219}}
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2024-11-14 21:03:44,905 (server:615) INFO: {'Role': 'Server #', 'Round': 36, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.274802), 'test_loss': np.float64(226827.683716), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.815487), 'val_loss': np.float64(224240.823157), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.274802), 'test_loss': np.float64(226827.683716), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.815487), 'val_loss': np.float64(224240.823157), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.886877), 'test_avg_loss_bottom_decile': np.float64(38.840957), 'test_avg_loss_top_decile': np.float64(42.818583), 'test_avg_loss_min': np.float64(35.8045), 'test_avg_loss_max': np.float64(42.818583), 'test_avg_loss_bottom10%': np.float64(35.8045), 'test_avg_loss_top10%': np.float64(42.818583), 'test_avg_loss_cos1': np.float64(0.998904), 'test_avg_loss_entropy': np.float64(2.301469), 'test_loss_std': np.float64(10626.893276), 'test_loss_bottom_decile': np.float64(218752.272339), 'test_loss_top_decile': np.float64(241154.258545), 'test_loss_min': np.float64(201650.942749), 'test_loss_max': np.float64(241154.258545), 'test_loss_bottom10%': np.float64(201650.942749), 'test_loss_top10%': np.float64(241154.258545), 'test_loss_cos1': np.float64(0.998904), 'test_loss_entropy': np.float64(2.301469), 'val_avg_loss_std': np.float64(2.203668), 'val_avg_loss_bottom_decile': np.float64(38.58076), 'val_avg_loss_top_decile': np.float64(43.279579), 'val_avg_loss_min': np.float64(35.135948), 'val_avg_loss_max': np.float64(43.279579), 'val_avg_loss_bottom10%': np.float64(35.135948), 'val_avg_loss_top10%': np.float64(43.279579), 'val_avg_loss_cos1': np.float64(0.998472), 'val_avg_loss_entropy': np.float64(2.30104), 'val_loss_std': np.float64(12411.058686), 'val_loss_bottom_decile': np.float64(217286.839478), 'val_loss_top_decile': np.float64(243750.586426), 'val_loss_min': np.float64(197885.658691), 'val_loss_max': np.float64(243750.586426), 'val_loss_bottom10%': np.float64(197885.658691), 'val_loss_top10%': np.float64(243750.586426), 'val_loss_cos1': np.float64(0.998472), 'val_loss_entropy': np.float64(2.30104)}}
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2024-11-14 21:03:44,946 (server:353) INFO: Server: Starting evaluation at the end of round 37.
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2024-11-14 21:03:44,948 (server:359) INFO: ----------- Starting a new training round (Round #38) -------------
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2024-11-14 21:07:24,323 (client:354) INFO: {'Role': 'Client #4', 'Round': 38, 'Results_raw': {'train_loss': 35.073976, 'val_loss': 35.101512, 'test_loss': 34.317938}}
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2024-11-14 21:08:34,177 (client:354) INFO: {'Role': 'Client #2', 'Round': 38, 'Results_raw': {'train_loss': 27.430437, 'val_loss': 27.241964, 'test_loss': 28.380803}}
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2024-11-14 21:09:44,037 (client:354) INFO: {'Role': 'Client #9', 'Round': 38, 'Results_raw': {'train_loss': 33.785025, 'val_loss': 33.761189, 'test_loss': 36.153895}}
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2024-11-14 21:10:53,930 (client:354) INFO: {'Role': 'Client #6', 'Round': 38, 'Results_raw': {'train_loss': 32.741448, 'val_loss': 30.938414, 'test_loss': 33.88106}}
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2024-11-14 21:12:03,994 (client:354) INFO: {'Role': 'Client #5', 'Round': 38, 'Results_raw': {'train_loss': 30.988101, 'val_loss': 31.43504, 'test_loss': 32.816758}}
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2024-11-14 21:13:12,778 (client:354) INFO: {'Role': 'Client #7', 'Round': 38, 'Results_raw': {'train_loss': 30.517335, 'val_loss': 30.191327, 'test_loss': 31.156778}}
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2024-11-14 21:14:22,640 (client:354) INFO: {'Role': 'Client #10', 'Round': 38, 'Results_raw': {'train_loss': 31.766471, 'val_loss': 32.064873, 'test_loss': 32.855994}}
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2024-11-14 21:15:32,673 (client:354) INFO: {'Role': 'Client #3', 'Round': 38, 'Results_raw': {'train_loss': 30.688393, 'val_loss': 33.144298, 'test_loss': 33.248788}}
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2024-11-14 21:16:42,146 (client:354) INFO: {'Role': 'Client #1', 'Round': 38, 'Results_raw': {'train_loss': 32.135661, 'val_loss': 32.672195, 'test_loss': 33.602074}}
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2024-11-14 21:17:52,185 (client:354) INFO: {'Role': 'Client #8', 'Round': 38, 'Results_raw': {'train_loss': 31.754121, 'val_loss': 31.464464, 'test_loss': 32.665299}}
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2024-11-14 21:17:52,187 (server:615) INFO: {'Role': 'Server #', 'Round': 37, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.17345), 'test_loss': np.float64(226256.869775), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.710262), 'val_loss': np.float64(223648.194128), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.17345), 'test_loss': np.float64(226256.869775), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.710262), 'val_loss': np.float64(223648.194128), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.904618), 'test_avg_loss_bottom_decile': np.float64(38.690984), 'test_avg_loss_top_decile': np.float64(42.788246), 'test_avg_loss_min': np.float64(35.676304), 'test_avg_loss_max': np.float64(42.788246), 'test_avg_loss_bottom10%': np.float64(35.676304), 'test_avg_loss_top10%': np.float64(42.788246), 'test_avg_loss_cos1': np.float64(0.998878), 'test_avg_loss_entropy': np.float64(2.301442), 'test_loss_std': np.float64(10726.810949), 'test_loss_bottom_decile': np.float64(217907.624023), 'test_loss_top_decile': np.float64(240983.401611), 'test_loss_min': np.float64(200928.946777), 'test_loss_max': np.float64(240983.401611), 'test_loss_bottom10%': np.float64(200928.946777), 'test_loss_top10%': np.float64(240983.401611), 'test_loss_cos1': np.float64(0.998878), 'test_loss_entropy': np.float64(2.301442), 'val_avg_loss_std': np.float64(2.218934), 'val_avg_loss_bottom_decile': np.float64(38.444871), 'val_avg_loss_top_decile': np.float64(43.193106), 'val_avg_loss_min': np.float64(34.992418), 'val_avg_loss_max': np.float64(43.193106), 'val_avg_loss_bottom10%': np.float64(34.992418), 'val_avg_loss_top10%': np.float64(43.193106), 'val_avg_loss_cos1': np.float64(0.998442), 'val_avg_loss_entropy': np.float64(2.30101), 'val_loss_std': np.float64(12497.033495), 'val_loss_bottom_decile': np.float64(216521.515015), 'val_loss_top_decile': np.float64(243263.575562), 'val_loss_min': np.float64(197077.29541), 'val_loss_max': np.float64(243263.575562), 'val_loss_bottom10%': np.float64(197077.29541), 'val_loss_top10%': np.float64(243263.575562), 'val_loss_cos1': np.float64(0.998442), 'val_loss_entropy': np.float64(2.30101)}}
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2024-11-14 21:17:52,216 (server:353) INFO: Server: Starting evaluation at the end of round 38.
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2024-11-14 21:17:52,217 (server:359) INFO: ----------- Starting a new training round (Round #39) -------------
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2024-11-14 21:21:29,630 (client:354) INFO: {'Role': 'Client #5', 'Round': 39, 'Results_raw': {'train_loss': 30.996713, 'val_loss': 31.587831, 'test_loss': 33.012084}}
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2024-11-14 21:22:39,976 (client:354) INFO: {'Role': 'Client #10', 'Round': 39, 'Results_raw': {'train_loss': 31.749791, 'val_loss': 31.689739, 'test_loss': 32.424832}}
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2024-11-14 21:23:50,565 (client:354) INFO: {'Role': 'Client #7', 'Round': 39, 'Results_raw': {'train_loss': 30.505538, 'val_loss': 30.20659, 'test_loss': 31.149678}}
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2024-11-14 21:25:01,241 (client:354) INFO: {'Role': 'Client #3', 'Round': 39, 'Results_raw': {'train_loss': 30.667582, 'val_loss': 33.264529, 'test_loss': 34.12445}}
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2024-11-14 21:26:10,892 (client:354) INFO: {'Role': 'Client #8', 'Round': 39, 'Results_raw': {'train_loss': 31.655279, 'val_loss': 31.678475, 'test_loss': 32.863294}}
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2024-11-14 21:27:21,418 (client:354) INFO: {'Role': 'Client #4', 'Round': 39, 'Results_raw': {'train_loss': 35.051365, 'val_loss': 35.289552, 'test_loss': 34.6915}}
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2024-11-14 21:28:32,309 (client:354) INFO: {'Role': 'Client #2', 'Round': 39, 'Results_raw': {'train_loss': 27.462767, 'val_loss': 27.174988, 'test_loss': 27.960872}}
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2024-11-14 21:29:43,110 (client:354) INFO: {'Role': 'Client #6', 'Round': 39, 'Results_raw': {'train_loss': 32.660941, 'val_loss': 31.054448, 'test_loss': 33.743526}}
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2024-11-14 21:30:55,265 (client:354) INFO: {'Role': 'Client #1', 'Round': 39, 'Results_raw': {'train_loss': 32.079437, 'val_loss': 32.755727, 'test_loss': 33.538031}}
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2024-11-14 21:32:06,230 (client:354) INFO: {'Role': 'Client #9', 'Round': 39, 'Results_raw': {'train_loss': 33.756616, 'val_loss': 33.54943, 'test_loss': 35.388632}}
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2024-11-14 21:32:06,234 (server:615) INFO: {'Role': 'Server #', 'Round': 38, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.137864), 'test_loss': np.float64(226056.448816), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.677468), 'val_loss': np.float64(223463.500415), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.137864), 'test_loss': np.float64(226056.448816), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.677468), 'val_loss': np.float64(223463.500415), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.874032), 'test_avg_loss_bottom_decile': np.float64(38.656328), 'test_avg_loss_top_decile': np.float64(42.737713), 'test_avg_loss_min': np.float64(35.735147), 'test_avg_loss_max': np.float64(42.737713), 'test_avg_loss_bottom10%': np.float64(35.735147), 'test_avg_loss_top10%': np.float64(42.737713), 'test_avg_loss_cos1': np.float64(0.998912), 'test_avg_loss_entropy': np.float64(2.301478), 'test_loss_std': np.float64(10554.549125), 'test_loss_bottom_decile': np.float64(217712.439087), 'test_loss_top_decile': np.float64(240698.802368), 'test_loss_min': np.float64(201260.350098), 'test_loss_max': np.float64(240698.802368), 'test_loss_bottom10%': np.float64(201260.350098), 'test_loss_top10%': np.float64(240698.802368), 'test_loss_cos1': np.float64(0.998912), 'test_loss_entropy': np.float64(2.301478), 'val_avg_loss_std': np.float64(2.178408), 'val_avg_loss_bottom_decile': np.float64(38.404337), 'val_avg_loss_top_decile': np.float64(43.126618), 'val_avg_loss_min': np.float64(35.056827), 'val_avg_loss_max': np.float64(43.126618), 'val_avg_loss_bottom10%': np.float64(35.056827), 'val_avg_loss_top10%': np.float64(43.126618), 'val_avg_loss_cos1': np.float64(0.998496), 'val_avg_loss_entropy': np.float64(2.301065), 'val_loss_std': np.float64(12268.792067), 'val_loss_bottom_decile': np.float64(216293.225098), 'val_loss_top_decile': np.float64(242889.114014), 'val_loss_min': np.float64(197440.051025), 'val_loss_max': np.float64(242889.114014), 'val_loss_bottom10%': np.float64(197440.051025), 'val_loss_top10%': np.float64(242889.114014), 'val_loss_cos1': np.float64(0.998496), 'val_loss_entropy': np.float64(2.301065)}}
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2024-11-14 21:32:06,283 (server:353) INFO: Server: Starting evaluation at the end of round 39.
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2024-11-14 21:32:06,284 (server:359) INFO: ----------- Starting a new training round (Round #40) -------------
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2024-11-14 21:35:42,346 (client:354) INFO: {'Role': 'Client #3', 'Round': 40, 'Results_raw': {'train_loss': 30.623578, 'val_loss': 32.941623, 'test_loss': 33.46353}}
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2024-11-14 21:36:52,789 (client:354) INFO: {'Role': 'Client #5', 'Round': 40, 'Results_raw': {'train_loss': 30.964246, 'val_loss': 31.53742, 'test_loss': 32.886711}}
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2024-11-14 21:38:02,240 (client:354) INFO: {'Role': 'Client #4', 'Round': 40, 'Results_raw': {'train_loss': 34.966313, 'val_loss': 35.24841, 'test_loss': 34.582226}}
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2024-11-14 21:39:12,381 (client:354) INFO: {'Role': 'Client #6', 'Round': 40, 'Results_raw': {'train_loss': 32.750932, 'val_loss': 31.01947, 'test_loss': 35.555333}}
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2024-11-14 21:40:24,252 (client:354) INFO: {'Role': 'Client #7', 'Round': 40, 'Results_raw': {'train_loss': 30.477703, 'val_loss': 30.224865, 'test_loss': 31.070177}}
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2024-11-14 21:41:41,954 (client:354) INFO: {'Role': 'Client #2', 'Round': 40, 'Results_raw': {'train_loss': 27.34524, 'val_loss': 27.351225, 'test_loss': 28.235423}}
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2024-11-14 21:43:03,118 (client:354) INFO: {'Role': 'Client #9', 'Round': 40, 'Results_raw': {'train_loss': 33.766094, 'val_loss': 33.68861, 'test_loss': 35.16443}}
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2024-11-14 21:44:24,580 (client:354) INFO: {'Role': 'Client #1', 'Round': 40, 'Results_raw': {'train_loss': 32.017025, 'val_loss': 32.729346, 'test_loss': 33.59158}}
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2024-11-14 21:45:35,988 (client:354) INFO: {'Role': 'Client #8', 'Round': 40, 'Results_raw': {'train_loss': 31.64561, 'val_loss': 31.654266, 'test_loss': 32.970358}}
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2024-11-14 21:46:43,164 (client:354) INFO: {'Role': 'Client #10', 'Round': 40, 'Results_raw': {'train_loss': 31.729173, 'val_loss': 31.941812, 'test_loss': 32.643447}}
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2024-11-14 21:46:43,168 (server:615) INFO: {'Role': 'Server #', 'Round': 39, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.089745), 'test_loss': np.float64(225785.443481), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.629231), 'val_loss': np.float64(223191.827747), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.089745), 'test_loss': np.float64(225785.443481), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.629231), 'val_loss': np.float64(223191.827747), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.904135), 'test_avg_loss_bottom_decile': np.float64(38.612594), 'test_avg_loss_top_decile': np.float64(42.666507), 'test_avg_loss_min': np.float64(35.578359), 'test_avg_loss_max': np.float64(42.666507), 'test_avg_loss_bottom10%': np.float64(35.578359), 'test_avg_loss_top10%': np.float64(42.666507), 'test_avg_loss_cos1': np.float64(0.998874), 'test_avg_loss_entropy': np.float64(2.301438), 'test_loss_std': np.float64(10724.086162), 'test_loss_bottom_decile': np.float64(217466.128418), 'test_loss_top_decile': np.float64(240297.768921), 'test_loss_min': np.float64(200377.319336), 'test_loss_max': np.float64(240297.768921), 'test_loss_bottom10%': np.float64(200377.319336), 'test_loss_top10%': np.float64(240297.768921), 'test_loss_cos1': np.float64(0.998874), 'test_loss_entropy': np.float64(2.301438), 'val_avg_loss_std': np.float64(2.229495), 'val_avg_loss_bottom_decile': np.float64(38.380451), 'val_avg_loss_top_decile': np.float64(43.091956), 'val_avg_loss_min': np.float64(34.903011), 'val_avg_loss_max': np.float64(43.091956), 'val_avg_loss_bottom10%': np.float64(34.903011), 'val_avg_loss_top10%': np.float64(43.091956), 'val_avg_loss_cos1': np.float64(0.998421), 'val_avg_loss_entropy': np.float64(2.300989), 'val_loss_std': np.float64(12556.514005), 'val_loss_bottom_decile': np.float64(216158.700806), 'val_loss_top_decile': np.float64(242693.895874), 'val_loss_min': np.float64(196573.760376), 'val_loss_max': np.float64(242693.895874), 'val_loss_bottom10%': np.float64(196573.760376), 'val_loss_top10%': np.float64(242693.895874), 'val_loss_cos1': np.float64(0.998421), 'val_loss_entropy': np.float64(2.300989)}}
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2024-11-14 21:46:43,214 (server:353) INFO: Server: Starting evaluation at the end of round 40.
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2024-11-14 21:46:43,215 (server:359) INFO: ----------- Starting a new training round (Round #41) -------------
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2024-11-14 21:50:11,066 (client:354) INFO: {'Role': 'Client #6', 'Round': 41, 'Results_raw': {'train_loss': 32.631101, 'val_loss': 31.0035, 'test_loss': 34.188557}}
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2024-11-14 21:51:20,712 (client:354) INFO: {'Role': 'Client #4', 'Round': 41, 'Results_raw': {'train_loss': 34.945316, 'val_loss': 35.216273, 'test_loss': 34.810205}}
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2024-11-14 21:52:30,079 (client:354) INFO: {'Role': 'Client #9', 'Round': 41, 'Results_raw': {'train_loss': 33.70736, 'val_loss': 33.589007, 'test_loss': 34.998051}}
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2024-11-14 21:53:41,935 (client:354) INFO: {'Role': 'Client #8', 'Round': 41, 'Results_raw': {'train_loss': 31.618275, 'val_loss': 31.573057, 'test_loss': 32.837886}}
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2024-11-14 21:54:50,057 (client:354) INFO: {'Role': 'Client #7', 'Round': 41, 'Results_raw': {'train_loss': 30.492707, 'val_loss': 30.255854, 'test_loss': 31.206895}}
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2024-11-14 21:55:59,941 (client:354) INFO: {'Role': 'Client #1', 'Round': 41, 'Results_raw': {'train_loss': 31.997423, 'val_loss': 32.776782, 'test_loss': 33.714272}}
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2024-11-14 21:57:05,668 (client:354) INFO: {'Role': 'Client #10', 'Round': 41, 'Results_raw': {'train_loss': 31.635683, 'val_loss': 31.898389, 'test_loss': 32.926712}}
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2024-11-14 21:58:08,229 (client:354) INFO: {'Role': 'Client #3', 'Round': 41, 'Results_raw': {'train_loss': 30.612075, 'val_loss': 33.018558, 'test_loss': 33.758608}}
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2024-11-14 21:59:10,905 (client:354) INFO: {'Role': 'Client #5', 'Round': 41, 'Results_raw': {'train_loss': 30.95961, 'val_loss': 31.507803, 'test_loss': 32.933167}}
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2024-11-14 22:00:13,341 (client:354) INFO: {'Role': 'Client #2', 'Round': 41, 'Results_raw': {'train_loss': 27.321794, 'val_loss': 27.236414, 'test_loss': 28.105911}}
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2024-11-14 22:00:13,344 (server:615) INFO: {'Role': 'Server #', 'Round': 40, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.120162), 'test_loss': np.float64(225956.754797), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.647094), 'val_loss': np.float64(223292.435645), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.120162), 'test_loss': np.float64(225956.754797), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.647094), 'val_loss': np.float64(223292.435645), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.912778), 'test_avg_loss_bottom_decile': np.float64(38.637526), 'test_avg_loss_top_decile': np.float64(42.796008), 'test_avg_loss_min': np.float64(35.609143), 'test_avg_loss_max': np.float64(42.796008), 'test_avg_loss_bottom10%': np.float64(35.609143), 'test_avg_loss_top10%': np.float64(42.796008), 'test_avg_loss_cos1': np.float64(0.998865), 'test_avg_loss_entropy': np.float64(2.30143), 'test_loss_std': np.float64(10772.764096), 'test_loss_bottom_decile': np.float64(217606.544189), 'test_loss_top_decile': np.float64(241027.11792), 'test_loss_min': np.float64(200550.691406), 'test_loss_max': np.float64(241027.11792), 'test_loss_bottom10%': np.float64(200550.691406), 'test_loss_top10%': np.float64(241027.11792), 'test_loss_cos1': np.float64(0.998865), 'test_loss_entropy': np.float64(2.30143), 'val_avg_loss_std': np.float64(2.19242), 'val_avg_loss_bottom_decile': np.float64(38.416002), 'val_avg_loss_top_decile': np.float64(43.071132), 'val_avg_loss_min': np.float64(34.912271), 'val_avg_loss_max': np.float64(43.071132), 'val_avg_loss_bottom10%': np.float64(34.912271), 'val_avg_loss_top10%': np.float64(43.071132), 'val_avg_loss_cos1': np.float64(0.998475), 'val_avg_loss_entropy': np.float64(2.301041), 'val_loss_std': np.float64(12347.709895), 'val_loss_bottom_decile': np.float64(216358.920532), 'val_loss_top_decile': np.float64(242576.615601), 'val_loss_min': np.float64(196625.909302), 'val_loss_max': np.float64(242576.615601), 'val_loss_bottom10%': np.float64(196625.909302), 'val_loss_top10%': np.float64(242576.615601), 'val_loss_cos1': np.float64(0.998475), 'val_loss_entropy': np.float64(2.301041)}}
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2024-11-14 22:00:13,378 (server:353) INFO: Server: Starting evaluation at the end of round 41.
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2024-11-14 22:00:13,378 (server:359) INFO: ----------- Starting a new training round (Round #42) -------------
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2024-11-14 22:03:23,950 (client:354) INFO: {'Role': 'Client #3', 'Round': 42, 'Results_raw': {'train_loss': 30.568222, 'val_loss': 32.624131, 'test_loss': 33.351398}}
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2024-11-14 22:04:28,894 (client:354) INFO: {'Role': 'Client #6', 'Round': 42, 'Results_raw': {'train_loss': 32.664892, 'val_loss': 31.054164, 'test_loss': 35.359636}}
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2024-11-14 22:05:33,291 (client:354) INFO: {'Role': 'Client #10', 'Round': 42, 'Results_raw': {'train_loss': 31.671179, 'val_loss': 32.13344, 'test_loss': 32.890606}}
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2024-11-14 22:06:36,245 (client:354) INFO: {'Role': 'Client #8', 'Round': 42, 'Results_raw': {'train_loss': 31.597082, 'val_loss': 31.718942, 'test_loss': 32.675811}}
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2024-11-14 22:07:39,440 (client:354) INFO: {'Role': 'Client #7', 'Round': 42, 'Results_raw': {'train_loss': 30.383315, 'val_loss': 30.247128, 'test_loss': 30.963143}}
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2024-11-14 22:08:42,883 (client:354) INFO: {'Role': 'Client #4', 'Round': 42, 'Results_raw': {'train_loss': 34.865583, 'val_loss': 35.301746, 'test_loss': 34.553416}}
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2024-11-14 22:09:46,630 (client:354) INFO: {'Role': 'Client #5', 'Round': 42, 'Results_raw': {'train_loss': 30.91967, 'val_loss': 31.908771, 'test_loss': 33.747976}}
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2024-11-14 22:10:48,585 (client:354) INFO: {'Role': 'Client #2', 'Round': 42, 'Results_raw': {'train_loss': 27.372555, 'val_loss': 27.18741, 'test_loss': 28.054788}}
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2024-11-14 22:11:55,794 (client:354) INFO: {'Role': 'Client #9', 'Round': 42, 'Results_raw': {'train_loss': 33.655758, 'val_loss': 33.65863, 'test_loss': 35.412342}}
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2024-11-14 22:12:59,665 (client:354) INFO: {'Role': 'Client #1', 'Round': 42, 'Results_raw': {'train_loss': 31.989965, 'val_loss': 32.658264, 'test_loss': 33.518853}}
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2024-11-14 22:12:59,667 (server:615) INFO: {'Role': 'Server #', 'Round': 41, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.007125), 'test_loss': np.float64(225320.127905), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.565378), 'val_loss': np.float64(222832.210706), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.007125), 'test_loss': np.float64(225320.127905), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.565378), 'val_loss': np.float64(222832.210706), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.877564), 'test_avg_loss_bottom_decile': np.float64(38.525856), 'test_avg_loss_top_decile': np.float64(42.533698), 'test_avg_loss_min': np.float64(35.546318), 'test_avg_loss_max': np.float64(42.533698), 'test_avg_loss_bottom10%': np.float64(35.546318), 'test_avg_loss_top10%': np.float64(42.533698), 'test_avg_loss_cos1': np.float64(0.998901), 'test_avg_loss_entropy': np.float64(2.301465), 'test_loss_std': np.float64(10574.43885), 'test_loss_bottom_decile': np.float64(216977.622681), 'test_loss_top_decile': np.float64(239549.785156), 'test_loss_min': np.float64(200196.864746), 'test_loss_max': np.float64(239549.785156), 'test_loss_bottom10%': np.float64(200196.864746), 'test_loss_top10%': np.float64(239549.785156), 'test_loss_cos1': np.float64(0.998901), 'test_loss_entropy': np.float64(2.301465), 'val_avg_loss_std': np.float64(2.207337), 'val_avg_loss_bottom_decile': np.float64(38.283952), 'val_avg_loss_top_decile': np.float64(42.920964), 'val_avg_loss_min': np.float64(34.885242), 'val_avg_loss_max': np.float64(42.920964), 'val_avg_loss_bottom10%': np.float64(34.885242), 'val_avg_loss_top10%': np.float64(42.920964), 'val_avg_loss_cos1': np.float64(0.998447), 'val_avg_loss_entropy': np.float64(2.301015), 'val_loss_std': np.float64(12431.720473), 'val_loss_bottom_decile': np.float64(215615.216431), 'val_loss_top_decile': np.float64(241730.871948), 'val_loss_min': np.float64(196473.683716), 'val_loss_max': np.float64(241730.871948), 'val_loss_bottom10%': np.float64(196473.683716), 'val_loss_top10%': np.float64(241730.871948), 'val_loss_cos1': np.float64(0.998447), 'val_loss_entropy': np.float64(2.301015)}}
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2024-11-14 22:12:59,702 (server:353) INFO: Server: Starting evaluation at the end of round 42.
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2024-11-14 22:12:59,703 (server:359) INFO: ----------- Starting a new training round (Round #43) -------------
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2024-11-14 22:16:13,629 (client:354) INFO: {'Role': 'Client #9', 'Round': 43, 'Results_raw': {'train_loss': 33.672106, 'val_loss': 33.908342, 'test_loss': 35.706449}}
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2024-11-14 22:17:18,029 (client:354) INFO: {'Role': 'Client #3', 'Round': 43, 'Results_raw': {'train_loss': 30.538458, 'val_loss': 33.200306, 'test_loss': 34.000483}}
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2024-11-14 22:18:19,747 (client:354) INFO: {'Role': 'Client #2', 'Round': 43, 'Results_raw': {'train_loss': 27.340402, 'val_loss': 27.278678, 'test_loss': 28.180091}}
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2024-11-14 22:19:20,947 (client:354) INFO: {'Role': 'Client #1', 'Round': 43, 'Results_raw': {'train_loss': 31.985783, 'val_loss': 32.451955, 'test_loss': 33.103779}}
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2024-11-14 22:20:24,019 (client:354) INFO: {'Role': 'Client #10', 'Round': 43, 'Results_raw': {'train_loss': 31.676742, 'val_loss': 32.264718, 'test_loss': 32.638744}}
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2024-11-14 22:21:35,631 (client:354) INFO: {'Role': 'Client #6', 'Round': 43, 'Results_raw': {'train_loss': 32.645378, 'val_loss': 31.080166, 'test_loss': 35.656296}}
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2024-11-14 22:22:46,873 (client:354) INFO: {'Role': 'Client #8', 'Round': 43, 'Results_raw': {'train_loss': 31.552563, 'val_loss': 31.600434, 'test_loss': 32.763796}}
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2024-11-14 22:23:57,795 (client:354) INFO: {'Role': 'Client #7', 'Round': 43, 'Results_raw': {'train_loss': 30.410959, 'val_loss': 30.160459, 'test_loss': 31.055783}}
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2024-11-14 22:25:08,638 (client:354) INFO: {'Role': 'Client #4', 'Round': 43, 'Results_raw': {'train_loss': 34.911883, 'val_loss': 35.024174, 'test_loss': 34.361722}}
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2024-11-14 22:26:12,329 (client:354) INFO: {'Role': 'Client #5', 'Round': 43, 'Results_raw': {'train_loss': 30.879626, 'val_loss': 31.948512, 'test_loss': 33.297914}}
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2024-11-14 22:26:12,332 (server:615) INFO: {'Role': 'Server #', 'Round': 42, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.009769), 'test_loss': np.float64(225335.019995), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.5543), 'val_loss': np.float64(222769.816406), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.009769), 'test_loss': np.float64(225335.019995), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.5543), 'val_loss': np.float64(222769.816406), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.876777), 'test_avg_loss_bottom_decile': np.float64(38.585149), 'test_avg_loss_top_decile': np.float64(42.532768), 'test_avg_loss_min': np.float64(35.534454), 'test_avg_loss_max': np.float64(42.532768), 'test_avg_loss_bottom10%': np.float64(35.534454), 'test_avg_loss_top10%': np.float64(42.532768), 'test_avg_loss_cos1': np.float64(0.998902), 'test_avg_loss_entropy': np.float64(2.301466), 'test_loss_std': np.float64(10570.009796), 'test_loss_bottom_decile': np.float64(217311.559326), 'test_loss_top_decile': np.float64(239544.550903), 'test_loss_min': np.float64(200130.042725), 'test_loss_max': np.float64(239544.550903), 'test_loss_bottom10%': np.float64(200130.042725), 'test_loss_top10%': np.float64(239544.550903), 'test_loss_cos1': np.float64(0.998902), 'test_loss_entropy': np.float64(2.301466), 'val_avg_loss_std': np.float64(2.183618), 'val_avg_loss_bottom_decile': np.float64(38.361735), 'val_avg_loss_top_decile': np.float64(43.01158), 'val_avg_loss_min': np.float64(34.871311), 'val_avg_loss_max': np.float64(43.01158), 'val_avg_loss_bottom10%': np.float64(34.871311), 'val_avg_loss_top10%': np.float64(43.01158), 'val_avg_loss_cos1': np.float64(0.99848), 'val_avg_loss_entropy': np.float64(2.301047), 'val_loss_std': np.float64(12298.136738), 'val_loss_bottom_decile': np.float64(216053.292603), 'val_loss_top_decile': np.float64(242241.21875), 'val_loss_min': np.float64(196395.224976), 'val_loss_max': np.float64(242241.21875), 'val_loss_bottom10%': np.float64(196395.224976), 'val_loss_top10%': np.float64(242241.21875), 'val_loss_cos1': np.float64(0.99848), 'val_loss_entropy': np.float64(2.301047)}}
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2024-11-14 22:26:12,365 (server:353) INFO: Server: Starting evaluation at the end of round 43.
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2024-11-14 22:26:12,366 (server:359) INFO: ----------- Starting a new training round (Round #44) -------------
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2024-11-14 22:29:12,284 (client:354) INFO: {'Role': 'Client #1', 'Round': 44, 'Results_raw': {'train_loss': 31.978876, 'val_loss': 32.57365, 'test_loss': 33.730325}}
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2024-11-14 22:30:12,924 (client:354) INFO: {'Role': 'Client #8', 'Round': 44, 'Results_raw': {'train_loss': 31.532052, 'val_loss': 31.645727, 'test_loss': 32.771856}}
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2024-11-14 22:31:13,860 (client:354) INFO: {'Role': 'Client #4', 'Round': 44, 'Results_raw': {'train_loss': 34.888218, 'val_loss': 35.320275, 'test_loss': 34.714321}}
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2024-11-14 22:32:15,014 (client:354) INFO: {'Role': 'Client #7', 'Round': 44, 'Results_raw': {'train_loss': 30.362318, 'val_loss': 30.25685, 'test_loss': 31.005975}}
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2024-11-14 22:33:16,337 (client:354) INFO: {'Role': 'Client #6', 'Round': 44, 'Results_raw': {'train_loss': 32.594421, 'val_loss': 31.186724, 'test_loss': 36.425662}}
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2024-11-14 22:34:17,710 (client:354) INFO: {'Role': 'Client #5', 'Round': 44, 'Results_raw': {'train_loss': 30.841451, 'val_loss': 31.475854, 'test_loss': 32.87326}}
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2024-11-14 22:35:19,895 (client:354) INFO: {'Role': 'Client #10', 'Round': 44, 'Results_raw': {'train_loss': 31.614462, 'val_loss': 31.632907, 'test_loss': 32.525395}}
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2024-11-14 22:36:22,649 (client:354) INFO: {'Role': 'Client #9', 'Round': 44, 'Results_raw': {'train_loss': 33.638275, 'val_loss': 33.61598, 'test_loss': 35.461878}}
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2024-11-14 22:37:26,065 (client:354) INFO: {'Role': 'Client #2', 'Round': 44, 'Results_raw': {'train_loss': 27.376742, 'val_loss': 27.181612, 'test_loss': 28.122304}}
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2024-11-14 22:38:29,551 (client:354) INFO: {'Role': 'Client #3', 'Round': 44, 'Results_raw': {'train_loss': 30.520706, 'val_loss': 32.734782, 'test_loss': 33.255002}}
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2024-11-14 22:38:29,556 (server:615) INFO: {'Role': 'Server #', 'Round': 43, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.963275), 'test_loss': np.float64(225073.162817), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.510505), 'val_loss': np.float64(222523.165833), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.963275), 'test_loss': np.float64(225073.162817), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.510505), 'val_loss': np.float64(222523.165833), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.896162), 'test_avg_loss_bottom_decile': np.float64(38.467797), 'test_avg_loss_top_decile': np.float64(42.542647), 'test_avg_loss_min': np.float64(35.475643), 'test_avg_loss_max': np.float64(42.542647), 'test_avg_loss_bottom10%': np.float64(35.475643), 'test_avg_loss_top10%': np.float64(42.542647), 'test_avg_loss_cos1': np.float64(0.998876), 'test_avg_loss_entropy': np.float64(2.30144), 'test_loss_std': np.float64(10679.182543), 'test_loss_bottom_decile': np.float64(216650.634033), 'test_loss_top_decile': np.float64(239600.190308), 'test_loss_min': np.float64(199798.819214), 'test_loss_max': np.float64(239600.190308), 'test_loss_bottom10%': np.float64(199798.819214), 'test_loss_top10%': np.float64(239600.190308), 'test_loss_cos1': np.float64(0.998876), 'test_loss_entropy': np.float64(2.30144), 'val_avg_loss_std': np.float64(2.218252), 'val_avg_loss_bottom_decile': np.float64(38.217343), 'val_avg_loss_top_decile': np.float64(42.95652), 'val_avg_loss_min': np.float64(34.799187), 'val_avg_loss_max': np.float64(42.95652), 'val_avg_loss_bottom10%': np.float64(34.799187), 'val_avg_loss_top10%': np.float64(42.95652), 'val_avg_loss_cos1': np.float64(0.998428), 'val_avg_loss_entropy': np.float64(2.300995), 'val_loss_std': np.float64(12493.196881), 'val_loss_bottom_decile': np.float64(215240.07312), 'val_loss_top_decile': np.float64(241931.121948), 'val_loss_min': np.float64(195989.019165), 'val_loss_max': np.float64(241931.121948), 'val_loss_bottom10%': np.float64(195989.019165), 'val_loss_top10%': np.float64(241931.121948), 'val_loss_cos1': np.float64(0.998428), 'val_loss_entropy': np.float64(2.300995)}}
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2024-11-14 22:38:29,602 (server:353) INFO: Server: Starting evaluation at the end of round 44.
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2024-11-14 22:38:29,602 (server:359) INFO: ----------- Starting a new training round (Round #45) -------------
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2024-11-14 22:41:34,876 (client:354) INFO: {'Role': 'Client #4', 'Round': 45, 'Results_raw': {'train_loss': 35.013056, 'val_loss': 34.995564, 'test_loss': 34.395932}}
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2024-11-14 22:42:39,060 (client:354) INFO: {'Role': 'Client #7', 'Round': 45, 'Results_raw': {'train_loss': 30.35977, 'val_loss': 30.042373, 'test_loss': 31.031625}}
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2024-11-14 22:43:44,406 (client:354) INFO: {'Role': 'Client #5', 'Round': 45, 'Results_raw': {'train_loss': 30.791773, 'val_loss': 31.732423, 'test_loss': 33.070164}}
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2024-11-14 22:44:47,510 (client:354) INFO: {'Role': 'Client #10', 'Round': 45, 'Results_raw': {'train_loss': 31.56037, 'val_loss': 31.807966, 'test_loss': 32.582335}}
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2024-11-14 22:45:56,907 (client:354) INFO: {'Role': 'Client #9', 'Round': 45, 'Results_raw': {'train_loss': 33.573382, 'val_loss': 33.838516, 'test_loss': 35.348037}}
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2024-11-14 22:47:09,067 (client:354) INFO: {'Role': 'Client #2', 'Round': 45, 'Results_raw': {'train_loss': 27.241001, 'val_loss': 27.256044, 'test_loss': 27.95056}}
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2024-11-14 22:48:16,732 (client:354) INFO: {'Role': 'Client #1', 'Round': 45, 'Results_raw': {'train_loss': 31.881495, 'val_loss': 32.693424, 'test_loss': 33.426349}}
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2024-11-14 22:49:24,150 (client:354) INFO: {'Role': 'Client #8', 'Round': 45, 'Results_raw': {'train_loss': 31.530424, 'val_loss': 31.635916, 'test_loss': 32.833825}}
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2024-11-14 22:50:29,966 (client:354) INFO: {'Role': 'Client #6', 'Round': 45, 'Results_raw': {'train_loss': 32.51671, 'val_loss': 30.999065, 'test_loss': 36.05587}}
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2024-11-14 22:51:35,060 (client:354) INFO: {'Role': 'Client #3', 'Round': 45, 'Results_raw': {'train_loss': 30.493234, 'val_loss': 33.572291, 'test_loss': 34.006871}}
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2024-11-14 22:51:35,067 (server:615) INFO: {'Role': 'Server #', 'Round': 44, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.048843), 'test_loss': np.float64(225555.082861), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.583982), 'val_loss': np.float64(222936.988342), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.048843), 'test_loss': np.float64(225555.082861), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.583982), 'val_loss': np.float64(222936.988342), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.905586), 'test_avg_loss_bottom_decile': np.float64(38.556803), 'test_avg_loss_top_decile': np.float64(42.558509), 'test_avg_loss_min': np.float64(35.486211), 'test_avg_loss_max': np.float64(42.558509), 'test_avg_loss_bottom10%': np.float64(35.486211), 'test_avg_loss_top10%': np.float64(42.558509), 'test_avg_loss_cos1': np.float64(0.99887), 'test_avg_loss_entropy': np.float64(2.301432), 'test_loss_std': np.float64(10732.262503), 'test_loss_bottom_decile': np.float64(217151.912842), 'test_loss_top_decile': np.float64(239689.521362), 'test_loss_min': np.float64(199858.338501), 'test_loss_max': np.float64(239689.521362), 'test_loss_bottom10%': np.float64(199858.338501), 'test_loss_top10%': np.float64(239689.521362), 'test_loss_cos1': np.float64(0.99887), 'test_loss_entropy': np.float64(2.301432), 'val_avg_loss_std': np.float64(2.220918), 'val_avg_loss_bottom_decile': np.float64(38.311868), 'val_avg_loss_top_decile': np.float64(43.049041), 'val_avg_loss_min': np.float64(34.82218), 'val_avg_loss_max': np.float64(43.049041), 'val_avg_loss_bottom10%': np.float64(34.82218), 'val_avg_loss_top10%': np.float64(43.049041), 'val_avg_loss_cos1': np.float64(0.99843), 'val_avg_loss_entropy': np.float64(2.300996), 'val_loss_std': np.float64(12508.208263), 'val_loss_bottom_decile': np.float64(215772.438721), 'val_loss_top_decile': np.float64(242452.196411), 'val_loss_min': np.float64(196118.518311), 'val_loss_max': np.float64(242452.196411), 'val_loss_bottom10%': np.float64(196118.518311), 'val_loss_top10%': np.float64(242452.196411), 'val_loss_cos1': np.float64(0.99843), 'val_loss_entropy': np.float64(2.300996)}}
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2024-11-14 22:51:35,115 (server:353) INFO: Server: Starting evaluation at the end of round 45.
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2024-11-14 22:51:35,116 (server:359) INFO: ----------- Starting a new training round (Round #46) -------------
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2024-11-14 22:54:23,332 (client:354) INFO: {'Role': 'Client #10', 'Round': 46, 'Results_raw': {'train_loss': 31.57834, 'val_loss': 31.72704, 'test_loss': 32.629673}}
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2024-11-14 22:55:23,428 (client:354) INFO: {'Role': 'Client #8', 'Round': 46, 'Results_raw': {'train_loss': 31.454711, 'val_loss': 31.693639, 'test_loss': 32.904274}}
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2024-11-14 22:56:25,702 (client:354) INFO: {'Role': 'Client #4', 'Round': 46, 'Results_raw': {'train_loss': 34.856182, 'val_loss': 35.213658, 'test_loss': 34.398374}}
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2024-11-14 22:57:34,614 (client:354) INFO: {'Role': 'Client #5', 'Round': 46, 'Results_raw': {'train_loss': 30.83617, 'val_loss': 31.705216, 'test_loss': 33.13057}}
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2024-11-14 22:58:35,121 (client:354) INFO: {'Role': 'Client #3', 'Round': 46, 'Results_raw': {'train_loss': 30.500415, 'val_loss': 33.07313, 'test_loss': 33.940791}}
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2024-11-14 22:59:28,474 (client:354) INFO: {'Role': 'Client #2', 'Round': 46, 'Results_raw': {'train_loss': 27.215573, 'val_loss': 27.123261, 'test_loss': 28.063098}}
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2024-11-14 23:00:23,143 (client:354) INFO: {'Role': 'Client #6', 'Round': 46, 'Results_raw': {'train_loss': 32.476942, 'val_loss': 31.12877, 'test_loss': 35.222882}}
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2024-11-14 23:01:16,201 (client:354) INFO: {'Role': 'Client #7', 'Round': 46, 'Results_raw': {'train_loss': 30.325113, 'val_loss': 30.216055, 'test_loss': 31.225587}}
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2024-11-14 23:02:09,309 (client:354) INFO: {'Role': 'Client #1', 'Round': 46, 'Results_raw': {'train_loss': 31.872572, 'val_loss': 32.591931, 'test_loss': 33.60176}}
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2024-11-14 23:03:02,207 (client:354) INFO: {'Role': 'Client #9', 'Round': 46, 'Results_raw': {'train_loss': 33.538005, 'val_loss': 33.426876, 'test_loss': 35.227047}}
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2024-11-14 23:03:02,215 (server:615) INFO: {'Role': 'Server #', 'Round': 45, 'Results_weighted_avg': {'test_avg_loss': np.float64(40.002247), 'test_loss': np.float64(225292.654077), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.53072), 'val_loss': np.float64(222637.017212), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(40.002247), 'test_loss': np.float64(225292.654077), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.53072), 'val_loss': np.float64(222637.017212), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.898515), 'test_avg_loss_bottom_decile': np.float64(38.491572), 'test_avg_loss_top_decile': np.float64(42.490539), 'test_avg_loss_min': np.float64(35.490539), 'test_avg_loss_max': np.float64(42.490539), 'test_avg_loss_bottom10%': np.float64(35.490539), 'test_avg_loss_top10%': np.float64(42.490539), 'test_avg_loss_cos1': np.float64(0.998876), 'test_avg_loss_entropy': np.float64(2.301439), 'test_loss_std': np.float64(10692.435607), 'test_loss_bottom_decile': np.float64(216784.536255), 'test_loss_top_decile': np.float64(239306.718384), 'test_loss_min': np.float64(199882.714966), 'test_loss_max': np.float64(239306.718384), 'test_loss_bottom10%': np.float64(199882.714966), 'test_loss_top10%': np.float64(239306.718384), 'test_loss_cos1': np.float64(0.998876), 'test_loss_entropy': np.float64(2.301439), 'val_avg_loss_std': np.float64(2.214987), 'val_avg_loss_bottom_decile': np.float64(38.261341), 'val_avg_loss_top_decile': np.float64(43.031178), 'val_avg_loss_min': np.float64(34.795611), 'val_avg_loss_max': np.float64(43.031178), 'val_avg_loss_bottom10%': np.float64(34.795611), 'val_avg_loss_top10%': np.float64(43.031178), 'val_avg_loss_cos1': np.float64(0.998434), 'val_avg_loss_entropy': np.float64(2.301001), 'val_loss_std': np.float64(12474.809541), 'val_loss_bottom_decile': np.float64(215487.873047), 'val_loss_top_decile': np.float64(242351.595947), 'val_loss_min': np.float64(195968.88208), 'val_loss_max': np.float64(242351.595947), 'val_loss_bottom10%': np.float64(195968.88208), 'val_loss_top10%': np.float64(242351.595947), 'val_loss_cos1': np.float64(0.998434), 'val_loss_entropy': np.float64(2.301001)}}
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2024-11-14 23:03:02,283 (server:353) INFO: Server: Starting evaluation at the end of round 46.
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2024-11-14 23:03:02,284 (server:359) INFO: ----------- Starting a new training round (Round #47) -------------
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2024-11-14 23:05:31,418 (client:354) INFO: {'Role': 'Client #7', 'Round': 47, 'Results_raw': {'train_loss': 30.314005, 'val_loss': 30.780945, 'test_loss': 31.61839}}
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2024-11-14 23:06:25,243 (client:354) INFO: {'Role': 'Client #5', 'Round': 47, 'Results_raw': {'train_loss': 30.765252, 'val_loss': 31.710262, 'test_loss': 33.273987}}
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2024-11-14 23:07:17,906 (client:354) INFO: {'Role': 'Client #3', 'Round': 47, 'Results_raw': {'train_loss': 30.445363, 'val_loss': 33.145475, 'test_loss': 33.894088}}
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2024-11-14 23:08:10,441 (client:354) INFO: {'Role': 'Client #8', 'Round': 47, 'Results_raw': {'train_loss': 31.478226, 'val_loss': 31.704757, 'test_loss': 32.758509}}
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2024-11-14 23:09:03,438 (client:354) INFO: {'Role': 'Client #2', 'Round': 47, 'Results_raw': {'train_loss': 27.197343, 'val_loss': 27.175176, 'test_loss': 28.057221}}
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2024-11-14 23:09:56,475 (client:354) INFO: {'Role': 'Client #4', 'Round': 47, 'Results_raw': {'train_loss': 34.863278, 'val_loss': 35.232188, 'test_loss': 34.618141}}
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2024-11-14 23:10:49,617 (client:354) INFO: {'Role': 'Client #1', 'Round': 47, 'Results_raw': {'train_loss': 31.870748, 'val_loss': 32.632061, 'test_loss': 33.671404}}
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2024-11-14 23:11:42,411 (client:354) INFO: {'Role': 'Client #10', 'Round': 47, 'Results_raw': {'train_loss': 31.502072, 'val_loss': 31.633444, 'test_loss': 32.565806}}
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2024-11-14 23:12:35,351 (client:354) INFO: {'Role': 'Client #9', 'Round': 47, 'Results_raw': {'train_loss': 33.52933, 'val_loss': 33.746449, 'test_loss': 35.75572}}
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2024-11-14 23:13:28,631 (client:354) INFO: {'Role': 'Client #6', 'Round': 47, 'Results_raw': {'train_loss': 32.483195, 'val_loss': 30.895477, 'test_loss': 34.214306}}
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2024-11-14 23:13:28,633 (server:615) INFO: {'Role': 'Server #', 'Round': 46, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.911145), 'test_loss': np.float64(224779.567859), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.445476), 'val_loss': np.float64(222156.919568), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.911145), 'test_loss': np.float64(224779.567859), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.445476), 'val_loss': np.float64(222156.919568), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.891711), 'test_avg_loss_bottom_decile': np.float64(38.391703), 'test_avg_loss_top_decile': np.float64(42.483553), 'test_avg_loss_min': np.float64(35.480132), 'test_avg_loss_max': np.float64(42.483553), 'test_avg_loss_bottom10%': np.float64(35.480132), 'test_avg_loss_top10%': np.float64(42.483553), 'test_avg_loss_cos1': np.float64(0.998879), 'test_avg_loss_entropy': np.float64(2.301443), 'test_loss_std': np.float64(10654.117474), 'test_loss_bottom_decile': np.float64(216222.072998), 'test_loss_top_decile': np.float64(239267.368164), 'test_loss_min': np.float64(199824.105103), 'test_loss_max': np.float64(239267.368164), 'test_loss_bottom10%': np.float64(199824.105103), 'test_loss_top10%': np.float64(239267.368164), 'test_loss_cos1': np.float64(0.998879), 'test_loss_entropy': np.float64(2.301443), 'val_avg_loss_std': np.float64(2.195217), 'val_avg_loss_bottom_decile': np.float64(38.155716), 'val_avg_loss_top_decile': np.float64(42.953402), 'val_avg_loss_min': np.float64(34.814261), 'val_avg_loss_max': np.float64(42.953402), 'val_avg_loss_bottom10%': np.float64(34.814261), 'val_avg_loss_top10%': np.float64(42.953402), 'val_avg_loss_cos1': np.float64(0.998455), 'val_avg_loss_entropy': np.float64(2.301024), 'val_loss_std': np.float64(12363.462702), 'val_loss_bottom_decile': np.float64(214892.990234), 'val_loss_top_decile': np.float64(241913.562256), 'val_loss_min': np.float64(196073.916504), 'val_loss_max': np.float64(241913.562256), 'val_loss_bottom10%': np.float64(196073.916504), 'val_loss_top10%': np.float64(241913.562256), 'val_loss_cos1': np.float64(0.998455), 'val_loss_entropy': np.float64(2.301024)}}
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2024-11-14 23:13:28,662 (server:353) INFO: Server: Starting evaluation at the end of round 47.
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2024-11-14 23:13:28,663 (server:359) INFO: ----------- Starting a new training round (Round #48) -------------
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2024-11-14 23:15:58,157 (client:354) INFO: {'Role': 'Client #7', 'Round': 48, 'Results_raw': {'train_loss': 30.325378, 'val_loss': 30.452472, 'test_loss': 31.315049}}
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2024-11-14 23:16:51,001 (client:354) INFO: {'Role': 'Client #4', 'Round': 48, 'Results_raw': {'train_loss': 34.896297, 'val_loss': 35.047389, 'test_loss': 34.39722}}
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2024-11-14 23:17:43,722 (client:354) INFO: {'Role': 'Client #8', 'Round': 48, 'Results_raw': {'train_loss': 31.435582, 'val_loss': 31.603201, 'test_loss': 32.828438}}
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2024-11-14 23:18:37,824 (client:354) INFO: {'Role': 'Client #5', 'Round': 48, 'Results_raw': {'train_loss': 30.774444, 'val_loss': 31.448952, 'test_loss': 32.755386}}
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2024-11-14 23:19:30,679 (client:354) INFO: {'Role': 'Client #6', 'Round': 48, 'Results_raw': {'train_loss': 32.511309, 'val_loss': 31.055868, 'test_loss': 36.014896}}
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2024-11-14 23:20:23,542 (client:354) INFO: {'Role': 'Client #9', 'Round': 48, 'Results_raw': {'train_loss': 33.563949, 'val_loss': 33.65065, 'test_loss': 35.526491}}
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2024-11-14 23:21:16,313 (client:354) INFO: {'Role': 'Client #2', 'Round': 48, 'Results_raw': {'train_loss': 27.246288, 'val_loss': 27.192014, 'test_loss': 27.949132}}
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2024-11-14 23:22:09,217 (client:354) INFO: {'Role': 'Client #10', 'Round': 48, 'Results_raw': {'train_loss': 31.491023, 'val_loss': 31.874986, 'test_loss': 32.682196}}
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2024-11-14 23:23:02,319 (client:354) INFO: {'Role': 'Client #3', 'Round': 48, 'Results_raw': {'train_loss': 30.480537, 'val_loss': 32.648253, 'test_loss': 33.604609}}
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2024-11-14 23:23:57,381 (client:354) INFO: {'Role': 'Client #1', 'Round': 48, 'Results_raw': {'train_loss': 31.81036, 'val_loss': 32.590662, 'test_loss': 33.712347}}
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2024-11-14 23:23:57,385 (server:615) INFO: {'Role': 'Server #', 'Round': 47, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.953512), 'test_loss': np.float64(225018.179211), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.477916), 'val_loss': np.float64(222339.62301), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.953512), 'test_loss': np.float64(225018.179211), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.477916), 'val_loss': np.float64(222339.62301), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.888461), 'test_avg_loss_bottom_decile': np.float64(38.457092), 'test_avg_loss_top_decile': np.float64(42.438997), 'test_avg_loss_min': np.float64(35.48788), 'test_avg_loss_max': np.float64(42.438997), 'test_avg_loss_bottom10%': np.float64(35.48788), 'test_avg_loss_top10%': np.float64(42.438997), 'test_avg_loss_cos1': np.float64(0.998885), 'test_avg_loss_entropy': np.float64(2.301449), 'test_loss_std': np.float64(10635.813649), 'test_loss_bottom_decile': np.float64(216590.342041), 'test_loss_top_decile': np.float64(239016.433716), 'test_loss_min': np.float64(199867.740967), 'test_loss_max': np.float64(239016.433716), 'test_loss_bottom10%': np.float64(199867.740967), 'test_loss_top10%': np.float64(239016.433716), 'test_loss_cos1': np.float64(0.998885), 'test_loss_entropy': np.float64(2.301449), 'val_avg_loss_std': np.float64(2.212891), 'val_avg_loss_bottom_decile': np.float64(38.203962), 'val_avg_loss_top_decile': np.float64(42.983776), 'val_avg_loss_min': np.float64(34.804822), 'val_avg_loss_max': np.float64(42.983776), 'val_avg_loss_bottom10%': np.float64(34.804822), 'val_avg_loss_top10%': np.float64(42.983776), 'val_avg_loss_cos1': np.float64(0.998433), 'val_avg_loss_entropy': np.float64(2.301001), 'val_loss_std': np.float64(12463.003339), 'val_loss_bottom_decile': np.float64(215164.713501), 'val_loss_top_decile': np.float64(242084.624146), 'val_loss_min': np.float64(196020.755127), 'val_loss_max': np.float64(242084.624146), 'val_loss_bottom10%': np.float64(196020.755127), 'val_loss_top10%': np.float64(242084.624146), 'val_loss_cos1': np.float64(0.998433), 'val_loss_entropy': np.float64(2.301001)}}
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2024-11-14 23:23:57,417 (server:353) INFO: Server: Starting evaluation at the end of round 48.
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2024-11-14 23:23:57,418 (server:359) INFO: ----------- Starting a new training round (Round #49) -------------
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2024-11-14 23:26:30,209 (client:354) INFO: {'Role': 'Client #10', 'Round': 49, 'Results_raw': {'train_loss': 31.4754, 'val_loss': 31.819915, 'test_loss': 32.732357}}
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2024-11-14 23:27:25,241 (client:354) INFO: {'Role': 'Client #3', 'Round': 49, 'Results_raw': {'train_loss': 30.456809, 'val_loss': 33.01886, 'test_loss': 33.749294}}
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2024-11-14 23:28:21,027 (client:354) INFO: {'Role': 'Client #2', 'Round': 49, 'Results_raw': {'train_loss': 27.23677, 'val_loss': 27.1306, 'test_loss': 28.215054}}
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2024-11-14 23:29:13,799 (client:354) INFO: {'Role': 'Client #6', 'Round': 49, 'Results_raw': {'train_loss': 32.478724, 'val_loss': 31.299968, 'test_loss': 36.565324}}
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2024-11-14 23:30:06,886 (client:354) INFO: {'Role': 'Client #7', 'Round': 49, 'Results_raw': {'train_loss': 30.275029, 'val_loss': 30.252939, 'test_loss': 31.080915}}
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2024-11-14 23:31:00,515 (client:354) INFO: {'Role': 'Client #4', 'Round': 49, 'Results_raw': {'train_loss': 34.827721, 'val_loss': 35.398699, 'test_loss': 34.604818}}
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2024-11-14 23:31:54,366 (client:354) INFO: {'Role': 'Client #5', 'Round': 49, 'Results_raw': {'train_loss': 30.749008, 'val_loss': 31.705091, 'test_loss': 33.271246}}
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2024-11-14 23:32:47,840 (client:354) INFO: {'Role': 'Client #9', 'Round': 49, 'Results_raw': {'train_loss': 33.470757, 'val_loss': 33.669149, 'test_loss': 36.023367}}
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2024-11-14 23:33:41,345 (client:354) INFO: {'Role': 'Client #8', 'Round': 49, 'Results_raw': {'train_loss': 31.377254, 'val_loss': 31.730749, 'test_loss': 32.998811}}
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2024-11-14 23:34:34,646 (client:354) INFO: {'Role': 'Client #1', 'Round': 49, 'Results_raw': {'train_loss': 31.752238, 'val_loss': 32.867744, 'test_loss': 33.783025}}
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2024-11-14 23:34:34,649 (server:615) INFO: {'Role': 'Server #', 'Round': 48, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.753879), 'test_loss': np.float64(223893.844946), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.292887), 'val_loss': np.float64(221297.541809), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.753879), 'test_loss': np.float64(223893.844946), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.292887), 'val_loss': np.float64(221297.541809), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.860516), 'test_avg_loss_bottom_decile': np.float64(38.23493), 'test_avg_loss_top_decile': np.float64(42.273009), 'test_avg_loss_min': np.float64(35.3736), 'test_avg_loss_max': np.float64(42.273009), 'test_avg_loss_bottom10%': np.float64(35.3736), 'test_avg_loss_top10%': np.float64(42.273009), 'test_avg_loss_cos1': np.float64(0.998907), 'test_avg_loss_entropy': np.float64(2.301472), 'test_loss_std': np.float64(10478.426048), 'test_loss_bottom_decile': np.float64(215339.12561), 'test_loss_top_decile': np.float64(238081.589355), 'test_loss_min': np.float64(199224.113159), 'test_loss_max': np.float64(238081.589355), 'test_loss_bottom10%': np.float64(199224.113159), 'test_loss_top10%': np.float64(238081.589355), 'test_loss_cos1': np.float64(0.998907), 'test_loss_entropy': np.float64(2.301472), 'val_avg_loss_std': np.float64(2.153084), 'val_avg_loss_bottom_decile': np.float64(38.006785), 'val_avg_loss_top_decile': np.float64(42.764506), 'val_avg_loss_min': np.float64(34.730253), 'val_avg_loss_max': np.float64(42.764506), 'val_avg_loss_bottom10%': np.float64(34.730253), 'val_avg_loss_top10%': np.float64(42.764506), 'val_avg_loss_cos1': np.float64(0.998502), 'val_avg_loss_entropy': np.float64(2.301071), 'val_loss_std': np.float64(12126.168952), 'val_loss_bottom_decile': np.float64(214054.212524), 'val_loss_top_decile': np.float64(240849.697876), 'val_loss_min': np.float64(195600.786499), 'val_loss_max': np.float64(240849.697876), 'val_loss_bottom10%': np.float64(195600.786499), 'val_loss_top10%': np.float64(240849.697876), 'val_loss_cos1': np.float64(0.998502), 'val_loss_entropy': np.float64(2.301071)}}
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2024-11-14 23:34:34,681 (server:353) INFO: Server: Starting evaluation at the end of round 49.
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2024-11-14 23:34:34,682 (server:359) INFO: ----------- Starting a new training round (Round #50) -------------
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2024-11-14 23:37:13,996 (client:354) INFO: {'Role': 'Client #8', 'Round': 50, 'Results_raw': {'train_loss': 31.414892, 'val_loss': 31.856717, 'test_loss': 32.923067}}
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2024-11-14 23:38:10,107 (client:354) INFO: {'Role': 'Client #1', 'Round': 50, 'Results_raw': {'train_loss': 31.758442, 'val_loss': 32.532954, 'test_loss': 33.595979}}
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2024-11-14 23:39:04,711 (client:354) INFO: {'Role': 'Client #9', 'Round': 50, 'Results_raw': {'train_loss': 33.512023, 'val_loss': 33.673868, 'test_loss': 35.375649}}
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2024-11-14 23:39:59,078 (client:354) INFO: {'Role': 'Client #10', 'Round': 50, 'Results_raw': {'train_loss': 31.447399, 'val_loss': 31.629952, 'test_loss': 32.438689}}
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2024-11-14 23:40:53,721 (client:354) INFO: {'Role': 'Client #3', 'Round': 50, 'Results_raw': {'train_loss': 30.444448, 'val_loss': 33.14188, 'test_loss': 33.865175}}
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2024-11-14 23:41:50,106 (client:354) INFO: {'Role': 'Client #5', 'Round': 50, 'Results_raw': {'train_loss': 30.702867, 'val_loss': 31.795891, 'test_loss': 33.13956}}
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2024-11-14 23:42:44,139 (client:354) INFO: {'Role': 'Client #7', 'Round': 50, 'Results_raw': {'train_loss': 30.208051, 'val_loss': 30.046743, 'test_loss': 30.916923}}
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2024-11-14 23:43:37,652 (client:354) INFO: {'Role': 'Client #2', 'Round': 50, 'Results_raw': {'train_loss': 27.171117, 'val_loss': 27.01224, 'test_loss': 27.943293}}
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2024-11-14 23:44:31,666 (client:354) INFO: {'Role': 'Client #6', 'Round': 50, 'Results_raw': {'train_loss': 32.453943, 'val_loss': 30.878732, 'test_loss': 35.371254}}
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2024-11-14 23:45:25,617 (client:354) INFO: {'Role': 'Client #4', 'Round': 50, 'Results_raw': {'train_loss': 34.825415, 'val_loss': 35.027572, 'test_loss': 34.331845}}
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2024-11-14 23:45:25,620 (server:615) INFO: {'Role': 'Server #', 'Round': 49, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.883761), 'test_loss': np.float64(224625.344446), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.410869), 'val_loss': np.float64(221962.016565), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.883761), 'test_loss': np.float64(224625.344446), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.410869), 'val_loss': np.float64(221962.016565), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.880694), 'test_avg_loss_bottom_decile': np.float64(38.39348), 'test_avg_loss_top_decile': np.float64(42.43766), 'test_avg_loss_min': np.float64(35.422644), 'test_avg_loss_max': np.float64(42.43766), 'test_avg_loss_bottom10%': np.float64(35.422644), 'test_avg_loss_top10%': np.float64(42.43766), 'test_avg_loss_cos1': np.float64(0.99889), 'test_avg_loss_entropy': np.float64(2.301454), 'test_loss_std': np.float64(10592.068943), 'test_loss_bottom_decile': np.float64(216232.079346), 'test_loss_top_decile': np.float64(239008.899536), 'test_loss_min': np.float64(199500.329224), 'test_loss_max': np.float64(239008.899536), 'test_loss_bottom10%': np.float64(199500.329224), 'test_loss_top10%': np.float64(239008.899536), 'test_loss_cos1': np.float64(0.99889), 'test_loss_entropy': np.float64(2.301454), 'val_avg_loss_std': np.float64(2.144504), 'val_avg_loss_bottom_decile': np.float64(38.181909), 'val_avg_loss_top_decile': np.float64(42.842523), 'val_avg_loss_min': np.float64(34.769672), 'val_avg_loss_max': np.float64(42.842523), 'val_avg_loss_bottom10%': np.float64(34.769672), 'val_avg_loss_top10%': np.float64(42.842523), 'val_avg_loss_cos1': np.float64(0.998523), 'val_avg_loss_entropy': np.float64(2.30109), 'val_loss_std': np.float64(12077.845354), 'val_loss_bottom_decile': np.float64(215040.511963), 'val_loss_top_decile': np.float64(241289.090576), 'val_loss_min': np.float64(195822.793701), 'val_loss_max': np.float64(241289.090576), 'val_loss_bottom10%': np.float64(195822.793701), 'val_loss_top10%': np.float64(241289.090576), 'val_loss_cos1': np.float64(0.998523), 'val_loss_entropy': np.float64(2.30109)}}
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2024-11-14 23:45:25,662 (server:353) INFO: Server: Starting evaluation at the end of round 50.
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2024-11-14 23:45:25,663 (server:359) INFO: ----------- Starting a new training round (Round #51) -------------
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2024-11-14 23:47:52,683 (client:354) INFO: {'Role': 'Client #8', 'Round': 51, 'Results_raw': {'train_loss': 31.447053, 'val_loss': 31.604203, 'test_loss': 32.923343}}
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2024-11-14 23:48:49,618 (client:354) INFO: {'Role': 'Client #1', 'Round': 51, 'Results_raw': {'train_loss': 31.748824, 'val_loss': 32.524619, 'test_loss': 33.562445}}
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2024-11-14 23:49:43,750 (client:354) INFO: {'Role': 'Client #4', 'Round': 51, 'Results_raw': {'train_loss': 34.799725, 'val_loss': 35.075463, 'test_loss': 34.376068}}
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2024-11-14 23:50:37,144 (client:354) INFO: {'Role': 'Client #10', 'Round': 51, 'Results_raw': {'train_loss': 31.486364, 'val_loss': 31.739673, 'test_loss': 32.468392}}
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2024-11-14 23:51:31,205 (client:354) INFO: {'Role': 'Client #3', 'Round': 51, 'Results_raw': {'train_loss': 30.405149, 'val_loss': 33.026963, 'test_loss': 33.62427}}
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2024-11-14 23:52:28,254 (client:354) INFO: {'Role': 'Client #2', 'Round': 51, 'Results_raw': {'train_loss': 27.185164, 'val_loss': 27.185729, 'test_loss': 28.016659}}
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2024-11-14 23:53:24,620 (client:354) INFO: {'Role': 'Client #7', 'Round': 51, 'Results_raw': {'train_loss': 30.238015, 'val_loss': 30.314782, 'test_loss': 31.28376}}
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2024-11-14 23:54:18,194 (client:354) INFO: {'Role': 'Client #5', 'Round': 51, 'Results_raw': {'train_loss': 30.717575, 'val_loss': 31.435544, 'test_loss': 32.826517}}
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2024-11-14 23:55:12,765 (client:354) INFO: {'Role': 'Client #9', 'Round': 51, 'Results_raw': {'train_loss': 33.426922, 'val_loss': 33.553805, 'test_loss': 35.33342}}
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2024-11-14 23:56:08,222 (client:354) INFO: {'Role': 'Client #6', 'Round': 51, 'Results_raw': {'train_loss': 32.450717, 'val_loss': 31.011442, 'test_loss': 34.844433}}
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2024-11-14 23:56:08,225 (server:615) INFO: {'Role': 'Server #', 'Round': 50, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.662893), 'test_loss': np.float64(223381.414124), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.20886), 'val_loss': np.float64(220824.3021), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.662893), 'test_loss': np.float64(223381.414124), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.20886), 'val_loss': np.float64(220824.3021), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.891244), 'test_avg_loss_bottom_decile': np.float64(38.141209), 'test_avg_loss_top_decile': np.float64(42.22329), 'test_avg_loss_min': np.float64(35.17163), 'test_avg_loss_max': np.float64(42.22329), 'test_avg_loss_bottom10%': np.float64(35.17163), 'test_avg_loss_top10%': np.float64(42.22329), 'test_avg_loss_cos1': np.float64(0.998865), 'test_avg_loss_entropy': np.float64(2.301428), 'test_loss_std': np.float64(10651.483464), 'test_loss_bottom_decile': np.float64(214811.286865), 'test_loss_top_decile': np.float64(237801.567017), 'test_loss_min': np.float64(198086.621216), 'test_loss_max': np.float64(237801.567017), 'test_loss_bottom10%': np.float64(198086.621216), 'test_loss_top10%': np.float64(237801.567017), 'test_loss_cos1': np.float64(0.998865), 'test_loss_entropy': np.float64(2.301428), 'val_avg_loss_std': np.float64(2.185906), 'val_avg_loss_bottom_decile': np.float64(37.907593), 'val_avg_loss_top_decile': np.float64(42.69078), 'val_avg_loss_min': np.float64(34.515682), 'val_avg_loss_max': np.float64(42.69078), 'val_avg_loss_bottom10%': np.float64(34.515682), 'val_avg_loss_top10%': np.float64(42.69078), 'val_avg_loss_cos1': np.float64(0.99845), 'val_avg_loss_entropy': np.float64(2.301016), 'val_loss_std': np.float64(12311.020122), 'val_loss_bottom_decile': np.float64(213495.565796), 'val_loss_top_decile': np.float64(240434.472534), 'val_loss_min': np.float64(194392.321411), 'val_loss_max': np.float64(240434.472534), 'val_loss_bottom10%': np.float64(194392.321411), 'val_loss_top10%': np.float64(240434.472534), 'val_loss_cos1': np.float64(0.99845), 'val_loss_entropy': np.float64(2.301016)}}
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2024-11-14 23:56:08,257 (server:353) INFO: Server: Starting evaluation at the end of round 51.
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2024-11-14 23:56:08,257 (server:359) INFO: ----------- Starting a new training round (Round #52) -------------
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2024-11-14 23:58:39,623 (client:354) INFO: {'Role': 'Client #10', 'Round': 52, 'Results_raw': {'train_loss': 31.413354, 'val_loss': 31.747583, 'test_loss': 32.699909}}
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2024-11-14 23:59:36,352 (client:354) INFO: {'Role': 'Client #1', 'Round': 52, 'Results_raw': {'train_loss': 31.748634, 'val_loss': 32.80456, 'test_loss': 33.638377}}
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2024-11-15 00:00:28,837 (client:354) INFO: {'Role': 'Client #5', 'Round': 52, 'Results_raw': {'train_loss': 30.662892, 'val_loss': 31.605801, 'test_loss': 33.107266}}
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2024-11-15 00:01:21,320 (client:354) INFO: {'Role': 'Client #4', 'Round': 52, 'Results_raw': {'train_loss': 34.680638, 'val_loss': 34.912063, 'test_loss': 34.375118}}
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2024-11-15 00:02:14,281 (client:354) INFO: {'Role': 'Client #7', 'Round': 52, 'Results_raw': {'train_loss': 30.220386, 'val_loss': 30.151937, 'test_loss': 30.899974}}
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2024-11-15 00:03:07,304 (client:354) INFO: {'Role': 'Client #9', 'Round': 52, 'Results_raw': {'train_loss': 33.51564, 'val_loss': 33.746677, 'test_loss': 35.630799}}
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2024-11-15 00:04:00,432 (client:354) INFO: {'Role': 'Client #3', 'Round': 52, 'Results_raw': {'train_loss': 30.331059, 'val_loss': 32.744277, 'test_loss': 33.34565}}
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2024-11-15 00:04:53,590 (client:354) INFO: {'Role': 'Client #6', 'Round': 52, 'Results_raw': {'train_loss': 32.373024, 'val_loss': 31.11183, 'test_loss': 34.458063}}
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2024-11-15 00:05:46,550 (client:354) INFO: {'Role': 'Client #2', 'Round': 52, 'Results_raw': {'train_loss': 27.162112, 'val_loss': 27.337305, 'test_loss': 28.357178}}
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2024-11-15 00:06:39,378 (client:354) INFO: {'Role': 'Client #8', 'Round': 52, 'Results_raw': {'train_loss': 31.402631, 'val_loss': 32.034704, 'test_loss': 33.224247}}
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2024-11-15 00:06:39,386 (server:615) INFO: {'Role': 'Server #', 'Round': 51, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.818861), 'test_loss': np.float64(224259.825488), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.356981), 'val_loss': np.float64(221658.517517), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.818861), 'test_loss': np.float64(224259.825488), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.356981), 'val_loss': np.float64(221658.517517), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.905291), 'test_avg_loss_bottom_decile': np.float64(38.301366), 'test_avg_loss_top_decile': np.float64(42.368451), 'test_avg_loss_min': np.float64(35.296924), 'test_avg_loss_max': np.float64(42.368451), 'test_avg_loss_bottom10%': np.float64(35.296924), 'test_avg_loss_top10%': np.float64(42.368451), 'test_avg_loss_cos1': np.float64(0.998857), 'test_avg_loss_entropy': np.float64(2.30142), 'test_loss_std': np.float64(10730.598399), 'test_loss_bottom_decile': np.float64(215713.292236), 'test_loss_top_decile': np.float64(238619.113403), 'test_loss_min': np.float64(198792.278564), 'test_loss_max': np.float64(238619.113403), 'test_loss_bottom10%': np.float64(198792.278564), 'test_loss_top10%': np.float64(238619.113403), 'test_loss_cos1': np.float64(0.998857), 'test_loss_entropy': np.float64(2.30142), 'val_avg_loss_std': np.float64(2.195743), 'val_avg_loss_bottom_decile': np.float64(38.099952), 'val_avg_loss_top_decile': np.float64(42.825107), 'val_avg_loss_min': np.float64(34.646342), 'val_avg_loss_max': np.float64(42.825107), 'val_avg_loss_bottom10%': np.float64(34.646342), 'val_avg_loss_top10%': np.float64(42.825107), 'val_avg_loss_cos1': np.float64(0.998447), 'val_avg_loss_entropy': np.float64(2.301014), 'val_loss_std': np.float64(12366.426776), 'val_loss_bottom_decile': np.float64(214578.927002), 'val_loss_top_decile': np.float64(241191.001465), 'val_loss_min': np.float64(195128.195435), 'val_loss_max': np.float64(241191.001465), 'val_loss_bottom10%': np.float64(195128.195435), 'val_loss_top10%': np.float64(241191.001465), 'val_loss_cos1': np.float64(0.998447), 'val_loss_entropy': np.float64(2.301014)}}
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2024-11-15 00:06:39,417 (server:353) INFO: Server: Starting evaluation at the end of round 52.
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2024-11-15 00:06:39,418 (server:359) INFO: ----------- Starting a new training round (Round #53) -------------
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2024-11-15 00:09:08,162 (client:354) INFO: {'Role': 'Client #9', 'Round': 53, 'Results_raw': {'train_loss': 33.440405, 'val_loss': 33.911118, 'test_loss': 35.488756}}
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2024-11-15 00:10:01,163 (client:354) INFO: {'Role': 'Client #1', 'Round': 53, 'Results_raw': {'train_loss': 31.764225, 'val_loss': 32.781258, 'test_loss': 33.871116}}
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2024-11-15 00:10:53,576 (client:354) INFO: {'Role': 'Client #2', 'Round': 53, 'Results_raw': {'train_loss': 27.097589, 'val_loss': 27.329849, 'test_loss': 28.092984}}
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2024-11-15 00:11:48,902 (client:354) INFO: {'Role': 'Client #6', 'Round': 53, 'Results_raw': {'train_loss': 32.403307, 'val_loss': 30.900043, 'test_loss': 34.411217}}
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2024-11-15 00:12:42,605 (client:354) INFO: {'Role': 'Client #3', 'Round': 53, 'Results_raw': {'train_loss': 30.312227, 'val_loss': 32.926559, 'test_loss': 33.409468}}
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2024-11-15 00:13:35,877 (client:354) INFO: {'Role': 'Client #4', 'Round': 53, 'Results_raw': {'train_loss': 34.661167, 'val_loss': 35.262853, 'test_loss': 34.749631}}
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2024-11-15 00:14:29,039 (client:354) INFO: {'Role': 'Client #10', 'Round': 53, 'Results_raw': {'train_loss': 31.341572, 'val_loss': 31.785753, 'test_loss': 32.722084}}
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2024-11-15 00:15:21,960 (client:354) INFO: {'Role': 'Client #8', 'Round': 53, 'Results_raw': {'train_loss': 31.392329, 'val_loss': 31.62489, 'test_loss': 32.916169}}
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2024-11-15 00:16:14,878 (client:354) INFO: {'Role': 'Client #7', 'Round': 53, 'Results_raw': {'train_loss': 30.16085, 'val_loss': 30.03813, 'test_loss': 30.895704}}
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2024-11-15 00:17:07,998 (client:354) INFO: {'Role': 'Client #5', 'Round': 53, 'Results_raw': {'train_loss': 30.639163, 'val_loss': 31.441646, 'test_loss': 32.766626}}
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2024-11-15 00:17:08,000 (server:615) INFO: {'Role': 'Server #', 'Round': 52, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.777856), 'test_loss': np.float64(224028.88761), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.31037), 'val_loss': np.float64(221396.00116), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.777856), 'test_loss': np.float64(224028.88761), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.31037), 'val_loss': np.float64(221396.00116), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.904786), 'test_avg_loss_bottom_decile': np.float64(38.26198), 'test_avg_loss_top_decile': np.float64(42.353493), 'test_avg_loss_min': np.float64(35.260395), 'test_avg_loss_max': np.float64(42.353493), 'test_avg_loss_bottom10%': np.float64(35.260395), 'test_avg_loss_top10%': np.float64(42.353493), 'test_avg_loss_cos1': np.float64(0.998855), 'test_avg_loss_entropy': np.float64(2.301419), 'test_loss_std': np.float64(10727.754818), 'test_loss_bottom_decile': np.float64(215491.471313), 'test_loss_top_decile': np.float64(238534.869873), 'test_loss_min': np.float64(198586.546875), 'test_loss_max': np.float64(238534.869873), 'test_loss_bottom10%': np.float64(198586.546875), 'test_loss_top10%': np.float64(238534.869873), 'test_loss_cos1': np.float64(0.998855), 'test_loss_entropy': np.float64(2.301419), 'val_avg_loss_std': np.float64(2.19114), 'val_avg_loss_bottom_decile': np.float64(38.057103), 'val_avg_loss_top_decile': np.float64(42.805786), 'val_avg_loss_min': np.float64(34.613837), 'val_avg_loss_max': np.float64(42.805786), 'val_avg_loss_bottom10%': np.float64(34.613837), 'val_avg_loss_top10%': np.float64(42.805786), 'val_avg_loss_cos1': np.float64(0.99845), 'val_avg_loss_entropy': np.float64(2.301017), 'val_loss_std': np.float64(12340.500858), 'val_loss_bottom_decile': np.float64(214337.604004), 'val_loss_top_decile': np.float64(241082.186279), 'val_loss_min': np.float64(194945.130859), 'val_loss_max': np.float64(241082.186279), 'val_loss_bottom10%': np.float64(194945.130859), 'val_loss_top10%': np.float64(241082.186279), 'val_loss_cos1': np.float64(0.99845), 'val_loss_entropy': np.float64(2.301017)}}
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2024-11-15 00:17:08,036 (server:353) INFO: Server: Starting evaluation at the end of round 53.
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2024-11-15 00:17:08,037 (server:359) INFO: ----------- Starting a new training round (Round #54) -------------
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2024-11-15 00:19:37,462 (client:354) INFO: {'Role': 'Client #5', 'Round': 54, 'Results_raw': {'train_loss': 30.646066, 'val_loss': 31.934714, 'test_loss': 33.325875}}
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2024-11-15 00:20:31,029 (client:354) INFO: {'Role': 'Client #4', 'Round': 54, 'Results_raw': {'train_loss': 34.669171, 'val_loss': 35.043366, 'test_loss': 34.220504}}
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2024-11-15 00:21:23,818 (client:354) INFO: {'Role': 'Client #2', 'Round': 54, 'Results_raw': {'train_loss': 27.087956, 'val_loss': 27.081514, 'test_loss': 28.073227}}
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2024-11-15 00:22:16,276 (client:354) INFO: {'Role': 'Client #9', 'Round': 54, 'Results_raw': {'train_loss': 33.382237, 'val_loss': 33.725634, 'test_loss': 35.398343}}
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2024-11-15 00:23:09,620 (client:354) INFO: {'Role': 'Client #7', 'Round': 54, 'Results_raw': {'train_loss': 30.220849, 'val_loss': 30.38326, 'test_loss': 31.30739}}
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2024-11-15 00:24:02,804 (client:354) INFO: {'Role': 'Client #10', 'Round': 54, 'Results_raw': {'train_loss': 31.362583, 'val_loss': 31.512136, 'test_loss': 32.484988}}
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2024-11-15 00:24:56,470 (client:354) INFO: {'Role': 'Client #3', 'Round': 54, 'Results_raw': {'train_loss': 30.360099, 'val_loss': 33.213719, 'test_loss': 33.803361}}
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2024-11-15 00:25:50,031 (client:354) INFO: {'Role': 'Client #8', 'Round': 54, 'Results_raw': {'train_loss': 31.330158, 'val_loss': 31.586843, 'test_loss': 32.841759}}
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2024-11-15 00:26:43,383 (client:354) INFO: {'Role': 'Client #1', 'Round': 54, 'Results_raw': {'train_loss': 31.673567, 'val_loss': 32.551461, 'test_loss': 33.338797}}
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2024-11-15 00:27:36,602 (client:354) INFO: {'Role': 'Client #6', 'Round': 54, 'Results_raw': {'train_loss': 32.348174, 'val_loss': 31.137059, 'test_loss': 36.436929}}
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2024-11-15 00:27:36,605 (server:615) INFO: {'Role': 'Server #', 'Round': 53, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.758176), 'test_loss': np.float64(223918.046533), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.278401), 'val_loss': np.float64(221215.955725), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.758176), 'test_loss': np.float64(223918.046533), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.278401), 'val_loss': np.float64(221215.955725), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.913336), 'test_avg_loss_bottom_decile': np.float64(38.162291), 'test_avg_loss_top_decile': np.float64(42.427185), 'test_avg_loss_min': np.float64(35.226608), 'test_avg_loss_max': np.float64(42.427185), 'test_avg_loss_bottom10%': np.float64(35.226608), 'test_avg_loss_top10%': np.float64(42.427185), 'test_avg_loss_cos1': np.float64(0.998844), 'test_avg_loss_entropy': np.float64(2.301407), 'test_loss_std': np.float64(10775.906713), 'test_loss_bottom_decile': np.float64(214930.025635), 'test_loss_top_decile': np.float64(238949.906006), 'test_loss_min': np.float64(198396.258545), 'test_loss_max': np.float64(238949.906006), 'test_loss_bottom10%': np.float64(198396.258545), 'test_loss_top10%': np.float64(238949.906006), 'test_loss_cos1': np.float64(0.998844), 'test_loss_entropy': np.float64(2.301407), 'val_avg_loss_std': np.float64(2.167747), 'val_avg_loss_bottom_decile': np.float64(37.952295), 'val_avg_loss_top_decile': np.float64(42.722341), 'val_avg_loss_min': np.float64(34.580115), 'val_avg_loss_max': np.float64(42.722341), 'val_avg_loss_bottom10%': np.float64(34.580115), 'val_avg_loss_top10%': np.float64(42.722341), 'val_avg_loss_cos1': np.float64(0.998481), 'val_avg_loss_entropy': np.float64(2.301047), 'val_loss_std': np.float64(12208.752492), 'val_loss_bottom_decile': np.float64(213747.32605), 'val_loss_top_decile': np.float64(240612.223389), 'val_loss_min': np.float64(194755.208984), 'val_loss_max': np.float64(240612.223389), 'val_loss_bottom10%': np.float64(194755.208984), 'val_loss_top10%': np.float64(240612.223389), 'val_loss_cos1': np.float64(0.998481), 'val_loss_entropy': np.float64(2.301047)}}
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2024-11-15 00:27:36,637 (server:353) INFO: Server: Starting evaluation at the end of round 54.
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2024-11-15 00:27:36,638 (server:359) INFO: ----------- Starting a new training round (Round #55) -------------
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2024-11-15 00:30:08,600 (client:354) INFO: {'Role': 'Client #5', 'Round': 55, 'Results_raw': {'train_loss': 30.609528, 'val_loss': 31.785837, 'test_loss': 33.345493}}
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2024-11-15 00:31:04,127 (client:354) INFO: {'Role': 'Client #2', 'Round': 55, 'Results_raw': {'train_loss': 27.080933, 'val_loss': 27.050388, 'test_loss': 27.815714}}
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2024-11-15 00:31:58,829 (client:354) INFO: {'Role': 'Client #3', 'Round': 55, 'Results_raw': {'train_loss': 30.317226, 'val_loss': 32.703141, 'test_loss': 33.458025}}
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2024-11-15 00:32:53,342 (client:354) INFO: {'Role': 'Client #7', 'Round': 55, 'Results_raw': {'train_loss': 30.115609, 'val_loss': 30.24866, 'test_loss': 31.184977}}
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2024-11-15 00:33:48,381 (client:354) INFO: {'Role': 'Client #6', 'Round': 55, 'Results_raw': {'train_loss': 32.343313, 'val_loss': 30.71594, 'test_loss': 34.967424}}
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2024-11-15 00:34:41,543 (client:354) INFO: {'Role': 'Client #9', 'Round': 55, 'Results_raw': {'train_loss': 33.405088, 'val_loss': 33.705039, 'test_loss': 35.199853}}
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2024-11-15 00:35:34,878 (client:354) INFO: {'Role': 'Client #1', 'Round': 55, 'Results_raw': {'train_loss': 31.659318, 'val_loss': 32.689499, 'test_loss': 33.909422}}
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2024-11-15 00:36:28,209 (client:354) INFO: {'Role': 'Client #10', 'Round': 55, 'Results_raw': {'train_loss': 31.396213, 'val_loss': 31.802062, 'test_loss': 32.610457}}
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2024-11-15 00:37:21,548 (client:354) INFO: {'Role': 'Client #4', 'Round': 55, 'Results_raw': {'train_loss': 34.64139, 'val_loss': 35.163853, 'test_loss': 34.708223}}
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2024-11-15 00:38:14,810 (client:354) INFO: {'Role': 'Client #8', 'Round': 55, 'Results_raw': {'train_loss': 31.271455, 'val_loss': 31.663869, 'test_loss': 32.973693}}
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2024-11-15 00:38:14,813 (server:615) INFO: {'Role': 'Server #', 'Round': 54, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.676981), 'test_loss': np.float64(223460.757251), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.214988), 'val_loss': np.float64(220858.811646), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.676981), 'test_loss': np.float64(223460.757251), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.214988), 'val_loss': np.float64(220858.811646), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.904156), 'test_avg_loss_bottom_decile': np.float64(38.05875), 'test_avg_loss_top_decile': np.float64(42.244581), 'test_avg_loss_min': np.float64(35.197273), 'test_avg_loss_max': np.float64(42.244581), 'test_avg_loss_bottom10%': np.float64(35.197273), 'test_avg_loss_top10%': np.float64(42.244581), 'test_avg_loss_cos1': np.float64(0.99885), 'test_avg_loss_entropy': np.float64(2.301414), 'test_loss_std': np.float64(10724.206178), 'test_loss_bottom_decile': np.float64(214346.882324), 'test_loss_top_decile': np.float64(237921.478882), 'test_loss_min': np.float64(198231.040771), 'test_loss_max': np.float64(237921.478882), 'test_loss_bottom10%': np.float64(198231.040771), 'test_loss_top10%': np.float64(237921.478882), 'test_loss_cos1': np.float64(0.99885), 'test_loss_entropy': np.float64(2.301414), 'val_avg_loss_std': np.float64(2.196546), 'val_avg_loss_bottom_decile': np.float64(37.843833), 'val_avg_loss_top_decile': np.float64(42.709572), 'val_avg_loss_min': np.float64(34.533719), 'val_avg_loss_max': np.float64(42.709572), 'val_avg_loss_bottom10%': np.float64(34.533719), 'val_avg_loss_top10%': np.float64(42.709572), 'val_avg_loss_cos1': np.float64(0.998435), 'val_avg_loss_entropy': np.float64(2.301002), 'val_loss_std': np.float64(12370.945863), 'val_loss_bottom_decile': np.float64(213136.465698), 'val_loss_top_decile': np.float64(240540.307007), 'val_loss_min': np.float64(194493.906494), 'val_loss_max': np.float64(240540.307007), 'val_loss_bottom10%': np.float64(194493.906494), 'val_loss_top10%': np.float64(240540.307007), 'val_loss_cos1': np.float64(0.998435), 'val_loss_entropy': np.float64(2.301002)}}
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2024-11-15 00:38:14,855 (server:353) INFO: Server: Starting evaluation at the end of round 55.
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2024-11-15 00:38:14,855 (server:359) INFO: ----------- Starting a new training round (Round #56) -------------
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2024-11-15 00:40:44,537 (client:354) INFO: {'Role': 'Client #5', 'Round': 56, 'Results_raw': {'train_loss': 30.628138, 'val_loss': 31.63587, 'test_loss': 33.131502}}
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2024-11-15 00:41:37,978 (client:354) INFO: {'Role': 'Client #10', 'Round': 56, 'Results_raw': {'train_loss': 31.307999, 'val_loss': 31.625481, 'test_loss': 32.608362}}
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2024-11-15 00:42:31,153 (client:354) INFO: {'Role': 'Client #7', 'Round': 56, 'Results_raw': {'train_loss': 30.120644, 'val_loss': 30.480988, 'test_loss': 31.252862}}
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2024-11-15 00:43:24,085 (client:354) INFO: {'Role': 'Client #8', 'Round': 56, 'Results_raw': {'train_loss': 31.328709, 'val_loss': 31.742826, 'test_loss': 32.990982}}
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2024-11-15 00:44:17,385 (client:354) INFO: {'Role': 'Client #9', 'Round': 56, 'Results_raw': {'train_loss': 33.369536, 'val_loss': 33.935319, 'test_loss': 35.633367}}
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2024-11-15 00:45:10,723 (client:354) INFO: {'Role': 'Client #4', 'Round': 56, 'Results_raw': {'train_loss': 34.627387, 'val_loss': 34.876039, 'test_loss': 34.328443}}
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2024-11-15 00:46:04,148 (client:354) INFO: {'Role': 'Client #3', 'Round': 56, 'Results_raw': {'train_loss': 30.378267, 'val_loss': 32.946205, 'test_loss': 33.667506}}
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2024-11-15 00:46:57,961 (client:354) INFO: {'Role': 'Client #1', 'Round': 56, 'Results_raw': {'train_loss': 31.648244, 'val_loss': 32.821359, 'test_loss': 33.834324}}
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2024-11-15 00:47:51,426 (client:354) INFO: {'Role': 'Client #2', 'Round': 56, 'Results_raw': {'train_loss': 27.087908, 'val_loss': 27.103479, 'test_loss': 28.094783}}
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2024-11-15 00:48:44,898 (client:354) INFO: {'Role': 'Client #6', 'Round': 56, 'Results_raw': {'train_loss': 32.320665, 'val_loss': 30.989125, 'test_loss': 36.020565}}
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2024-11-15 00:48:44,901 (server:615) INFO: {'Role': 'Server #', 'Round': 55, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.614776), 'test_loss': np.float64(223110.41897), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.143987), 'val_loss': np.float64(220458.9323), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.614776), 'test_loss': np.float64(223110.41897), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.143987), 'val_loss': np.float64(220458.9323), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.8835), 'test_avg_loss_bottom_decile': np.float64(38.101002), 'test_avg_loss_top_decile': np.float64(42.222809), 'test_avg_loss_min': np.float64(35.16627), 'test_avg_loss_max': np.float64(42.222809), 'test_avg_loss_bottom10%': np.float64(35.16627), 'test_avg_loss_top10%': np.float64(42.222809), 'test_avg_loss_cos1': np.float64(0.998872), 'test_avg_loss_entropy': np.float64(2.301436), 'test_loss_std': np.float64(10607.873636), 'test_loss_bottom_decile': np.float64(214584.844238), 'test_loss_top_decile': np.float64(237798.861938), 'test_loss_min': np.float64(198056.43042), 'test_loss_max': np.float64(237798.861938), 'test_loss_bottom10%': np.float64(198056.43042), 'test_loss_top10%': np.float64(237798.861938), 'test_loss_cos1': np.float64(0.998872), 'test_loss_entropy': np.float64(2.301436), 'val_avg_loss_std': np.float64(2.157465), 'val_avg_loss_bottom_decile': np.float64(37.852663), 'val_avg_loss_top_decile': np.float64(42.616785), 'val_avg_loss_min': np.float64(34.495703), 'val_avg_loss_max': np.float64(42.616785), 'val_avg_loss_bottom10%': np.float64(34.495703), 'val_avg_loss_top10%': np.float64(42.616785), 'val_avg_loss_cos1': np.float64(0.998485), 'val_avg_loss_entropy': np.float64(2.301052), 'val_loss_std': np.float64(12150.841906), 'val_loss_bottom_decile': np.float64(213186.199829), 'val_loss_top_decile': np.float64(240017.734375), 'val_loss_min': np.float64(194279.796997), 'val_loss_max': np.float64(240017.734375), 'val_loss_bottom10%': np.float64(194279.796997), 'val_loss_top10%': np.float64(240017.734375), 'val_loss_cos1': np.float64(0.998485), 'val_loss_entropy': np.float64(2.301052)}}
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2024-11-15 00:48:44,931 (server:353) INFO: Server: Starting evaluation at the end of round 56.
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2024-11-15 00:48:44,932 (server:359) INFO: ----------- Starting a new training round (Round #57) -------------
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2024-11-15 00:51:15,421 (client:354) INFO: {'Role': 'Client #2', 'Round': 57, 'Results_raw': {'train_loss': 27.044017, 'val_loss': 27.093546, 'test_loss': 28.090649}}
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2024-11-15 00:52:08,967 (client:354) INFO: {'Role': 'Client #10', 'Round': 57, 'Results_raw': {'train_loss': 31.33329, 'val_loss': 31.580596, 'test_loss': 32.445497}}
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2024-11-15 00:53:01,777 (client:354) INFO: {'Role': 'Client #5', 'Round': 57, 'Results_raw': {'train_loss': 30.564136, 'val_loss': 31.444597, 'test_loss': 32.699907}}
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2024-11-15 00:53:54,697 (client:354) INFO: {'Role': 'Client #7', 'Round': 57, 'Results_raw': {'train_loss': 30.082111, 'val_loss': 30.191183, 'test_loss': 31.027745}}
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2024-11-15 00:54:47,940 (client:354) INFO: {'Role': 'Client #4', 'Round': 57, 'Results_raw': {'train_loss': 34.656174, 'val_loss': 35.17157, 'test_loss': 34.300304}}
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2024-11-15 00:55:40,801 (client:354) INFO: {'Role': 'Client #6', 'Round': 57, 'Results_raw': {'train_loss': 32.345158, 'val_loss': 30.972592, 'test_loss': 34.720107}}
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2024-11-15 00:56:33,484 (client:354) INFO: {'Role': 'Client #8', 'Round': 57, 'Results_raw': {'train_loss': 31.25396, 'val_loss': 31.772781, 'test_loss': 32.852401}}
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2024-11-15 00:57:26,056 (client:354) INFO: {'Role': 'Client #1', 'Round': 57, 'Results_raw': {'train_loss': 31.639324, 'val_loss': 32.711178, 'test_loss': 33.662314}}
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2024-11-15 00:58:19,045 (client:354) INFO: {'Role': 'Client #9', 'Round': 57, 'Results_raw': {'train_loss': 33.379028, 'val_loss': 33.798877, 'test_loss': 35.545588}}
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2024-11-15 00:59:12,041 (client:354) INFO: {'Role': 'Client #3', 'Round': 57, 'Results_raw': {'train_loss': 30.340228, 'val_loss': 32.803646, 'test_loss': 33.802353}}
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2024-11-15 00:59:12,044 (server:615) INFO: {'Role': 'Server #', 'Round': 56, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.687653), 'test_loss': np.float64(223520.858984), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.207058), 'val_loss': np.float64(220814.153308), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.687653), 'test_loss': np.float64(223520.858984), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.207058), 'val_loss': np.float64(220814.153308), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.895443), 'test_avg_loss_bottom_decile': np.float64(38.210399), 'test_avg_loss_top_decile': np.float64(42.234099), 'test_avg_loss_min': np.float64(35.163933), 'test_avg_loss_max': np.float64(42.234099), 'test_avg_loss_bottom10%': np.float64(35.163933), 'test_avg_loss_top10%': np.float64(42.234099), 'test_avg_loss_cos1': np.float64(0.998861), 'test_avg_loss_entropy': np.float64(2.301424), 'test_loss_std': np.float64(10675.135562), 'test_loss_bottom_decile': np.float64(215200.966919), 'test_loss_top_decile': np.float64(237862.447876), 'test_loss_min': np.float64(198043.272095), 'test_loss_max': np.float64(237862.447876), 'test_loss_bottom10%': np.float64(198043.272095), 'test_loss_top10%': np.float64(237862.447876), 'test_loss_cos1': np.float64(0.998861), 'test_loss_entropy': np.float64(2.301424), 'val_avg_loss_std': np.float64(2.184165), 'val_avg_loss_bottom_decile': np.float64(37.990336), 'val_avg_loss_top_decile': np.float64(42.639241), 'val_avg_loss_min': np.float64(34.503284), 'val_avg_loss_max': np.float64(42.639241), 'val_avg_loss_bottom10%': np.float64(34.503284), 'val_avg_loss_top10%': np.float64(42.639241), 'val_avg_loss_cos1': np.float64(0.998452), 'val_avg_loss_entropy': np.float64(2.301018), 'val_loss_std': np.float64(12301.218586), 'val_loss_bottom_decile': np.float64(213961.57373), 'val_loss_top_decile': np.float64(240144.20752), 'val_loss_min': np.float64(194322.495117), 'val_loss_max': np.float64(240144.20752), 'val_loss_bottom10%': np.float64(194322.495117), 'val_loss_top10%': np.float64(240144.20752), 'val_loss_cos1': np.float64(0.998452), 'val_loss_entropy': np.float64(2.301018)}}
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2024-11-15 00:59:12,076 (server:353) INFO: Server: Starting evaluation at the end of round 57.
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2024-11-15 00:59:12,076 (server:359) INFO: ----------- Starting a new training round (Round #58) -------------
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2024-11-15 01:01:54,597 (client:354) INFO: {'Role': 'Client #6', 'Round': 58, 'Results_raw': {'train_loss': 32.251404, 'val_loss': 31.020129, 'test_loss': 34.206781}}
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2024-11-15 01:02:49,455 (client:354) INFO: {'Role': 'Client #8', 'Round': 58, 'Results_raw': {'train_loss': 31.252889, 'val_loss': 31.571924, 'test_loss': 32.773802}}
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2024-11-15 01:03:44,431 (client:354) INFO: {'Role': 'Client #10', 'Round': 58, 'Results_raw': {'train_loss': 31.337138, 'val_loss': 31.778577, 'test_loss': 32.646104}}
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2024-11-15 01:04:39,817 (client:354) INFO: {'Role': 'Client #9', 'Round': 58, 'Results_raw': {'train_loss': 33.385783, 'val_loss': 33.531099, 'test_loss': 35.265912}}
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2024-11-15 01:05:37,794 (client:354) INFO: {'Role': 'Client #4', 'Round': 58, 'Results_raw': {'train_loss': 34.655761, 'val_loss': 35.157538, 'test_loss': 34.520917}}
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2024-11-15 01:06:31,239 (client:354) INFO: {'Role': 'Client #2', 'Round': 58, 'Results_raw': {'train_loss': 27.054028, 'val_loss': 27.125798, 'test_loss': 28.097746}}
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2024-11-15 01:07:26,274 (client:354) INFO: {'Role': 'Client #3', 'Round': 58, 'Results_raw': {'train_loss': 30.277831, 'val_loss': 32.798415, 'test_loss': 33.53862}}
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2024-11-15 01:08:22,269 (client:354) INFO: {'Role': 'Client #5', 'Round': 58, 'Results_raw': {'train_loss': 30.562525, 'val_loss': 31.561249, 'test_loss': 32.712572}}
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2024-11-15 01:09:19,158 (client:354) INFO: {'Role': 'Client #7', 'Round': 58, 'Results_raw': {'train_loss': 30.082577, 'val_loss': 30.000982, 'test_loss': 30.842851}}
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2024-11-15 01:10:13,037 (client:354) INFO: {'Role': 'Client #1', 'Round': 58, 'Results_raw': {'train_loss': 31.607987, 'val_loss': 32.7829, 'test_loss': 33.888022}}
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2024-11-15 01:10:13,040 (server:615) INFO: {'Role': 'Server #', 'Round': 57, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.643377), 'test_loss': np.float64(223271.50199), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.157447), 'val_loss': np.float64(220534.739209), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.643377), 'test_loss': np.float64(223271.50199), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.157447), 'val_loss': np.float64(220534.739209), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.898527), 'test_avg_loss_bottom_decile': np.float64(38.102179), 'test_avg_loss_top_decile': np.float64(42.176269), 'test_avg_loss_min': np.float64(35.148546), 'test_avg_loss_max': np.float64(42.176269), 'test_avg_loss_bottom10%': np.float64(35.148546), 'test_avg_loss_top10%': np.float64(42.176269), 'test_avg_loss_cos1': np.float64(0.998855), 'test_avg_loss_entropy': np.float64(2.301418), 'test_loss_std': np.float64(10692.505588), 'test_loss_bottom_decile': np.float64(214591.474854), 'test_loss_top_decile': np.float64(237536.748047), 'test_loss_min': np.float64(197956.608887), 'test_loss_max': np.float64(237536.748047), 'test_loss_bottom10%': np.float64(197956.608887), 'test_loss_top10%': np.float64(237536.748047), 'test_loss_cos1': np.float64(0.998855), 'test_loss_entropy': np.float64(2.301418), 'val_avg_loss_std': np.float64(2.177115), 'val_avg_loss_bottom_decile': np.float64(37.883224), 'val_avg_loss_top_decile': np.float64(42.671168), 'val_avg_loss_min': np.float64(34.471911), 'val_avg_loss_max': np.float64(42.671168), 'val_avg_loss_bottom10%': np.float64(34.471911), 'val_avg_loss_top10%': np.float64(42.671168), 'val_avg_loss_cos1': np.float64(0.998458), 'val_avg_loss_entropy': np.float64(2.301025), 'val_loss_std': np.float64(12261.511855), 'val_loss_bottom_decile': np.float64(213358.318481), 'val_loss_top_decile': np.float64(240324.01709), 'val_loss_min': np.float64(194145.803955), 'val_loss_max': np.float64(240324.01709), 'val_loss_bottom10%': np.float64(194145.803955), 'val_loss_top10%': np.float64(240324.01709), 'val_loss_cos1': np.float64(0.998458), 'val_loss_entropy': np.float64(2.301025)}}
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2024-11-15 01:10:13,068 (server:353) INFO: Server: Starting evaluation at the end of round 58.
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2024-11-15 01:10:13,069 (server:359) INFO: ----------- Starting a new training round (Round #59) -------------
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2024-11-15 01:12:41,985 (client:354) INFO: {'Role': 'Client #1', 'Round': 59, 'Results_raw': {'train_loss': 31.576442, 'val_loss': 32.691453, 'test_loss': 33.706838}}
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2024-11-15 01:13:34,666 (client:354) INFO: {'Role': 'Client #10', 'Round': 59, 'Results_raw': {'train_loss': 31.304612, 'val_loss': 31.935498, 'test_loss': 32.693274}}
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2024-11-15 01:14:27,578 (client:354) INFO: {'Role': 'Client #5', 'Round': 59, 'Results_raw': {'train_loss': 30.544574, 'val_loss': 31.480154, 'test_loss': 32.907907}}
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2024-11-15 01:15:20,627 (client:354) INFO: {'Role': 'Client #3', 'Round': 59, 'Results_raw': {'train_loss': 30.286959, 'val_loss': 33.169238, 'test_loss': 33.842443}}
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2024-11-15 01:16:14,170 (client:354) INFO: {'Role': 'Client #8', 'Round': 59, 'Results_raw': {'train_loss': 31.257715, 'val_loss': 31.539285, 'test_loss': 32.685076}}
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2024-11-15 01:17:07,602 (client:354) INFO: {'Role': 'Client #2', 'Round': 59, 'Results_raw': {'train_loss': 27.068999, 'val_loss': 27.21711, 'test_loss': 28.288721}}
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2024-11-15 01:18:01,316 (client:354) INFO: {'Role': 'Client #6', 'Round': 59, 'Results_raw': {'train_loss': 32.356068, 'val_loss': 30.997212, 'test_loss': 34.589471}}
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2024-11-15 01:18:54,993 (client:354) INFO: {'Role': 'Client #7', 'Round': 59, 'Results_raw': {'train_loss': 30.074633, 'val_loss': 30.091183, 'test_loss': 30.901029}}
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2024-11-15 01:19:48,425 (client:354) INFO: {'Role': 'Client #9', 'Round': 59, 'Results_raw': {'train_loss': 33.351067, 'val_loss': 33.625674, 'test_loss': 35.380295}}
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2024-11-15 01:20:42,475 (client:354) INFO: {'Role': 'Client #4', 'Round': 59, 'Results_raw': {'train_loss': 34.566262, 'val_loss': 35.049322, 'test_loss': 34.592254}}
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2024-11-15 01:20:42,478 (server:615) INFO: {'Role': 'Server #', 'Round': 58, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.601222), 'test_loss': np.float64(223034.084595), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.131502), 'val_loss': np.float64(220388.617078), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.601222), 'test_loss': np.float64(223034.084595), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.131502), 'val_loss': np.float64(220388.617078), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.907386), 'test_avg_loss_bottom_decile': np.float64(38.041829), 'test_avg_loss_top_decile': np.float64(42.15779), 'test_avg_loss_min': np.float64(35.065524), 'test_avg_loss_max': np.float64(42.15779), 'test_avg_loss_bottom10%': np.float64(35.065524), 'test_avg_loss_top10%': np.float64(42.15779), 'test_avg_loss_cos1': np.float64(0.998842), 'test_avg_loss_entropy': np.float64(2.301405), 'test_loss_std': np.float64(10742.395777), 'test_loss_bottom_decile': np.float64(214251.583008), 'test_loss_top_decile': np.float64(237432.673828), 'test_loss_min': np.float64(197489.029663), 'test_loss_max': np.float64(237432.673828), 'test_loss_bottom10%': np.float64(197489.029663), 'test_loss_top10%': np.float64(237432.673828), 'test_loss_cos1': np.float64(0.998842), 'test_loss_entropy': np.float64(2.301405), 'val_avg_loss_std': np.float64(2.175601), 'val_avg_loss_bottom_decile': np.float64(37.805443), 'val_avg_loss_top_decile': np.float64(42.674741), 'val_avg_loss_min': np.float64(34.426992), 'val_avg_loss_max': np.float64(42.674741), 'val_avg_loss_bottom10%': np.float64(34.426992), 'val_avg_loss_top10%': np.float64(42.674741), 'val_avg_loss_cos1': np.float64(0.998458), 'val_avg_loss_entropy': np.float64(2.301024), 'val_loss_std': np.float64(12252.985022), 'val_loss_bottom_decile': np.float64(212920.252686), 'val_loss_top_decile': np.float64(240344.138916), 'val_loss_min': np.float64(193892.81665), 'val_loss_max': np.float64(240344.138916), 'val_loss_bottom10%': np.float64(193892.81665), 'val_loss_top10%': np.float64(240344.138916), 'val_loss_cos1': np.float64(0.998458), 'val_loss_entropy': np.float64(2.301024)}}
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2024-11-15 01:20:42,512 (server:353) INFO: Server: Starting evaluation at the end of round 59.
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2024-11-15 01:20:42,513 (server:359) INFO: ----------- Starting a new training round (Round #60) -------------
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2024-11-15 01:23:16,591 (client:354) INFO: {'Role': 'Client #4', 'Round': 60, 'Results_raw': {'train_loss': 34.514303, 'val_loss': 35.377438, 'test_loss': 34.82575}}
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2024-11-15 01:24:10,513 (client:354) INFO: {'Role': 'Client #10', 'Round': 60, 'Results_raw': {'train_loss': 31.276486, 'val_loss': 31.76167, 'test_loss': 32.703131}}
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2024-11-15 01:25:04,463 (client:354) INFO: {'Role': 'Client #1', 'Round': 60, 'Results_raw': {'train_loss': 31.543653, 'val_loss': 32.87734, 'test_loss': 33.956123}}
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2024-11-15 01:25:57,638 (client:354) INFO: {'Role': 'Client #9', 'Round': 60, 'Results_raw': {'train_loss': 33.329697, 'val_loss': 33.462498, 'test_loss': 35.15256}}
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2024-11-15 01:26:51,406 (client:354) INFO: {'Role': 'Client #5', 'Round': 60, 'Results_raw': {'train_loss': 30.485035, 'val_loss': 31.729639, 'test_loss': 33.16605}}
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2024-11-15 01:27:44,794 (client:354) INFO: {'Role': 'Client #3', 'Round': 60, 'Results_raw': {'train_loss': 30.22525, 'val_loss': 32.680281, 'test_loss': 33.44001}}
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2024-11-15 01:28:38,277 (client:354) INFO: {'Role': 'Client #7', 'Round': 60, 'Results_raw': {'train_loss': 30.057732, 'val_loss': 30.333396, 'test_loss': 31.442475}}
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2024-11-15 01:29:31,765 (client:354) INFO: {'Role': 'Client #2', 'Round': 60, 'Results_raw': {'train_loss': 27.061821, 'val_loss': 27.179249, 'test_loss': 28.049661}}
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2024-11-15 01:30:25,081 (client:354) INFO: {'Role': 'Client #6', 'Round': 60, 'Results_raw': {'train_loss': 32.323789, 'val_loss': 30.845098, 'test_loss': 34.10688}}
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2024-11-15 01:31:18,587 (client:354) INFO: {'Role': 'Client #8', 'Round': 60, 'Results_raw': {'train_loss': 31.179797, 'val_loss': 31.484287, 'test_loss': 32.594628}}
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2024-11-15 01:31:18,590 (server:615) INFO: {'Role': 'Server #', 'Round': 59, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.630108), 'test_loss': np.float64(223196.769922), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.165387), 'val_loss': np.float64(220579.45769), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.630108), 'test_loss': np.float64(223196.769922), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.165387), 'val_loss': np.float64(220579.45769), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.912261), 'test_avg_loss_bottom_decile': np.float64(38.048009), 'test_avg_loss_top_decile': np.float64(42.176504), 'test_avg_loss_min': np.float64(35.091181), 'test_avg_loss_max': np.float64(42.176504), 'test_avg_loss_bottom10%': np.float64(35.091181), 'test_avg_loss_top10%': np.float64(42.176504), 'test_avg_loss_cos1': np.float64(0.998838), 'test_avg_loss_entropy': np.float64(2.3014), 'test_loss_std': np.float64(10769.852817), 'test_loss_bottom_decile': np.float64(214286.384766), 'test_loss_top_decile': np.float64(237538.070557), 'test_loss_min': np.float64(197633.528687), 'test_loss_max': np.float64(237538.070557), 'test_loss_bottom10%': np.float64(197633.528687), 'test_loss_top10%': np.float64(237538.070557), 'test_loss_cos1': np.float64(0.998838), 'test_loss_entropy': np.float64(2.3014), 'val_avg_loss_std': np.float64(2.184571), 'val_avg_loss_bottom_decile': np.float64(37.821049), 'val_avg_loss_top_decile': np.float64(42.680791), 'val_avg_loss_min': np.float64(34.45571), 'val_avg_loss_max': np.float64(42.680791), 'val_avg_loss_bottom10%': np.float64(34.45571), 'val_avg_loss_top10%': np.float64(42.680791), 'val_avg_loss_cos1': np.float64(0.998448), 'val_avg_loss_entropy': np.float64(2.301014), 'val_loss_std': np.float64(12303.504346), 'val_loss_bottom_decile': np.float64(213008.145752), 'val_loss_top_decile': np.float64(240378.213867), 'val_loss_min': np.float64(194054.558838), 'val_loss_max': np.float64(240378.213867), 'val_loss_bottom10%': np.float64(194054.558838), 'val_loss_top10%': np.float64(240378.213867), 'val_loss_cos1': np.float64(0.998448), 'val_loss_entropy': np.float64(2.301014)}}
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2024-11-15 01:31:18,618 (server:353) INFO: Server: Starting evaluation at the end of round 60.
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2024-11-15 01:31:18,618 (server:359) INFO: ----------- Starting a new training round (Round #61) -------------
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2024-11-15 01:33:53,496 (client:354) INFO: {'Role': 'Client #6', 'Round': 61, 'Results_raw': {'train_loss': 32.295537, 'val_loss': 31.143171, 'test_loss': 36.078062}}
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2024-11-15 01:34:49,392 (client:354) INFO: {'Role': 'Client #2', 'Round': 61, 'Results_raw': {'train_loss': 26.98917, 'val_loss': 27.057666, 'test_loss': 28.171432}}
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2024-11-15 01:35:44,622 (client:354) INFO: {'Role': 'Client #8', 'Round': 61, 'Results_raw': {'train_loss': 31.204415, 'val_loss': 31.562595, 'test_loss': 32.852588}}
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2024-11-15 01:36:39,466 (client:354) INFO: {'Role': 'Client #4', 'Round': 61, 'Results_raw': {'train_loss': 34.616724, 'val_loss': 35.36775, 'test_loss': 34.854248}}
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2024-11-15 01:37:35,073 (client:354) INFO: {'Role': 'Client #5', 'Round': 61, 'Results_raw': {'train_loss': 30.496802, 'val_loss': 31.602832, 'test_loss': 32.937117}}
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2024-11-15 01:38:29,362 (client:354) INFO: {'Role': 'Client #7', 'Round': 61, 'Results_raw': {'train_loss': 30.032052, 'val_loss': 30.127336, 'test_loss': 30.933064}}
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2024-11-15 01:39:23,036 (client:354) INFO: {'Role': 'Client #9', 'Round': 61, 'Results_raw': {'train_loss': 33.286385, 'val_loss': 33.841207, 'test_loss': 35.532998}}
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2024-11-15 01:40:16,563 (client:354) INFO: {'Role': 'Client #10', 'Round': 61, 'Results_raw': {'train_loss': 31.272654, 'val_loss': 31.947207, 'test_loss': 32.650175}}
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2024-11-15 01:41:09,993 (client:354) INFO: {'Role': 'Client #1', 'Round': 61, 'Results_raw': {'train_loss': 31.574803, 'val_loss': 32.578661, 'test_loss': 33.788354}}
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2024-11-15 01:42:03,744 (client:354) INFO: {'Role': 'Client #3', 'Round': 61, 'Results_raw': {'train_loss': 30.250109, 'val_loss': 32.982365, 'test_loss': 33.556207}}
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2024-11-15 01:42:03,749 (server:615) INFO: {'Role': 'Server #', 'Round': 60, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.697414), 'test_loss': np.float64(223575.833142), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.234634), 'val_loss': np.float64(220969.460315), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.697414), 'test_loss': np.float64(223575.833142), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.234634), 'val_loss': np.float64(220969.460315), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.919957), 'test_avg_loss_bottom_decile': np.float64(38.082713), 'test_avg_loss_top_decile': np.float64(42.312876), 'test_avg_loss_min': np.float64(35.210472), 'test_avg_loss_max': np.float64(42.312876), 'test_avg_loss_bottom10%': np.float64(35.210472), 'test_avg_loss_top10%': np.float64(42.312876), 'test_avg_loss_cos1': np.float64(0.998832), 'test_avg_loss_entropy': np.float64(2.301396), 'test_loss_std': np.float64(10813.199241), 'test_loss_bottom_decile': np.float64(214481.839111), 'test_loss_top_decile': np.float64(238306.115723), 'test_loss_min': np.float64(198305.378906), 'test_loss_max': np.float64(238306.115723), 'test_loss_bottom10%': np.float64(198305.378906), 'test_loss_top10%': np.float64(238306.115723), 'test_loss_cos1': np.float64(0.998832), 'test_loss_entropy': np.float64(2.301396), 'val_avg_loss_std': np.float64(2.204688), 'val_avg_loss_bottom_decile': np.float64(37.873565), 'val_avg_loss_top_decile': np.float64(42.746924), 'val_avg_loss_min': np.float64(34.545695), 'val_avg_loss_max': np.float64(42.746924), 'val_avg_loss_bottom10%': np.float64(34.545695), 'val_avg_loss_top10%': np.float64(42.746924), 'val_avg_loss_cos1': np.float64(0.998425), 'val_avg_loss_entropy': np.float64(2.300992), 'val_loss_std': np.float64(12416.804934), 'val_loss_bottom_decile': np.float64(213303.919312), 'val_loss_top_decile': np.float64(240750.678223), 'val_loss_min': np.float64(194561.352051), 'val_loss_max': np.float64(240750.678223), 'val_loss_bottom10%': np.float64(194561.352051), 'val_loss_top10%': np.float64(240750.678223), 'val_loss_cos1': np.float64(0.998425), 'val_loss_entropy': np.float64(2.300992)}}
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2024-11-15 01:42:03,789 (server:353) INFO: Server: Starting evaluation at the end of round 61.
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2024-11-15 01:42:03,789 (server:359) INFO: ----------- Starting a new training round (Round #62) -------------
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2024-11-15 01:44:35,826 (client:354) INFO: {'Role': 'Client #4', 'Round': 62, 'Results_raw': {'train_loss': 34.569293, 'val_loss': 35.326683, 'test_loss': 34.743977}}
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2024-11-15 01:45:28,820 (client:354) INFO: {'Role': 'Client #8', 'Round': 62, 'Results_raw': {'train_loss': 31.14844, 'val_loss': 31.641961, 'test_loss': 32.903504}}
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2024-11-15 01:46:21,599 (client:354) INFO: {'Role': 'Client #3', 'Round': 62, 'Results_raw': {'train_loss': 30.17795, 'val_loss': 32.875832, 'test_loss': 33.724943}}
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2024-11-15 01:47:13,768 (client:354) INFO: {'Role': 'Client #10', 'Round': 62, 'Results_raw': {'train_loss': 31.244656, 'val_loss': 31.615476, 'test_loss': 32.811726}}
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2024-11-15 01:48:07,013 (client:354) INFO: {'Role': 'Client #2', 'Round': 62, 'Results_raw': {'train_loss': 27.052161, 'val_loss': 27.283851, 'test_loss': 28.60334}}
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2024-11-15 01:48:59,899 (client:354) INFO: {'Role': 'Client #6', 'Round': 62, 'Results_raw': {'train_loss': 32.234973, 'val_loss': 31.087591, 'test_loss': 34.957297}}
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2024-11-15 01:49:52,996 (client:354) INFO: {'Role': 'Client #9', 'Round': 62, 'Results_raw': {'train_loss': 33.284988, 'val_loss': 33.421765, 'test_loss': 34.988531}}
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2024-11-15 01:50:46,940 (client:354) INFO: {'Role': 'Client #5', 'Round': 62, 'Results_raw': {'train_loss': 30.508906, 'val_loss': 31.718895, 'test_loss': 33.038565}}
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2024-11-15 01:51:40,713 (client:354) INFO: {'Role': 'Client #7', 'Round': 62, 'Results_raw': {'train_loss': 29.994474, 'val_loss': 30.122216, 'test_loss': 30.942751}}
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2024-11-15 01:52:34,291 (client:354) INFO: {'Role': 'Client #1', 'Round': 62, 'Results_raw': {'train_loss': 31.510072, 'val_loss': 32.640052, 'test_loss': 33.875147}}
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2024-11-15 01:52:34,293 (server:615) INFO: {'Role': 'Server #', 'Round': 61, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.630091), 'test_loss': np.float64(223196.670166), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.168963), 'val_loss': np.float64(220599.597815), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.630091), 'test_loss': np.float64(223196.670166), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.168963), 'val_loss': np.float64(220599.597815), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.932122), 'test_avg_loss_bottom_decile': np.float64(38.021482), 'test_avg_loss_top_decile': np.float64(42.216869), 'test_avg_loss_min': np.float64(35.061251), 'test_avg_loss_max': np.float64(42.216869), 'test_avg_loss_bottom10%': np.float64(35.061251), 'test_avg_loss_top10%': np.float64(42.216869), 'test_avg_loss_cos1': np.float64(0.998814), 'test_avg_loss_entropy': np.float64(2.301375), 'test_loss_std': np.float64(10881.711222), 'test_loss_bottom_decile': np.float64(214136.984497), 'test_loss_top_decile': np.float64(237765.408081), 'test_loss_min': np.float64(197464.964722), 'test_loss_max': np.float64(237765.408081), 'test_loss_bottom10%': np.float64(197464.964722), 'test_loss_top10%': np.float64(237765.408081), 'test_loss_cos1': np.float64(0.998814), 'test_loss_entropy': np.float64(2.301375), 'val_avg_loss_std': np.float64(2.22487), 'val_avg_loss_bottom_decile': np.float64(37.808024), 'val_avg_loss_top_decile': np.float64(42.698925), 'val_avg_loss_min': np.float64(34.408694), 'val_avg_loss_max': np.float64(42.698925), 'val_avg_loss_bottom10%': np.float64(34.408694), 'val_avg_loss_top10%': np.float64(42.698925), 'val_avg_loss_cos1': np.float64(0.998391), 'val_avg_loss_entropy': np.float64(2.300956), 'val_loss_std': np.float64(12530.468576), 'val_loss_bottom_decile': np.float64(212934.792969), 'val_loss_top_decile': np.float64(240480.34375), 'val_loss_min': np.float64(193789.764526), 'val_loss_max': np.float64(240480.34375), 'val_loss_bottom10%': np.float64(193789.764526), 'val_loss_top10%': np.float64(240480.34375), 'val_loss_cos1': np.float64(0.998391), 'val_loss_entropy': np.float64(2.300956)}}
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2024-11-15 01:52:34,321 (server:353) INFO: Server: Starting evaluation at the end of round 62.
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2024-11-15 01:52:34,322 (server:359) INFO: ----------- Starting a new training round (Round #63) -------------
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2024-11-15 01:55:03,253 (client:354) INFO: {'Role': 'Client #8', 'Round': 63, 'Results_raw': {'train_loss': 31.201806, 'val_loss': 31.696468, 'test_loss': 33.045517}}
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2024-11-15 01:55:57,027 (client:354) INFO: {'Role': 'Client #3', 'Round': 63, 'Results_raw': {'train_loss': 30.197264, 'val_loss': 33.118607, 'test_loss': 33.750017}}
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2024-11-15 01:56:49,596 (client:354) INFO: {'Role': 'Client #10', 'Round': 63, 'Results_raw': {'train_loss': 31.223019, 'val_loss': 31.67675, 'test_loss': 32.828952}}
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2024-11-15 01:57:42,364 (client:354) INFO: {'Role': 'Client #1', 'Round': 63, 'Results_raw': {'train_loss': 31.501914, 'val_loss': 32.520033, 'test_loss': 33.534486}}
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2024-11-15 01:58:35,100 (client:354) INFO: {'Role': 'Client #9', 'Round': 63, 'Results_raw': {'train_loss': 33.243452, 'val_loss': 33.905269, 'test_loss': 35.414916}}
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2024-11-15 01:59:28,436 (client:354) INFO: {'Role': 'Client #7', 'Round': 63, 'Results_raw': {'train_loss': 29.963271, 'val_loss': 30.096649, 'test_loss': 31.131023}}
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2024-11-15 02:00:21,465 (client:354) INFO: {'Role': 'Client #4', 'Round': 63, 'Results_raw': {'train_loss': 34.542837, 'val_loss': 34.952123, 'test_loss': 34.44609}}
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2024-11-15 02:01:14,667 (client:354) INFO: {'Role': 'Client #5', 'Round': 63, 'Results_raw': {'train_loss': 30.508017, 'val_loss': 31.682777, 'test_loss': 32.990594}}
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2024-11-15 02:02:07,868 (client:354) INFO: {'Role': 'Client #2', 'Round': 63, 'Results_raw': {'train_loss': 26.955173, 'val_loss': 27.070734, 'test_loss': 28.041384}}
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2024-11-15 02:03:00,934 (client:354) INFO: {'Role': 'Client #6', 'Round': 63, 'Results_raw': {'train_loss': 32.206088, 'val_loss': 31.206024, 'test_loss': 34.432376}}
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2024-11-15 02:03:00,936 (server:615) INFO: {'Role': 'Server #', 'Round': 62, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.677622), 'test_loss': np.float64(223464.367419), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.200503), 'val_loss': np.float64(220777.23125), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.677622), 'test_loss': np.float64(223464.367419), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.200503), 'val_loss': np.float64(220777.23125), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.932789), 'test_avg_loss_bottom_decile': np.float64(38.073272), 'test_avg_loss_top_decile': np.float64(42.314263), 'test_avg_loss_min': np.float64(35.118583), 'test_avg_loss_max': np.float64(42.314263), 'test_avg_loss_bottom10%': np.float64(35.118583), 'test_avg_loss_top10%': np.float64(42.314263), 'test_avg_loss_cos1': np.float64(0.998816), 'test_avg_loss_entropy': np.float64(2.301378), 'test_loss_std': np.float64(10885.468502), 'test_loss_bottom_decile': np.float64(214428.667725), 'test_loss_top_decile': np.float64(238313.92688), 'test_loss_min': np.float64(197787.858887), 'test_loss_max': np.float64(238313.92688), 'test_loss_bottom10%': np.float64(197787.858887), 'test_loss_top10%': np.float64(238313.92688), 'test_loss_cos1': np.float64(0.998816), 'test_loss_entropy': np.float64(2.301378), 'val_avg_loss_std': np.float64(2.193996), 'val_avg_loss_bottom_decile': np.float64(37.834299), 'val_avg_loss_top_decile': np.float64(42.728506), 'val_avg_loss_min': np.float64(34.454141), 'val_avg_loss_max': np.float64(42.728506), 'val_avg_loss_bottom10%': np.float64(34.454141), 'val_avg_loss_top10%': np.float64(42.728506), 'val_avg_loss_cos1': np.float64(0.998437), 'val_avg_loss_entropy': np.float64(2.301003), 'val_loss_std': np.float64(12356.585093), 'val_loss_bottom_decile': np.float64(213082.771362), 'val_loss_top_decile': np.float64(240646.944214), 'val_loss_min': np.float64(194045.720703), 'val_loss_max': np.float64(240646.944214), 'val_loss_bottom10%': np.float64(194045.720703), 'val_loss_top10%': np.float64(240646.944214), 'val_loss_cos1': np.float64(0.998437), 'val_loss_entropy': np.float64(2.301003)}}
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2024-11-15 02:03:00,965 (server:353) INFO: Server: Starting evaluation at the end of round 63.
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2024-11-15 02:03:00,965 (server:359) INFO: ----------- Starting a new training round (Round #64) -------------
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2024-11-15 02:05:32,652 (client:354) INFO: {'Role': 'Client #10', 'Round': 64, 'Results_raw': {'train_loss': 31.225373, 'val_loss': 31.944519, 'test_loss': 32.844199}}
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2024-11-15 02:06:27,566 (client:354) INFO: {'Role': 'Client #9', 'Round': 64, 'Results_raw': {'train_loss': 33.21428, 'val_loss': 33.668193, 'test_loss': 35.41297}}
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2024-11-15 02:07:22,965 (client:354) INFO: {'Role': 'Client #1', 'Round': 64, 'Results_raw': {'train_loss': 31.471532, 'val_loss': 32.647819, 'test_loss': 33.740699}}
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2024-11-15 02:08:18,235 (client:354) INFO: {'Role': 'Client #5', 'Round': 64, 'Results_raw': {'train_loss': 30.44876, 'val_loss': 31.535442, 'test_loss': 32.930696}}
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2024-11-15 02:09:12,641 (client:354) INFO: {'Role': 'Client #2', 'Round': 64, 'Results_raw': {'train_loss': 26.950423, 'val_loss': 26.953204, 'test_loss': 28.156228}}
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2024-11-15 02:10:05,936 (client:354) INFO: {'Role': 'Client #7', 'Round': 64, 'Results_raw': {'train_loss': 29.954444, 'val_loss': 30.268429, 'test_loss': 31.056169}}
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2024-11-15 02:10:59,261 (client:354) INFO: {'Role': 'Client #6', 'Round': 64, 'Results_raw': {'train_loss': 32.283307, 'val_loss': 31.365106, 'test_loss': 35.95885}}
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2024-11-15 02:11:52,641 (client:354) INFO: {'Role': 'Client #8', 'Round': 64, 'Results_raw': {'train_loss': 31.176625, 'val_loss': 31.725801, 'test_loss': 33.050125}}
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2024-11-15 02:12:46,020 (client:354) INFO: {'Role': 'Client #3', 'Round': 64, 'Results_raw': {'train_loss': 30.209414, 'val_loss': 32.956797, 'test_loss': 34.063994}}
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2024-11-15 02:13:39,136 (client:354) INFO: {'Role': 'Client #4', 'Round': 64, 'Results_raw': {'train_loss': 34.465718, 'val_loss': 34.953896, 'test_loss': 34.33054}}
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2024-11-15 02:13:39,138 (server:615) INFO: {'Role': 'Server #', 'Round': 63, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.577514), 'test_loss': np.float64(222900.561389), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.133093), 'val_loss': np.float64(220397.582117), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.577514), 'test_loss': np.float64(222900.561389), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.133093), 'val_loss': np.float64(220397.582117), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.914596), 'test_avg_loss_bottom_decile': np.float64(38.069696), 'test_avg_loss_top_decile': np.float64(42.162566), 'test_avg_loss_min': np.float64(34.996164), 'test_avg_loss_max': np.float64(42.162566), 'test_avg_loss_bottom10%': np.float64(34.996164), 'test_avg_loss_top10%': np.float64(42.162566), 'test_avg_loss_cos1': np.float64(0.998832), 'test_avg_loss_entropy': np.float64(2.301394), 'test_loss_std': np.float64(10783.007283), 'test_loss_bottom_decile': np.float64(214408.528687), 'test_loss_top_decile': np.float64(237459.56897), 'test_loss_min': np.float64(197098.395142), 'test_loss_max': np.float64(237459.56897), 'test_loss_bottom10%': np.float64(197098.395142), 'test_loss_top10%': np.float64(237459.56897), 'test_loss_cos1': np.float64(0.998832), 'test_loss_entropy': np.float64(2.301394), 'val_avg_loss_std': np.float64(2.209268), 'val_avg_loss_bottom_decile': np.float64(37.826592), 'val_avg_loss_top_decile': np.float64(42.563165), 'val_avg_loss_min': np.float64(34.36277), 'val_avg_loss_max': np.float64(42.563165), 'val_avg_loss_bottom10%': np.float64(34.36277), 'val_avg_loss_top10%': np.float64(42.563165), 'val_avg_loss_cos1': np.float64(0.99841), 'val_avg_loss_entropy': np.float64(2.300975), 'val_loss_std': np.float64(12442.595202), 'val_loss_bottom_decile': np.float64(213039.368164), 'val_loss_top_decile': np.float64(239715.74707), 'val_loss_min': np.float64(193531.121582), 'val_loss_max': np.float64(239715.74707), 'val_loss_bottom10%': np.float64(193531.121582), 'val_loss_top10%': np.float64(239715.74707), 'val_loss_cos1': np.float64(0.99841), 'val_loss_entropy': np.float64(2.300975)}}
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2024-11-15 02:13:39,167 (server:353) INFO: Server: Starting evaluation at the end of round 64.
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2024-11-15 02:13:39,167 (server:359) INFO: ----------- Starting a new training round (Round #65) -------------
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2024-11-15 02:16:09,898 (client:354) INFO: {'Role': 'Client #2', 'Round': 65, 'Results_raw': {'train_loss': 26.946594, 'val_loss': 27.221296, 'test_loss': 28.303949}}
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2024-11-15 02:17:03,479 (client:354) INFO: {'Role': 'Client #5', 'Round': 65, 'Results_raw': {'train_loss': 30.455569, 'val_loss': 31.667518, 'test_loss': 33.055864}}
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2024-11-15 02:17:56,731 (client:354) INFO: {'Role': 'Client #10', 'Round': 65, 'Results_raw': {'train_loss': 31.222818, 'val_loss': 31.824912, 'test_loss': 32.819174}}
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2024-11-15 02:18:49,619 (client:354) INFO: {'Role': 'Client #1', 'Round': 65, 'Results_raw': {'train_loss': 31.483792, 'val_loss': 32.905293, 'test_loss': 33.860663}}
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2024-11-15 02:19:43,171 (client:354) INFO: {'Role': 'Client #7', 'Round': 65, 'Results_raw': {'train_loss': 29.98228, 'val_loss': 30.283551, 'test_loss': 31.15483}}
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2024-11-15 02:20:36,418 (client:354) INFO: {'Role': 'Client #3', 'Round': 65, 'Results_raw': {'train_loss': 30.119808, 'val_loss': 32.761193, 'test_loss': 33.514845}}
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2024-11-15 02:21:29,586 (client:354) INFO: {'Role': 'Client #9', 'Round': 65, 'Results_raw': {'train_loss': 33.27336, 'val_loss': 33.682953, 'test_loss': 35.371143}}
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2024-11-15 02:22:22,959 (client:354) INFO: {'Role': 'Client #8', 'Round': 65, 'Results_raw': {'train_loss': 31.145544, 'val_loss': 31.623358, 'test_loss': 32.924967}}
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2024-11-15 02:23:17,355 (client:354) INFO: {'Role': 'Client #4', 'Round': 65, 'Results_raw': {'train_loss': 34.584546, 'val_loss': 35.351013, 'test_loss': 34.639887}}
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2024-11-15 02:24:10,630 (client:354) INFO: {'Role': 'Client #6', 'Round': 65, 'Results_raw': {'train_loss': 32.231264, 'val_loss': 31.115562, 'test_loss': 34.479354}}
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2024-11-15 02:24:10,633 (server:615) INFO: {'Role': 'Server #', 'Round': 64, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.521662), 'test_loss': np.float64(222585.997607), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.047844), 'val_loss': np.float64(219917.455627), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.521662), 'test_loss': np.float64(222585.997607), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.047844), 'val_loss': np.float64(219917.455627), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.928609), 'test_avg_loss_bottom_decile': np.float64(37.985566), 'test_avg_loss_top_decile': np.float64(42.143716), 'test_avg_loss_min': np.float64(34.945026), 'test_avg_loss_max': np.float64(42.143716), 'test_avg_loss_bottom10%': np.float64(34.945026), 'test_avg_loss_top10%': np.float64(42.143716), 'test_avg_loss_cos1': np.float64(0.998811), 'test_avg_loss_entropy': np.float64(2.301373), 'test_loss_std': np.float64(10861.927913), 'test_loss_bottom_decile': np.float64(213934.706909), 'test_loss_top_decile': np.float64(237353.410767), 'test_loss_min': np.float64(196810.386353), 'test_loss_max': np.float64(237353.410767), 'test_loss_bottom10%': np.float64(196810.386353), 'test_loss_top10%': np.float64(237353.410767), 'test_loss_cos1': np.float64(0.998811), 'test_loss_entropy': np.float64(2.301373), 'val_avg_loss_std': np.float64(2.204134), 'val_avg_loss_bottom_decile': np.float64(37.734646), 'val_avg_loss_top_decile': np.float64(42.598516), 'val_avg_loss_min': np.float64(34.285045), 'val_avg_loss_max': np.float64(42.598516), 'val_avg_loss_bottom10%': np.float64(34.285045), 'val_avg_loss_top10%': np.float64(42.598516), 'val_avg_loss_cos1': np.float64(0.998411), 'val_avg_loss_entropy': np.float64(2.300976), 'val_loss_std': np.float64(12413.682889), 'val_loss_bottom_decile': np.float64(212521.527466), 'val_loss_top_decile': np.float64(239914.843628), 'val_loss_min': np.float64(193093.374878), 'val_loss_max': np.float64(239914.843628), 'val_loss_bottom10%': np.float64(193093.374878), 'val_loss_top10%': np.float64(239914.843628), 'val_loss_cos1': np.float64(0.998411), 'val_loss_entropy': np.float64(2.300976)}}
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2024-11-15 02:24:10,660 (server:353) INFO: Server: Starting evaluation at the end of round 65.
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2024-11-15 02:24:10,661 (server:359) INFO: ----------- Starting a new training round (Round #66) -------------
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2024-11-15 02:26:40,913 (client:354) INFO: {'Role': 'Client #4', 'Round': 66, 'Results_raw': {'train_loss': 34.481774, 'val_loss': 35.006245, 'test_loss': 34.305078}}
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2024-11-15 02:27:34,378 (client:354) INFO: {'Role': 'Client #6', 'Round': 66, 'Results_raw': {'train_loss': 32.142012, 'val_loss': 30.897382, 'test_loss': 33.800947}}
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2024-11-15 02:28:27,633 (client:354) INFO: {'Role': 'Client #9', 'Round': 66, 'Results_raw': {'train_loss': 33.231746, 'val_loss': 33.688556, 'test_loss': 35.586157}}
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2024-11-15 02:29:20,477 (client:354) INFO: {'Role': 'Client #5', 'Round': 66, 'Results_raw': {'train_loss': 30.40581, 'val_loss': 31.660344, 'test_loss': 33.101603}}
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2024-11-15 02:30:13,691 (client:354) INFO: {'Role': 'Client #2', 'Round': 66, 'Results_raw': {'train_loss': 26.946707, 'val_loss': 27.109131, 'test_loss': 28.113803}}
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2024-11-15 02:31:07,062 (client:354) INFO: {'Role': 'Client #10', 'Round': 66, 'Results_raw': {'train_loss': 31.16393, 'val_loss': 31.653117, 'test_loss': 32.671831}}
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2024-11-15 02:32:00,434 (client:354) INFO: {'Role': 'Client #8', 'Round': 66, 'Results_raw': {'train_loss': 31.133294, 'val_loss': 31.685815, 'test_loss': 33.041129}}
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2024-11-15 02:32:53,837 (client:354) INFO: {'Role': 'Client #1', 'Round': 66, 'Results_raw': {'train_loss': 31.482368, 'val_loss': 32.564028, 'test_loss': 33.664444}}
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2024-11-15 02:33:47,666 (client:354) INFO: {'Role': 'Client #7', 'Round': 66, 'Results_raw': {'train_loss': 29.953472, 'val_loss': 30.036545, 'test_loss': 31.142384}}
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2024-11-15 02:34:41,119 (client:354) INFO: {'Role': 'Client #3', 'Round': 66, 'Results_raw': {'train_loss': 30.139605, 'val_loss': 33.221117, 'test_loss': 33.921652}}
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2024-11-15 02:34:41,122 (server:615) INFO: {'Role': 'Server #', 'Round': 65, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.533779), 'test_loss': np.float64(222654.240784), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.060647), 'val_loss': np.float64(219989.563208), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.533779), 'test_loss': np.float64(222654.240784), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.060647), 'val_loss': np.float64(219989.563208), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.93013), 'test_avg_loss_bottom_decile': np.float64(37.899757), 'test_avg_loss_top_decile': np.float64(42.186345), 'test_avg_loss_min': np.float64(34.988658), 'test_avg_loss_max': np.float64(42.186345), 'test_avg_loss_bottom10%': np.float64(34.988658), 'test_avg_loss_top10%': np.float64(42.186345), 'test_avg_loss_cos1': np.float64(0.99881), 'test_avg_loss_entropy': np.float64(2.301372), 'test_loss_std': np.float64(10870.492271), 'test_loss_bottom_decile': np.float64(213451.430908), 'test_loss_top_decile': np.float64(237593.49353), 'test_loss_min': np.float64(197056.12439), 'test_loss_max': np.float64(237593.49353), 'test_loss_bottom10%': np.float64(197056.12439), 'test_loss_top10%': np.float64(237593.49353), 'test_loss_cos1': np.float64(0.99881), 'test_loss_entropy': np.float64(2.301372), 'val_avg_loss_std': np.float64(2.185577), 'val_avg_loss_bottom_decile': np.float64(37.660996), 'val_avg_loss_top_decile': np.float64(42.513701), 'val_avg_loss_min': np.float64(34.345161), 'val_avg_loss_max': np.float64(42.513701), 'val_avg_loss_bottom10%': np.float64(34.345161), 'val_avg_loss_top10%': np.float64(42.513701), 'val_avg_loss_cos1': np.float64(0.998438), 'val_avg_loss_entropy': np.float64(2.301004), 'val_loss_std': np.float64(12309.170635), 'val_loss_bottom_decile': np.float64(212106.732056), 'val_loss_top_decile': np.float64(239437.165894), 'val_loss_min': np.float64(193431.94812), 'val_loss_max': np.float64(239437.165894), 'val_loss_bottom10%': np.float64(193431.94812), 'val_loss_top10%': np.float64(239437.165894), 'val_loss_cos1': np.float64(0.998438), 'val_loss_entropy': np.float64(2.301004)}}
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2024-11-15 02:34:41,156 (server:353) INFO: Server: Starting evaluation at the end of round 66.
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2024-11-15 02:34:41,156 (server:359) INFO: ----------- Starting a new training round (Round #67) -------------
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2024-11-15 02:37:12,866 (client:354) INFO: {'Role': 'Client #3', 'Round': 67, 'Results_raw': {'train_loss': 30.148694, 'val_loss': 33.172674, 'test_loss': 33.67074}}
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2024-11-15 02:38:08,679 (client:354) INFO: {'Role': 'Client #9', 'Round': 67, 'Results_raw': {'train_loss': 33.244543, 'val_loss': 33.595625, 'test_loss': 35.468061}}
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2024-11-15 02:39:04,057 (client:354) INFO: {'Role': 'Client #5', 'Round': 67, 'Results_raw': {'train_loss': 30.415657, 'val_loss': 31.730374, 'test_loss': 32.89389}}
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2024-11-15 02:39:59,534 (client:354) INFO: {'Role': 'Client #7', 'Round': 67, 'Results_raw': {'train_loss': 29.93831, 'val_loss': 30.077625, 'test_loss': 30.961553}}
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2024-11-15 02:40:55,736 (client:354) INFO: {'Role': 'Client #4', 'Round': 67, 'Results_raw': {'train_loss': 34.455666, 'val_loss': 35.447063, 'test_loss': 34.761674}}
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2024-11-15 02:41:51,444 (client:354) INFO: {'Role': 'Client #1', 'Round': 67, 'Results_raw': {'train_loss': 31.505377, 'val_loss': 32.843433, 'test_loss': 34.045306}}
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2024-11-15 02:42:45,269 (client:354) INFO: {'Role': 'Client #6', 'Round': 67, 'Results_raw': {'train_loss': 32.765854, 'val_loss': 31.868939, 'test_loss': 35.921462}}
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2024-11-15 02:43:38,851 (client:354) INFO: {'Role': 'Client #10', 'Round': 67, 'Results_raw': {'train_loss': 31.186493, 'val_loss': 31.641732, 'test_loss': 32.60581}}
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2024-11-15 02:44:32,465 (client:354) INFO: {'Role': 'Client #2', 'Round': 67, 'Results_raw': {'train_loss': 26.951197, 'val_loss': 27.055842, 'test_loss': 28.173714}}
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2024-11-15 02:45:26,250 (client:354) INFO: {'Role': 'Client #8', 'Round': 67, 'Results_raw': {'train_loss': 31.086973, 'val_loss': 31.65603, 'test_loss': 32.877064}}
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2024-11-15 02:45:26,253 (server:615) INFO: {'Role': 'Server #', 'Round': 66, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.515244), 'test_loss': np.float64(222549.851953), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.038661), 'val_loss': np.float64(219865.739172), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.515244), 'test_loss': np.float64(222549.851953), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.038661), 'val_loss': np.float64(219865.739172), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.909827), 'test_avg_loss_bottom_decile': np.float64(37.896551), 'test_avg_loss_top_decile': np.float64(42.078414), 'test_avg_loss_min': np.float64(35.004957), 'test_avg_loss_max': np.float64(42.078414), 'test_avg_loss_bottom10%': np.float64(35.004957), 'test_avg_loss_top10%': np.float64(42.078414), 'test_avg_loss_cos1': np.float64(0.998834), 'test_avg_loss_entropy': np.float64(2.301397), 'test_loss_std': np.float64(10756.14361), 'test_loss_bottom_decile': np.float64(213433.374512), 'test_loss_top_decile': np.float64(236985.625977), 'test_loss_min': np.float64(197147.917725), 'test_loss_max': np.float64(236985.625977), 'test_loss_bottom10%': np.float64(197147.917725), 'test_loss_top10%': np.float64(236985.625977), 'test_loss_cos1': np.float64(0.998834), 'test_loss_entropy': np.float64(2.301397), 'val_avg_loss_std': np.float64(2.170246), 'val_avg_loss_bottom_decile': np.float64(37.66559), 'val_avg_loss_top_decile': np.float64(42.542426), 'val_avg_loss_min': np.float64(34.364182), 'val_avg_loss_max': np.float64(42.542426), 'val_avg_loss_bottom10%': np.float64(34.364182), 'val_avg_loss_top10%': np.float64(42.542426), 'val_avg_loss_cos1': np.float64(0.998458), 'val_avg_loss_entropy': np.float64(2.301025), 'val_loss_std': np.float64(12222.827425), 'val_loss_bottom_decile': np.float64(212132.604858), 'val_loss_top_decile': np.float64(239598.945923), 'val_loss_min': np.float64(193539.074829), 'val_loss_max': np.float64(239598.945923), 'val_loss_bottom10%': np.float64(193539.074829), 'val_loss_top10%': np.float64(239598.945923), 'val_loss_cos1': np.float64(0.998458), 'val_loss_entropy': np.float64(2.301025)}}
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2024-11-15 02:45:26,287 (server:353) INFO: Server: Starting evaluation at the end of round 67.
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2024-11-15 02:45:26,288 (server:359) INFO: ----------- Starting a new training round (Round #68) -------------
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2024-11-15 02:47:57,004 (client:354) INFO: {'Role': 'Client #9', 'Round': 68, 'Results_raw': {'train_loss': 33.178082, 'val_loss': 33.390038, 'test_loss': 35.324944}}
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2024-11-15 02:48:50,427 (client:354) INFO: {'Role': 'Client #2', 'Round': 68, 'Results_raw': {'train_loss': 26.835774, 'val_loss': 27.12158, 'test_loss': 28.260111}}
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2024-11-15 02:49:43,089 (client:354) INFO: {'Role': 'Client #5', 'Round': 68, 'Results_raw': {'train_loss': 30.399278, 'val_loss': 31.674628, 'test_loss': 33.179363}}
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2024-11-15 02:50:35,912 (client:354) INFO: {'Role': 'Client #1', 'Round': 68, 'Results_raw': {'train_loss': 31.464487, 'val_loss': 32.727918, 'test_loss': 33.701869}}
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2024-11-15 02:51:29,290 (client:354) INFO: {'Role': 'Client #10', 'Round': 68, 'Results_raw': {'train_loss': 31.145973, 'val_loss': 31.642128, 'test_loss': 32.718492}}
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2024-11-15 02:52:22,640 (client:354) INFO: {'Role': 'Client #4', 'Round': 68, 'Results_raw': {'train_loss': 34.349044, 'val_loss': 35.2443, 'test_loss': 34.621319}}
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2024-11-15 02:53:16,215 (client:354) INFO: {'Role': 'Client #3', 'Round': 68, 'Results_raw': {'train_loss': 30.096912, 'val_loss': 32.918266, 'test_loss': 33.88066}}
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2024-11-15 02:54:09,406 (client:354) INFO: {'Role': 'Client #7', 'Round': 68, 'Results_raw': {'train_loss': 29.914184, 'val_loss': 30.204881, 'test_loss': 31.217755}}
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2024-11-15 02:55:03,030 (client:354) INFO: {'Role': 'Client #8', 'Round': 68, 'Results_raw': {'train_loss': 31.092821, 'val_loss': 31.477873, 'test_loss': 32.677896}}
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2024-11-15 02:55:56,702 (client:354) INFO: {'Role': 'Client #6', 'Round': 68, 'Results_raw': {'train_loss': 32.274015, 'val_loss': 31.355509, 'test_loss': 35.489947}}
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2024-11-15 02:55:56,706 (server:615) INFO: {'Role': 'Server #', 'Round': 67, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.527036), 'test_loss': np.float64(222616.26792), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.054682), 'val_loss': np.float64(219955.968066), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.527036), 'test_loss': np.float64(222616.26792), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.054682), 'val_loss': np.float64(219955.968066), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.934899), 'test_avg_loss_bottom_decile': np.float64(37.901644), 'test_avg_loss_top_decile': np.float64(42.168811), 'test_avg_loss_min': np.float64(34.924456), 'test_avg_loss_max': np.float64(42.168811), 'test_avg_loss_bottom10%': np.float64(34.924456), 'test_avg_loss_top10%': np.float64(42.168811), 'test_avg_loss_cos1': np.float64(0.998804), 'test_avg_loss_entropy': np.float64(2.301365), 'test_loss_std': np.float64(10897.350302), 'test_loss_bottom_decile': np.float64(213462.056641), 'test_loss_top_decile': np.float64(237494.743286), 'test_loss_min': np.float64(196694.535278), 'test_loss_max': np.float64(237494.743286), 'test_loss_bottom10%': np.float64(196694.535278), 'test_loss_top10%': np.float64(237494.743286), 'test_loss_cos1': np.float64(0.998804), 'test_loss_entropy': np.float64(2.301365), 'val_avg_loss_std': np.float64(2.203002), 'val_avg_loss_bottom_decile': np.float64(37.651996), 'val_avg_loss_top_decile': np.float64(42.4848), 'val_avg_loss_min': np.float64(34.268297), 'val_avg_loss_max': np.float64(42.4848), 'val_avg_loss_bottom10%': np.float64(34.268297), 'val_avg_loss_top10%': np.float64(42.4848), 'val_avg_loss_cos1': np.float64(0.998413), 'val_avg_loss_entropy': np.float64(2.300977), 'val_loss_std': np.float64(12407.307157), 'val_loss_bottom_decile': np.float64(212056.039429), 'val_loss_top_decile': np.float64(239274.395386), 'val_loss_min': np.float64(192999.047241), 'val_loss_max': np.float64(239274.395386), 'val_loss_bottom10%': np.float64(192999.047241), 'val_loss_top10%': np.float64(239274.395386), 'val_loss_cos1': np.float64(0.998413), 'val_loss_entropy': np.float64(2.300977)}}
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2024-11-15 02:55:56,738 (server:353) INFO: Server: Starting evaluation at the end of round 68.
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2024-11-15 02:55:56,739 (server:359) INFO: ----------- Starting a new training round (Round #69) -------------
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2024-11-15 02:58:26,432 (client:354) INFO: {'Role': 'Client #10', 'Round': 69, 'Results_raw': {'train_loss': 31.154058, 'val_loss': 32.599617, 'test_loss': 33.432497}}
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2024-11-15 02:59:20,078 (client:354) INFO: {'Role': 'Client #8', 'Round': 69, 'Results_raw': {'train_loss': 31.079574, 'val_loss': 31.62447, 'test_loss': 32.751572}}
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2024-11-15 03:00:13,147 (client:354) INFO: {'Role': 'Client #6', 'Round': 69, 'Results_raw': {'train_loss': 32.20037, 'val_loss': 30.765601, 'test_loss': 32.916338}}
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2024-11-15 03:01:06,292 (client:354) INFO: {'Role': 'Client #5', 'Round': 69, 'Results_raw': {'train_loss': 30.359609, 'val_loss': 31.729447, 'test_loss': 33.158707}}
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2024-11-15 03:01:59,846 (client:354) INFO: {'Role': 'Client #9', 'Round': 69, 'Results_raw': {'train_loss': 33.22356, 'val_loss': 33.531618, 'test_loss': 35.232914}}
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2024-11-15 03:02:53,830 (client:354) INFO: {'Role': 'Client #7', 'Round': 69, 'Results_raw': {'train_loss': 29.902948, 'val_loss': 30.271605, 'test_loss': 30.984232}}
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2024-11-15 03:03:47,635 (client:354) INFO: {'Role': 'Client #2', 'Round': 69, 'Results_raw': {'train_loss': 26.909296, 'val_loss': 27.130188, 'test_loss': 27.78769}}
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2024-11-15 03:04:41,617 (client:354) INFO: {'Role': 'Client #4', 'Round': 69, 'Results_raw': {'train_loss': 34.351334, 'val_loss': 35.038756, 'test_loss': 34.409836}}
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2024-11-15 03:05:35,236 (client:354) INFO: {'Role': 'Client #1', 'Round': 69, 'Results_raw': {'train_loss': 31.429318, 'val_loss': 32.674305, 'test_loss': 33.837181}}
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2024-11-15 03:06:29,019 (client:354) INFO: {'Role': 'Client #3', 'Round': 69, 'Results_raw': {'train_loss': 30.115501, 'val_loss': 32.98518, 'test_loss': 33.495506}}
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2024-11-15 03:06:29,021 (server:615) INFO: {'Role': 'Server #', 'Round': 68, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.407427), 'test_loss': np.float64(221942.626624), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(38.944663), 'val_loss': np.float64(219336.34342), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.407427), 'test_loss': np.float64(221942.626624), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(38.944663), 'val_loss': np.float64(219336.34342), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.859784), 'test_avg_loss_bottom_decile': np.float64(37.856106), 'test_avg_loss_top_decile': np.float64(41.89844), 'test_avg_loss_min': np.float64(35.021083), 'test_avg_loss_max': np.float64(41.89844), 'test_avg_loss_bottom10%': np.float64(35.021083), 'test_avg_loss_top10%': np.float64(41.89844), 'test_avg_loss_cos1': np.float64(0.998888), 'test_avg_loss_entropy': np.float64(2.301453), 'test_loss_std': np.float64(10474.305108), 'test_loss_bottom_decile': np.float64(213205.58728), 'test_loss_top_decile': np.float64(235972.014038), 'test_loss_min': np.float64(197238.737549), 'test_loss_max': np.float64(235972.014038), 'test_loss_bottom10%': np.float64(197238.737549), 'test_loss_top10%': np.float64(235972.014038), 'test_loss_cos1': np.float64(0.998888), 'test_loss_entropy': np.float64(2.301453), 'val_avg_loss_std': np.float64(2.135243), 'val_avg_loss_bottom_decile': np.float64(37.63629), 'val_avg_loss_top_decile': np.float64(42.449951), 'val_avg_loss_min': np.float64(34.38897), 'val_avg_loss_max': np.float64(42.449951), 'val_avg_loss_bottom10%': np.float64(34.38897), 'val_avg_loss_top10%': np.float64(42.449951), 'val_avg_loss_cos1': np.float64(0.9985), 'val_avg_loss_entropy': np.float64(2.301069), 'val_loss_std': np.float64(12025.690166), 'val_loss_bottom_decile': np.float64(211967.587158), 'val_loss_top_decile': np.float64(239078.125), 'val_loss_min': np.float64(193678.680054), 'val_loss_max': np.float64(239078.125), 'val_loss_bottom10%': np.float64(193678.680054), 'val_loss_top10%': np.float64(239078.125), 'val_loss_cos1': np.float64(0.9985), 'val_loss_entropy': np.float64(2.301069)}}
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2024-11-15 03:06:29,053 (server:370) INFO: Server: Training is finished! Starting evaluation.
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2024-11-15 03:08:05,372 (server:615) INFO: {'Role': 'Server #', 'Round': 69, 'Results_weighted_avg': {'test_avg_loss': np.float64(39.477885), 'test_loss': np.float64(222339.446387), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.011095), 'val_loss': np.float64(219710.484338), 'val_total': np.float64(5632.0)}, 'Results_avg': {'test_avg_loss': np.float64(39.477885), 'test_loss': np.float64(222339.446387), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(39.011095), 'val_loss': np.float64(219710.484338), 'val_total': np.float64(5632.0)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.92846), 'test_avg_loss_bottom_decile': np.float64(37.904032), 'test_avg_loss_top_decile': np.float64(42.142872), 'test_avg_loss_min': np.float64(34.939129), 'test_avg_loss_max': np.float64(42.142872), 'test_avg_loss_bottom10%': np.float64(34.939129), 'test_avg_loss_top10%': np.float64(42.142872), 'test_avg_loss_cos1': np.float64(0.998809), 'test_avg_loss_entropy': np.float64(2.301372), 'test_loss_std': np.float64(10861.084025), 'test_loss_bottom_decile': np.float64(213475.505737), 'test_loss_top_decile': np.float64(237348.657227), 'test_loss_min': np.float64(196777.172974), 'test_loss_max': np.float64(237348.657227), 'test_loss_bottom10%': np.float64(196777.172974), 'test_loss_top10%': np.float64(237348.657227), 'test_loss_cos1': np.float64(0.998809), 'test_loss_entropy': np.float64(2.301372), 'val_avg_loss_std': np.float64(2.195831), 'val_avg_loss_bottom_decile': np.float64(37.687822), 'val_avg_loss_top_decile': np.float64(42.524211), 'val_avg_loss_min': np.float64(34.306593), 'val_avg_loss_max': np.float64(42.524211), 'val_avg_loss_bottom10%': np.float64(34.306593), 'val_avg_loss_top10%': np.float64(42.524211), 'val_avg_loss_cos1': np.float64(0.99842), 'val_avg_loss_entropy': np.float64(2.300986), 'val_loss_std': np.float64(12366.919175), 'val_loss_bottom_decile': np.float64(212257.814819), 'val_loss_top_decile': np.float64(239496.359009), 'val_loss_min': np.float64(193214.731445), 'val_loss_max': np.float64(239496.359009), 'val_loss_bottom10%': np.float64(193214.731445), 'val_loss_top10%': np.float64(239496.359009), 'val_loss_cos1': np.float64(0.99842), 'val_loss_entropy': np.float64(2.300986)}}
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2024-11-15 03:08:05,374 (server:420) INFO: Server: Final evaluation is finished! Starting merging results.
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2024-11-15 03:08:05,375 (server:546) INFO: {'Role': 'Server #', 'Round': 'Final', 'Results_raw': {'client_best_individual': {'val_loss': 192999.047241, 'test_avg_loss': 34.924456, 'test_loss': 196694.535278, 'test_total': 5632.0, 'val_avg_loss': 34.268297, 'val_total': 5632.0}, 'client_summarized_weighted_avg': {'val_loss': np.float64(219336.34342), 'test_avg_loss': np.float64(39.407427), 'test_loss': np.float64(221942.626624), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(38.944663), 'val_total': np.float64(5632.0)}, 'client_summarized_avg': {'val_loss': np.float64(219336.34342), 'test_avg_loss': np.float64(39.407427), 'test_loss': np.float64(221942.626624), 'test_total': np.float64(5632.0), 'val_avg_loss': np.float64(38.944663), 'val_total': np.float64(5632.0)}, 'client_summarized_fairness': {'val_loss_entropy': np.float64(2.300956), 'val_loss_cos1': np.float64(0.998391), 'val_loss_top10%': np.float64(240480.34375), 'val_loss_bottom10%': np.float64(193789.764526), 'val_loss_max': np.float64(240480.34375), 'val_loss_min': np.float64(193789.764526), 'val_loss_top_decile': np.float64(240480.34375), 'val_loss_bottom_decile': np.float64(212934.792969), 'val_loss_std': np.float64(12530.468576), 'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_avg_loss_std': np.float64(1.932122), 'test_avg_loss_bottom_decile': np.float64(38.021482), 'test_avg_loss_top_decile': np.float64(42.216869), 'test_avg_loss_min': np.float64(35.061251), 'test_avg_loss_max': np.float64(42.216869), 'test_avg_loss_bottom10%': np.float64(35.061251), 'test_avg_loss_top10%': np.float64(42.216869), 'test_avg_loss_cos1': np.float64(0.998814), 'test_avg_loss_entropy': np.float64(2.301375), 'test_loss_std': np.float64(10881.711222), 'test_loss_bottom_decile': np.float64(214136.984497), 'test_loss_top_decile': np.float64(237765.408081), 'test_loss_min': np.float64(197464.964722), 'test_loss_max': np.float64(237765.408081), 'test_loss_bottom10%': np.float64(197464.964722), 'test_loss_top10%': np.float64(237765.408081), 'test_loss_cos1': np.float64(0.998814), 'test_loss_entropy': np.float64(2.301375), 'val_avg_loss_std': np.float64(2.22487), 'val_avg_loss_bottom_decile': np.float64(37.808024), 'val_avg_loss_top_decile': np.float64(42.698925), 'val_avg_loss_min': np.float64(34.408694), 'val_avg_loss_max': np.float64(42.698925), 'val_avg_loss_bottom10%': np.float64(34.408694), 'val_avg_loss_top10%': np.float64(42.698925), 'val_avg_loss_cos1': np.float64(0.998391), 'val_avg_loss_entropy': np.float64(2.300956)}}}
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2024-11-15 03:08:05,377 (server:565) INFO: {'Role': 'Client #1', 'Round': 70, 'Results_raw': {'test_avg_loss': 40.417779, 'test_loss': 227632.932373, 'test_total': 5632, 'val_avg_loss': 39.940517, 'val_loss': 224944.989868, 'val_total': 5632}}
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2024-11-15 03:08:05,377 (server:565) INFO: {'Role': 'Client #2', 'Round': 70, 'Results_raw': {'test_avg_loss': 34.939129, 'test_loss': 196777.172974, 'test_total': 5632, 'val_avg_loss': 34.306593, 'val_loss': 193214.731445, 'val_total': 5632}}
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2024-11-15 03:08:05,378 (server:565) INFO: {'Role': 'Client #3', 'Round': 70, 'Results_raw': {'test_avg_loss': 40.297725, 'test_loss': 226956.787598, 'test_total': 5632, 'val_avg_loss': 41.489846, 'val_loss': 233670.812744, 'val_total': 5632}}
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2024-11-15 03:08:05,378 (server:565) INFO: {'Role': 'Client #4', 'Round': 70, 'Results_raw': {'test_avg_loss': 41.554609, 'test_loss': 234035.557739, 'test_total': 5632, 'val_avg_loss': 42.524211, 'val_loss': 239496.359009, 'val_total': 5632}}
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2024-11-15 03:08:05,378 (server:565) INFO: {'Role': 'Client #5', 'Round': 70, 'Results_raw': {'test_avg_loss': 39.95886, 'test_loss': 225048.299805, 'test_total': 5632, 'val_avg_loss': 38.891742, 'val_loss': 219038.293091, 'val_total': 5632}}
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2024-11-15 03:08:05,379 (server:565) INFO: {'Role': 'Client #6', 'Round': 70, 'Results_raw': {'test_avg_loss': 38.727572, 'test_loss': 218113.685425, 'test_total': 5632, 'val_avg_loss': 38.312715, 'val_loss': 215777.21228, 'val_total': 5632}}
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2024-11-15 03:08:05,379 (server:565) INFO: {'Role': 'Client #7', 'Round': 70, 'Results_raw': {'test_avg_loss': 37.904032, 'test_loss': 213475.505737, 'test_total': 5632, 'val_avg_loss': 37.687822, 'val_loss': 212257.814819, 'val_total': 5632}}
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2024-11-15 03:08:05,379 (server:565) INFO: {'Role': 'Client #8', 'Round': 70, 'Results_raw': {'test_avg_loss': 39.005635, 'test_loss': 219679.733643, 'test_total': 5632, 'val_avg_loss': 38.048974, 'val_loss': 214291.824219, 'val_total': 5632}}
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2024-11-15 03:08:05,380 (server:565) INFO: {'Role': 'Client #9', 'Round': 70, 'Results_raw': {'test_avg_loss': 42.142872, 'test_loss': 237348.657227, 'test_total': 5632, 'val_avg_loss': 40.711813, 'val_loss': 229288.93335, 'val_total': 5632}}
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2024-11-15 03:08:05,380 (server:565) INFO: {'Role': 'Client #10', 'Round': 70, 'Results_raw': {'test_avg_loss': 39.830634, 'test_loss': 224326.131348, 'test_total': 5632, 'val_avg_loss': 38.19671, 'val_loss': 215123.872559, 'val_total': 5632}}
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2024-11-15 03:08:05,386 (monitor:173) INFO: In worker #0, the system-related metrics are: {'id': 0, 'fl_end_time_minutes': 907.604177, '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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2024-11-15 03:08:05,387 (client:582) INFO: ================= client 1 received finish message =================
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2024-11-15 03:08:05,390 (monitor:173) INFO: In worker #1, the system-related metrics are: {'id': 1, 'fl_end_time_minutes': 907.603648, 'total_model_size': 564874, 'total_flops': 216776641213440.0, '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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2024-11-15 03:08:05,390 (client:582) INFO: ================= client 2 received finish message =================
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2024-11-15 03:08:05,392 (monitor:173) INFO: In worker #2, the system-related metrics are: {'id': 2, 'fl_end_time_minutes': 907.602994, 'total_model_size': 564874, 'total_flops': 216776641213440.0, '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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2024-11-15 03:08:05,392 (client:582) INFO: ================= client 3 received finish message =================
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2024-11-15 03:08:05,394 (monitor:173) INFO: In worker #3, the system-related metrics are: {'id': 3, 'fl_end_time_minutes': 907.602751, 'total_model_size': 564874, 'total_flops': 216776641213440.0, '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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2024-11-15 03:08:05,394 (client:582) INFO: ================= client 4 received finish message =================
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2024-11-15 03:08:05,396 (monitor:173) INFO: In worker #4, the system-related metrics are: {'id': 4, 'fl_end_time_minutes': 907.602498, 'total_model_size': 564874, 'total_flops': 216776641213440.0, '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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2024-11-15 03:08:05,396 (client:582) INFO: ================= client 5 received finish message =================
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2024-11-15 03:08:05,398 (monitor:173) INFO: In worker #5, the system-related metrics are: {'id': 5, 'fl_end_time_minutes': 907.602253, 'total_model_size': 564874, 'total_flops': 216776641213440.0, '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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2024-11-15 03:08:05,398 (client:582) INFO: ================= client 6 received finish message =================
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2024-11-15 03:08:05,400 (monitor:173) INFO: In worker #6, the system-related metrics are: {'id': 6, 'fl_end_time_minutes': 907.602016, 'total_model_size': 564874, 'total_flops': 216776641213440.0, '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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2024-11-15 03:08:05,401 (client:582) INFO: ================= client 7 received finish message =================
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2024-11-15 03:08:05,402 (monitor:173) INFO: In worker #7, the system-related metrics are: {'id': 7, 'fl_end_time_minutes': 907.601727, 'total_model_size': 564874, 'total_flops': 216776641213440.0, '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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2024-11-15 03:08:05,403 (client:582) INFO: ================= client 8 received finish message =================
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2024-11-15 03:08:05,405 (monitor:173) INFO: In worker #8, the system-related metrics are: {'id': 8, 'fl_end_time_minutes': 907.601493, 'total_model_size': 564874, 'total_flops': 216776641213440.0, '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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2024-11-15 03:08:05,405 (client:582) INFO: ================= client 9 received finish message =================
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2024-11-15 03:08:05,407 (monitor:173) INFO: In worker #9, the system-related metrics are: {'id': 9, 'fl_end_time_minutes': 907.601257, 'total_model_size': 564874, 'total_flops': 216776641213440.0, '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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2024-11-15 03:08:05,407 (client:582) INFO: ================= client 10 received finish message =================
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2024-11-15 03:08:05,409 (monitor:173) INFO: In worker #10, the system-related metrics are: {'id': 10, 'fl_end_time_minutes': 907.600997, 'total_model_size': 564874, 'total_flops': 216776641213440.0, '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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2024-11-15 03:08:05,409 (monitor:338) INFO: We will compress the file eval_results.raw into a .gz file, and delete the old one
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2024-11-15 03:08:05,443 (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(907.602346), 'sys_avg/total_model_size': '501.49K', 'sys_avg/total_flops': '179.23T', '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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2024-11-15 03:08:05,444 (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(0.000948), 'sys_std/total_model_size': '158.58K', 'sys_std/total_flops': '56.68T', '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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