1414 lines
306 KiB
Plaintext
1414 lines
306 KiB
Plaintext
2024-11-15 19:01:52,461 (logging:124) INFO: the current machine is at 127.0.1.1
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2024-11-15 19:01:52,461 (logging:126) INFO: the current dir is /home/czzhangheng/code/FederatedScope
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2024-11-15 19:01:52,462 (logging:127) INFO: the output dir is exp/FedAvg_DDGCRN_on_trafficflow_lr0.0015_lstep1
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2024-11-15 19:04:16,897 (config:243) INFO: the used configs are:
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aggregator:
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BFT_args:
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byzantine_node_num: 0
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inside_weight: 1.0
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num_agg_groups: 1
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num_agg_topk: []
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outside_weight: 0.0
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robust_rule: fedavg
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asyn:
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use: False
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attack:
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alpha_TV: 0.001
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alpha_prop_loss: 0
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attack_method:
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attacker_id: -1
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classifier_PIA: randomforest
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edge_num: 100
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edge_path: edge_data/
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freq: 10
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info_diff_type: l2
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inject_round: 0
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insert_round: 100000
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label_type: dirty
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max_ite: 400
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mean: [0.9637]
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mia_is_simulate_in: False
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mia_simulate_in_round: 20
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pgd_eps: 2
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pgd_lr: 0.1
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pgd_poisoning: False
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poison_ratio: 0.5
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reconstruct_lr: 0.01
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reconstruct_optim: Adam
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scale_para: 1.0
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scale_poisoning: False
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self_epoch: 6
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self_lr: 0.05
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self_opt: False
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setting: fix
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std: [0.1592]
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target_label_ind: -1
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trigger_path: trigger/
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trigger_type: edge
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backend: torch
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cfg_file:
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check_completeness: False
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criterion:
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type: MAPE
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data:
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add_day_in_week: True
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add_time_in_day: True
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args: []
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batch_size: 64
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cSBM_phi: [0.5, 0.5, 0.5]
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cache_dir:
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column_wise: False
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consistent_label_distribution: True
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days_per_week: 7
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default_graph: True
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drop_last: False
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file_path:
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hetero_data_name: []
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hetero_synth_batch_size: 32
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hetero_synth_feat_dim: 128
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hetero_synth_prim_weight: 0.5
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horizon: 12
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is_debug: False
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lag: 12
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loader:
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max_query_len: 128
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max_seq_len: 384
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max_tgt_len: 128
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normalizer: std
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num_contrast: 0
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num_nodes: 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: 16
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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_DDGCRN_on_trafficflow_lr0.0015_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: DDGCRN
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use_bias: True
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use_contrastive_loss: False
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use_day: True
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use_week: True
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nbafl:
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use: False
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outdir: exp/FedAvg_DDGCRN_on_trafficflow_lr0.0015_lstep1
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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: 0.0015
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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: 16
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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: 0.0015
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max_grad_norm: 5
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optimizer:
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lr: 0.0015
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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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2024-11-15 19:04:17,091 (utils:147) INFO: The device information file is not provided
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2024-11-15 19:04:17,255 (fed_runner:173) INFO: Server has been set up ...
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2024-11-15 19:04:17,295 (fed_runner:225) INFO: Client 1 has been set up ...
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2024-11-15 19:04:17,315 (fed_runner:225) INFO: Client 2 has been set up ...
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2024-11-15 19:04:17,331 (fed_runner:225) INFO: Client 3 has been set up ...
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2024-11-15 19:04:17,346 (fed_runner:225) INFO: Client 4 has been set up ...
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2024-11-15 19:04:17,362 (fed_runner:225) INFO: Client 5 has been set up ...
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2024-11-15 19:04:17,380 (fed_runner:225) INFO: Client 6 has been set up ...
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2024-11-15 19:04:17,398 (fed_runner:225) INFO: Client 7 has been set up ...
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2024-11-15 19:04:17,417 (fed_runner:225) INFO: Client 8 has been set up ...
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2024-11-15 19:04:17,433 (fed_runner:225) INFO: Client 9 has been set up ...
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2024-11-15 19:04:17,449 (fed_runner:225) INFO: Client 10 has been set up ...
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2024-11-15 19:04:17,449 (trainer:345) INFO: Model meta-info: <class 'federatedscope.trafficflow.model.DDGCRN.DDGCRN'>.
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2024-11-15 19:04:17,452 (trainer:353) INFO: Num of original para names: 50.
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2024-11-15 19:04:17,452 (trainer:354) INFO: Num of original trainable para names: 50.
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2024-11-15 19:04:17,452 (trainer:356) INFO: Num of preserved para names in local update: 50.
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Preserved para names in local update: {'encoder1.DGCRM_cells.0.gate.fc.fc3.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder1.DGCRM_cells.0.update.bias_pool', 'encoder1.DGCRM_cells.0.gate.fc.fc2.bias', 'encoder1.DGCRM_cells.0.update.fc.fc2.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc3.bias', 'encoder2.DGCRM_cells.0.update.fc.fc3.weight', 'encoder2.DGCRM_cells.0.update.fc.fc2.bias', 'encoder1.DGCRM_cells.0.gate.weights_pool', 'encoder2.DGCRM_cells.0.gate.weights', 'encoder2.DGCRM_cells.0.update.weights', 'T_i_D_emb', 'encoder1.DGCRM_cells.0.update.fc.fc2.weight', 'encoder2.DGCRM_cells.0.update.weights_pool', 'end_conv3.weight', 'node_embeddings1', 'encoder2.DGCRM_cells.0.gate.bias', 'node_embeddings2', 'end_conv1.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder1.DGCRM_cells.0.update.bias', 'D_i_W_emb', 'encoder2.DGCRM_cells.0.update.fc.fc1.weight', 'encoder2.DGCRM_cells.0.gate.weights_pool', 'encoder2.DGCRM_cells.0.gate.fc.fc2.bias', 'encoder2.DGCRM_cells.0.update.bias_pool', 'encoder1.DGCRM_cells.0.gate.fc.fc2.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder1.DGCRM_cells.0.gate.weights', 'encoder1.DGCRM_cells.0.update.weights', 'encoder1.DGCRM_cells.0.update.fc.fc1.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc3.weight', 'end_conv1.bias', 'encoder2.DGCRM_cells.0.update.fc.fc1.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc3.weight', 'encoder1.DGCRM_cells.0.update.fc.fc1.bias', 'end_conv3.bias', 'encoder2.DGCRM_cells.0.update.fc.fc2.weight', 'encoder1.DGCRM_cells.0.gate.bias_pool', 'encoder2.DGCRM_cells.0.gate.fc.fc2.weight', 'end_conv2.weight', 'encoder1.DGCRM_cells.0.gate.bias', 'encoder1.DGCRM_cells.0.update.fc.fc3.bias', 'encoder1.DGCRM_cells.0.update.fc.fc3.weight', 'encoder2.DGCRM_cells.0.update.bias', 'end_conv2.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder2.DGCRM_cells.0.gate.bias_pool', 'encoder1.DGCRM_cells.0.update.weights_pool', 'encoder2.DGCRM_cells.0.update.fc.fc3.bias'}.
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2024-11-15 19:04:17,452 (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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2024-11-15 19:04:17,454 (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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2024-11-15 19:04:17,496 (server:843) INFO: ----------- Starting training (Round #0) -------------
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2024-11-15 19:08:22,576 (client:354) INFO: {'Role': 'Client #9', 'Round': 0, 'Results_raw': {'train_loss': 19.922109, 'val_loss': 10.823175, 'test_loss': 10.439157}}
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2024-11-15 19:12:27,766 (client:354) INFO: {'Role': 'Client #3', 'Round': 0, 'Results_raw': {'train_loss': 26.487959, 'val_loss': 10.913484, 'test_loss': 11.100974}}
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2024-11-15 19:16:37,086 (client:354) INFO: {'Role': 'Client #6', 'Round': 0, 'Results_raw': {'train_loss': 23.143616, 'val_loss': 11.768305, 'test_loss': 11.551977}}
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2024-11-15 19:20:44,484 (client:354) INFO: {'Role': 'Client #7', 'Round': 0, 'Results_raw': {'train_loss': 24.371911, 'val_loss': 11.223665, 'test_loss': 10.960493}}
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2024-11-15 19:24:51,615 (client:354) INFO: {'Role': 'Client #4', 'Round': 0, 'Results_raw': {'train_loss': 21.19577, 'val_loss': 11.433274, 'test_loss': 10.801504}}
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2024-11-15 19:29:07,127 (client:354) INFO: {'Role': 'Client #2', 'Round': 0, 'Results_raw': {'train_loss': 21.370431, 'val_loss': 10.120902, 'test_loss': 9.917662}}
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2024-11-15 19:33:20,041 (client:354) INFO: {'Role': 'Client #1', 'Round': 0, 'Results_raw': {'train_loss': 22.313808, 'val_loss': 11.48513, 'test_loss': 11.011622}}
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2024-11-15 19:37:33,327 (client:354) INFO: {'Role': 'Client #8', 'Round': 0, 'Results_raw': {'train_loss': 18.736294, 'val_loss': 10.864812, 'test_loss': 10.626677}}
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2024-11-15 19:41:39,723 (client:354) INFO: {'Role': 'Client #5', 'Round': 0, 'Results_raw': {'train_loss': 19.967596, 'val_loss': 10.576997, 'test_loss': 10.392135}}
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2024-11-15 19:45:46,784 (client:354) INFO: {'Role': 'Client #10', 'Round': 0, 'Results_raw': {'train_loss': 19.377654, 'val_loss': 11.024943, 'test_loss': 10.781996}}
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2024-11-15 19:45:46,830 (server:353) INFO: Server: Starting evaluation at the end of round 0.
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2024-11-15 19:45:46,831 (server:359) INFO: ----------- Starting a new training round (Round #1) -------------
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2024-11-15 19:57:03,642 (client:354) INFO: {'Role': 'Client #6', 'Round': 1, 'Results_raw': {'train_loss': 13.278051, 'val_loss': 10.844003, 'test_loss': 10.682155}}
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2024-11-15 20:00:58,796 (client:354) INFO: {'Role': 'Client #4', 'Round': 1, 'Results_raw': {'train_loss': 12.660472, 'val_loss': 10.672563, 'test_loss': 10.112973}}
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2024-11-15 20:04:59,351 (client:354) INFO: {'Role': 'Client #5', 'Round': 1, 'Results_raw': {'train_loss': 12.679627, 'val_loss': 10.303644, 'test_loss': 10.161147}}
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2024-11-15 20:08:53,027 (client:354) INFO: {'Role': 'Client #8', 'Round': 1, 'Results_raw': {'train_loss': 12.488843, 'val_loss': 10.47958, 'test_loss': 10.324075}}
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2024-11-15 20:12:52,656 (client:354) INFO: {'Role': 'Client #7', 'Round': 1, 'Results_raw': {'train_loss': 13.587077, 'val_loss': 10.696619, 'test_loss': 10.473701}}
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2024-11-15 20:16:54,658 (client:354) INFO: {'Role': 'Client #9', 'Round': 1, 'Results_raw': {'train_loss': 12.529375, 'val_loss': 10.375508, 'test_loss': 10.010982}}
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2024-11-15 20:21:03,245 (client:354) INFO: {'Role': 'Client #10', 'Round': 1, 'Results_raw': {'train_loss': 12.605151, 'val_loss': 9.941362, 'test_loss': 9.721843}}
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2024-11-15 20:25:10,168 (client:354) INFO: {'Role': 'Client #3', 'Round': 1, 'Results_raw': {'train_loss': 14.861257, 'val_loss': 10.599849, 'test_loss': 10.769314}}
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2024-11-15 20:29:17,087 (client:354) INFO: {'Role': 'Client #2', 'Round': 1, 'Results_raw': {'train_loss': 12.403771, 'val_loss': 9.855844, 'test_loss': 9.70413}}
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2024-11-15 20:33:19,357 (client:354) INFO: {'Role': 'Client #1', 'Round': 1, 'Results_raw': {'train_loss': 13.459371, 'val_loss': 11.283294, 'test_loss': 10.891012}}
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2024-11-15 20:33:19,367 (server:615) INFO: {'Role': 'Server #', 'Round': 0, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(596674.935437), 'test_avg_loss': np.float64(105.943703), 'val_total': np.float64(5632.0), 'val_loss': np.float64(639821.205652), 'val_avg_loss': np.float64(113.604617)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(596674.935437), 'test_avg_loss': np.float64(105.943703), 'val_total': np.float64(5632.0), 'val_loss': np.float64(639821.205652), 'val_avg_loss': np.float64(113.604617)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(135382.499927), 'test_loss_bottom_decile': np.float64(470824.836731), 'test_loss_top_decile': np.float64(896531.748108), 'test_loss_min': np.float64(439096.04715), 'test_loss_max': np.float64(896531.748108), 'test_loss_bottom10%': np.float64(439096.04715), 'test_loss_top10%': np.float64(896531.748108), 'test_loss_cos1': np.float64(0.975212), 'test_loss_entropy': np.float64(2.278097), 'test_avg_loss_std': np.float64(24.038086), 'test_avg_loss_bottom_decile': np.float64(83.59816), 'test_avg_loss_top_decile': np.float64(159.185325), 'test_avg_loss_min': np.float64(77.964497), 'test_avg_loss_max': np.float64(159.185325), 'test_avg_loss_bottom10%': np.float64(77.964497), 'test_avg_loss_top10%': np.float64(159.185325), 'test_avg_loss_cos1': np.float64(0.975212), 'test_avg_loss_entropy': np.float64(2.278097), 'val_loss_std': np.float64(158744.543141), 'val_loss_bottom_decile': np.float64(498269.112946), 'val_loss_top_decile': np.float64(988673.398621), 'val_loss_min': np.float64(463003.740387), 'val_loss_max': np.float64(988673.398621), 'val_loss_bottom10%': np.float64(463003.740387), 'val_loss_top10%': np.float64(988673.398621), 'val_loss_cos1': np.float64(0.970573), 'val_loss_entropy': np.float64(2.273437), 'val_avg_loss_std': np.float64(28.186176), 'val_avg_loss_bottom_decile': np.float64(88.471078), 'val_avg_loss_top_decile': np.float64(175.545703), 'val_avg_loss_min': np.float64(82.209471), 'val_avg_loss_max': np.float64(175.545703), 'val_avg_loss_bottom10%': np.float64(82.209471), 'val_avg_loss_top10%': np.float64(175.545703), 'val_avg_loss_cos1': np.float64(0.970573), 'val_avg_loss_entropy': np.float64(2.273437)}}
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2024-11-15 20:33:19,399 (server:353) INFO: Server: Starting evaluation at the end of round 1.
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2024-11-15 20:33:19,400 (server:359) INFO: ----------- Starting a new training round (Round #2) -------------
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2024-11-15 20:44:24,037 (client:354) INFO: {'Role': 'Client #2', 'Round': 2, 'Results_raw': {'train_loss': 10.24268, 'val_loss': 9.425386, 'test_loss': 9.240978}}
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2024-11-15 20:48:17,222 (client:354) INFO: {'Role': 'Client #7', 'Round': 2, 'Results_raw': {'train_loss': 11.302975, 'val_loss': 10.047688, 'test_loss': 9.855851}}
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2024-11-15 20:52:12,336 (client:354) INFO: {'Role': 'Client #3', 'Round': 2, 'Results_raw': {'train_loss': 11.399153, 'val_loss': 10.118177, 'test_loss': 10.321062}}
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2024-11-15 20:56:08,043 (client:354) INFO: {'Role': 'Client #6', 'Round': 2, 'Results_raw': {'train_loss': 11.487482, 'val_loss': 10.78387, 'test_loss': 10.606853}}
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2024-11-15 21:00:01,812 (client:354) INFO: {'Role': 'Client #8', 'Round': 2, 'Results_raw': {'train_loss': 10.72199, 'val_loss': 10.202026, 'test_loss': 9.993625}}
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2024-11-15 21:03:55,758 (client:354) INFO: {'Role': 'Client #9', 'Round': 2, 'Results_raw': {'train_loss': 10.962234, 'val_loss': 10.325711, 'test_loss': 9.897658}}
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2024-11-15 21:07:47,021 (client:354) INFO: {'Role': 'Client #10', 'Round': 2, 'Results_raw': {'train_loss': 10.656351, 'val_loss': 9.68218, 'test_loss': 9.456567}}
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2024-11-15 21:11:21,968 (client:354) INFO: {'Role': 'Client #1', 'Round': 2, 'Results_raw': {'train_loss': 11.269923, 'val_loss': 10.467442, 'test_loss': 10.106459}}
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2024-11-15 21:14:46,520 (client:354) INFO: {'Role': 'Client #4', 'Round': 2, 'Results_raw': {'train_loss': 11.017943, 'val_loss': 10.579075, 'test_loss': 9.987326}}
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2024-11-15 21:18:11,716 (client:354) INFO: {'Role': 'Client #5', 'Round': 2, 'Results_raw': {'train_loss': 10.80224, 'val_loss': 10.044356, 'test_loss': 9.810681}}
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2024-11-15 21:18:11,719 (server:615) INFO: {'Role': 'Server #', 'Round': 1, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(81009.601902), 'test_avg_loss': np.float64(14.383807), 'val_total': np.float64(5632.0), 'val_loss': np.float64(83580.833589), 'val_avg_loss': np.float64(14.840347)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(81009.601902), 'test_avg_loss': np.float64(14.383807), 'val_total': np.float64(5632.0), 'val_loss': np.float64(83580.833589), 'val_avg_loss': np.float64(14.840347)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7772.713045), 'test_loss_bottom_decile': np.float64(73818.092033), 'test_loss_top_decile': np.float64(95459.864929), 'test_loss_min': np.float64(72748.201813), 'test_loss_max': np.float64(95459.864929), 'test_loss_bottom10%': np.float64(72748.201813), 'test_loss_top10%': np.float64(95459.864929), 'test_loss_cos1': np.float64(0.995429), 'test_loss_entropy': np.float64(2.298073), 'test_avg_loss_std': np.float64(1.380098), 'test_avg_loss_bottom_decile': np.float64(13.106906), 'test_avg_loss_top_decile': np.float64(16.94955), 'test_avg_loss_min': np.float64(12.916939), 'test_avg_loss_max': np.float64(16.94955), 'test_avg_loss_bottom10%': np.float64(12.916939), 'test_avg_loss_top10%': np.float64(16.94955), 'test_avg_loss_cos1': np.float64(0.995429), 'test_avg_loss_entropy': np.float64(2.298073), 'val_loss_std': np.float64(8591.548417), 'val_loss_bottom_decile': np.float64(75037.074883), 'val_loss_top_decile': np.float64(98918.186432), 'val_loss_min': np.float64(74512.265236), 'val_loss_max': np.float64(98918.186432), 'val_loss_bottom10%': np.float64(74512.265236), 'val_loss_top10%': np.float64(98918.186432), 'val_loss_cos1': np.float64(0.994758), 'val_loss_entropy': np.float64(2.297394), 'val_avg_loss_std': np.float64(1.525488), 'val_avg_loss_bottom_decile': np.float64(13.323344), 'val_avg_loss_top_decile': np.float64(17.563598), 'val_avg_loss_min': np.float64(13.230161), 'val_avg_loss_max': np.float64(17.563598), 'val_avg_loss_bottom10%': np.float64(13.230161), 'val_avg_loss_top10%': np.float64(17.563598), 'val_avg_loss_cos1': np.float64(0.994758), 'val_avg_loss_entropy': np.float64(2.297394)}}
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2024-11-15 21:18:11,763 (server:353) INFO: Server: Starting evaluation at the end of round 2.
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2024-11-15 21:18:11,764 (server:359) INFO: ----------- Starting a new training round (Round #3) -------------
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2024-11-15 21:28:05,878 (client:354) INFO: {'Role': 'Client #1', 'Round': 3, 'Results_raw': {'train_loss': 10.994892, 'val_loss': 10.206203, 'test_loss': 9.803261}}
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2024-11-15 21:31:39,818 (client:354) INFO: {'Role': 'Client #8', 'Round': 3, 'Results_raw': {'train_loss': 10.41743, 'val_loss': 10.057886, 'test_loss': 9.881681}}
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2024-11-15 21:35:17,450 (client:354) INFO: {'Role': 'Client #6', 'Round': 3, 'Results_raw': {'train_loss': 11.022957, 'val_loss': 10.320746, 'test_loss': 10.125513}}
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2024-11-15 21:39:01,869 (client:354) INFO: {'Role': 'Client #3', 'Round': 3, 'Results_raw': {'train_loss': 11.015007, 'val_loss': 9.841092, 'test_loss': 10.017797}}
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2024-11-15 21:43:03,943 (client:354) INFO: {'Role': 'Client #4', 'Round': 3, 'Results_raw': {'train_loss': 10.630771, 'val_loss': 10.334227, 'test_loss': 9.748233}}
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2024-11-15 21:47:08,619 (client:354) INFO: {'Role': 'Client #10', 'Round': 3, 'Results_raw': {'train_loss': 10.362786, 'val_loss': 9.610707, 'test_loss': 9.444879}}
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2024-11-15 21:50:59,498 (client:354) INFO: {'Role': 'Client #7', 'Round': 3, 'Results_raw': {'train_loss': 10.963404, 'val_loss': 9.921592, 'test_loss': 9.777975}}
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2024-11-15 21:54:51,164 (client:354) INFO: {'Role': 'Client #5', 'Round': 3, 'Results_raw': {'train_loss': 10.370863, 'val_loss': 9.856243, 'test_loss': 9.672945}}
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2024-11-15 21:58:41,626 (client:354) INFO: {'Role': 'Client #9', 'Round': 3, 'Results_raw': {'train_loss': 10.642435, 'val_loss': 10.032827, 'test_loss': 9.642332}}
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2024-11-15 22:02:32,303 (client:354) INFO: {'Role': 'Client #2', 'Round': 3, 'Results_raw': {'train_loss': 9.840438, 'val_loss': 9.151722, 'test_loss': 8.985357}}
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2024-11-15 22:02:32,306 (server:615) INFO: {'Role': 'Server #', 'Round': 2, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(80690.203847), 'test_avg_loss': np.float64(14.327096), 'val_total': np.float64(5632.0), 'val_loss': np.float64(83647.559337), 'val_avg_loss': np.float64(14.852194)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(80690.203847), 'test_avg_loss': np.float64(14.327096), 'val_total': np.float64(5632.0), 'val_loss': np.float64(83647.559337), 'val_avg_loss': np.float64(14.852194)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(10573.390034), 'test_loss_bottom_decile': np.float64(70783.978706), 'test_loss_top_decile': np.float64(99453.866058), 'test_loss_min': np.float64(67125.99247), 'test_loss_max': np.float64(99453.866058), 'test_loss_bottom10%': np.float64(67125.99247), 'test_loss_top10%': np.float64(99453.866058), 'test_loss_cos1': np.float64(0.991524), 'test_loss_entropy': np.float64(2.294143), 'test_avg_loss_std': np.float64(1.877377), 'test_avg_loss_bottom_decile': np.float64(12.568178), 'test_avg_loss_top_decile': np.float64(17.658712), 'test_avg_loss_min': np.float64(11.918678), 'test_avg_loss_max': np.float64(17.658712), 'test_avg_loss_bottom10%': np.float64(11.918678), 'test_avg_loss_top10%': np.float64(17.658712), 'test_avg_loss_cos1': np.float64(0.991524), 'test_avg_loss_entropy': np.float64(2.294143), 'val_loss_std': np.float64(11881.238892), 'val_loss_bottom_decile': np.float64(71490.544647), 'val_loss_top_decile': np.float64(104128.913025), 'val_loss_min': np.float64(69146.145332), 'val_loss_max': np.float64(104128.913025), 'val_loss_bottom10%': np.float64(69146.145332), 'val_loss_top10%': np.float64(104128.913025), 'val_loss_cos1': np.float64(0.990063), 'val_loss_entropy': np.float64(2.292657), 'val_avg_loss_std': np.float64(2.109595), 'val_avg_loss_bottom_decile': np.float64(12.693634), 'val_avg_loss_top_decile': np.float64(18.488798), 'val_avg_loss_min': np.float64(12.27737), 'val_avg_loss_max': np.float64(18.488798), 'val_avg_loss_bottom10%': np.float64(12.27737), 'val_avg_loss_top10%': np.float64(18.488798), 'val_avg_loss_cos1': np.float64(0.990063), 'val_avg_loss_entropy': np.float64(2.292657)}}
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2024-11-15 22:02:32,342 (server:353) INFO: Server: Starting evaluation at the end of round 3.
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2024-11-15 22:02:32,342 (server:359) INFO: ----------- Starting a new training round (Round #4) -------------
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2024-11-15 22:13:07,149 (client:354) INFO: {'Role': 'Client #3', 'Round': 4, 'Results_raw': {'train_loss': 10.661312, 'val_loss': 9.927432, 'test_loss': 10.120375}}
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2024-11-15 22:17:01,468 (client:354) INFO: {'Role': 'Client #1', 'Round': 4, 'Results_raw': {'train_loss': 10.694731, 'val_loss': 10.039393, 'test_loss': 9.663586}}
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2024-11-15 22:21:09,988 (client:354) INFO: {'Role': 'Client #10', 'Round': 4, 'Results_raw': {'train_loss': 10.168579, 'val_loss': 9.485929, 'test_loss': 9.285267}}
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2024-11-15 22:25:07,510 (client:354) INFO: {'Role': 'Client #6', 'Round': 4, 'Results_raw': {'train_loss': 10.777168, 'val_loss': 10.04507, 'test_loss': 9.899603}}
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2024-11-15 22:29:01,768 (client:354) INFO: {'Role': 'Client #5', 'Round': 4, 'Results_raw': {'train_loss': 10.231568, 'val_loss': 10.077764, 'test_loss': 9.824509}}
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2024-11-15 22:32:54,849 (client:354) INFO: {'Role': 'Client #2', 'Round': 4, 'Results_raw': {'train_loss': 9.564158, 'val_loss': 9.568446, 'test_loss': 9.41588}}
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2024-11-15 22:36:49,370 (client:354) INFO: {'Role': 'Client #8', 'Round': 4, 'Results_raw': {'train_loss': 10.190332, 'val_loss': 10.013163, 'test_loss': 9.817248}}
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2024-11-15 22:40:41,605 (client:354) INFO: {'Role': 'Client #9', 'Round': 4, 'Results_raw': {'train_loss': 10.426794, 'val_loss': 10.245263, 'test_loss': 9.820233}}
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2024-11-15 22:44:34,700 (client:354) INFO: {'Role': 'Client #7', 'Round': 4, 'Results_raw': {'train_loss': 10.703014, 'val_loss': 9.769895, 'test_loss': 9.597706}}
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2024-11-15 22:48:31,092 (client:354) INFO: {'Role': 'Client #4', 'Round': 4, 'Results_raw': {'train_loss': 10.449717, 'val_loss': 10.539454, 'test_loss': 9.944714}}
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2024-11-15 22:48:31,095 (server:615) INFO: {'Role': 'Server #', 'Round': 3, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(80692.091145), 'test_avg_loss': np.float64(14.327431), 'val_total': np.float64(5632.0), 'val_loss': np.float64(83627.736955), 'val_avg_loss': np.float64(14.848675)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(80692.091145), 'test_avg_loss': np.float64(14.327431), 'val_total': np.float64(5632.0), 'val_loss': np.float64(83627.736955), 'val_avg_loss': np.float64(14.848675)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(10508.203494), 'test_loss_bottom_decile': np.float64(69751.498138), 'test_loss_top_decile': np.float64(97769.919716), 'test_loss_min': np.float64(66260.687347), 'test_loss_max': np.float64(97769.919716), 'test_loss_bottom10%': np.float64(66260.687347), 'test_loss_top10%': np.float64(97769.919716), 'test_loss_cos1': np.float64(0.991627), 'test_loss_entropy': np.float64(2.29419), 'test_avg_loss_std': np.float64(1.865803), 'test_avg_loss_bottom_decile': np.float64(12.384854), 'test_avg_loss_top_decile': np.float64(17.359716), 'test_avg_loss_min': np.float64(11.765037), 'test_avg_loss_max': np.float64(17.359716), 'test_avg_loss_bottom10%': np.float64(11.765037), 'test_avg_loss_top10%': np.float64(17.359716), 'test_avg_loss_cos1': np.float64(0.991627), 'test_avg_loss_entropy': np.float64(2.29419), 'val_loss_std': np.float64(11784.984161), 'val_loss_bottom_decile': np.float64(70291.615379), 'val_loss_top_decile': np.float64(102044.699005), 'val_loss_min': np.float64(68183.971405), 'val_loss_max': np.float64(102044.699005), 'val_loss_bottom10%': np.float64(68183.971405), 'val_loss_top10%': np.float64(102044.699005), 'val_loss_cos1': np.float64(0.990216), 'val_loss_entropy': np.float64(2.292742), 'val_avg_loss_std': np.float64(2.092504), 'val_avg_loss_bottom_decile': np.float64(12.480756), 'val_avg_loss_top_decile': np.float64(18.118732), 'val_avg_loss_min': np.float64(12.106529), 'val_avg_loss_max': np.float64(18.118732), 'val_avg_loss_bottom10%': np.float64(12.106529), 'val_avg_loss_top10%': np.float64(18.118732), 'val_avg_loss_cos1': np.float64(0.990216), 'val_avg_loss_entropy': np.float64(2.292742)}}
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2024-11-15 22:48:31,135 (server:353) INFO: Server: Starting evaluation at the end of round 4.
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2024-11-15 22:48:31,136 (server:359) INFO: ----------- Starting a new training round (Round #5) -------------
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2024-11-15 22:59:30,248 (client:354) INFO: {'Role': 'Client #2', 'Round': 5, 'Results_raw': {'train_loss': 9.39158, 'val_loss': 8.966788, 'test_loss': 8.800504}}
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2024-11-15 23:03:23,766 (client:354) INFO: {'Role': 'Client #5', 'Round': 5, 'Results_raw': {'train_loss': 10.026462, 'val_loss': 9.598664, 'test_loss': 9.392262}}
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2024-11-15 23:07:14,920 (client:354) INFO: {'Role': 'Client #10', 'Round': 5, 'Results_raw': {'train_loss': 9.922194, 'val_loss': 9.558754, 'test_loss': 9.347305}}
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2024-11-15 23:11:07,336 (client:354) INFO: {'Role': 'Client #6', 'Round': 5, 'Results_raw': {'train_loss': 10.584455, 'val_loss': 9.913598, 'test_loss': 9.701797}}
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2024-11-15 23:14:58,699 (client:354) INFO: {'Role': 'Client #4', 'Round': 5, 'Results_raw': {'train_loss': 10.307746, 'val_loss': 10.220073, 'test_loss': 9.609602}}
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2024-11-15 23:18:50,782 (client:354) INFO: {'Role': 'Client #1', 'Round': 5, 'Results_raw': {'train_loss': 10.494237, 'val_loss': 10.06138, 'test_loss': 9.653965}}
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2024-11-15 23:22:44,136 (client:354) INFO: {'Role': 'Client #9', 'Round': 5, 'Results_raw': {'train_loss': 10.265171, 'val_loss': 9.947149, 'test_loss': 9.520118}}
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2024-11-15 23:26:38,068 (client:354) INFO: {'Role': 'Client #8', 'Round': 5, 'Results_raw': {'train_loss': 10.031275, 'val_loss': 9.764933, 'test_loss': 9.634319}}
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2024-11-15 23:30:42,591 (client:354) INFO: {'Role': 'Client #7', 'Round': 5, 'Results_raw': {'train_loss': 10.525785, 'val_loss': 9.629031, 'test_loss': 9.476627}}
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2024-11-15 23:34:40,279 (client:354) INFO: {'Role': 'Client #3', 'Round': 5, 'Results_raw': {'train_loss': 10.495277, 'val_loss': 10.118605, 'test_loss': 10.323916}}
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2024-11-15 23:34:40,283 (server:615) INFO: {'Role': 'Server #', 'Round': 4, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(82376.715615), 'test_avg_loss': np.float64(14.626548), 'val_total': np.float64(5632.0), 'val_loss': np.float64(85315.109327), 'val_avg_loss': np.float64(15.148279)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(82376.715615), 'test_avg_loss': np.float64(14.626548), 'val_total': np.float64(5632.0), 'val_loss': np.float64(85315.109327), 'val_avg_loss': np.float64(15.148279)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(10778.893129), 'test_loss_bottom_decile': np.float64(71087.392303), 'test_loss_top_decile': np.float64(98814.738739), 'test_loss_min': np.float64(67602.734978), 'test_loss_max': np.float64(98814.738739), 'test_loss_bottom10%': np.float64(67602.734978), 'test_loss_top10%': np.float64(98814.738739), 'test_loss_cos1': np.float64(0.991548), 'test_loss_entropy': np.float64(2.294108), 'test_avg_loss_std': np.float64(1.913866), 'test_avg_loss_bottom_decile': np.float64(12.622051), 'test_avg_loss_top_decile': np.float64(17.545231), 'test_avg_loss_min': np.float64(12.003327), 'test_avg_loss_max': np.float64(17.545231), 'test_avg_loss_bottom10%': np.float64(12.003327), 'test_avg_loss_top10%': np.float64(17.545231), 'test_avg_loss_cos1': np.float64(0.991548), 'test_avg_loss_entropy': np.float64(2.294108), 'val_loss_std': np.float64(12029.993491), 'val_loss_bottom_decile': np.float64(71486.085121), 'val_loss_top_decile': np.float64(103009.439011), 'val_loss_min': np.float64(69548.445412), 'val_loss_max': np.float64(103009.439011), 'val_loss_bottom10%': np.float64(69548.445412), 'val_loss_top10%': np.float64(103009.439011), 'val_loss_cos1': np.float64(0.990204), 'val_loss_entropy': np.float64(2.292733), 'val_avg_loss_std': np.float64(2.136007), 'val_avg_loss_bottom_decile': np.float64(12.692842), 'val_avg_loss_top_decile': np.float64(18.290028), 'val_avg_loss_min': np.float64(12.348801), 'val_avg_loss_max': np.float64(18.290028), 'val_avg_loss_bottom10%': np.float64(12.348801), 'val_avg_loss_top10%': np.float64(18.290028), 'val_avg_loss_cos1': np.float64(0.990204), 'val_avg_loss_entropy': np.float64(2.292733)}}
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2024-11-15 23:34:40,318 (server:353) INFO: Server: Starting evaluation at the end of round 5.
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2024-11-15 23:34:40,319 (server:359) INFO: ----------- Starting a new training round (Round #6) -------------
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2024-11-15 23:45:30,140 (client:354) INFO: {'Role': 'Client #6', 'Round': 6, 'Results_raw': {'train_loss': 10.448787, 'val_loss': 10.063177, 'test_loss': 9.824486}}
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2024-11-15 23:49:22,224 (client:354) INFO: {'Role': 'Client #5', 'Round': 6, 'Results_raw': {'train_loss': 9.929189, 'val_loss': 9.634282, 'test_loss': 9.450385}}
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2024-11-15 23:53:16,283 (client:354) INFO: {'Role': 'Client #9', 'Round': 6, 'Results_raw': {'train_loss': 10.133099, 'val_loss': 9.818458, 'test_loss': 9.451529}}
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2024-11-15 23:57:07,407 (client:354) INFO: {'Role': 'Client #2', 'Round': 6, 'Results_raw': {'train_loss': 9.312383, 'val_loss': 8.706308, 'test_loss': 8.559953}}
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2024-11-16 00:00:58,431 (client:354) INFO: {'Role': 'Client #4', 'Round': 6, 'Results_raw': {'train_loss': 10.131937, 'val_loss': 10.07042, 'test_loss': 9.43913}}
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2024-11-16 00:04:54,665 (client:354) INFO: {'Role': 'Client #8', 'Round': 6, 'Results_raw': {'train_loss': 9.884089, 'val_loss': 9.688976, 'test_loss': 9.524209}}
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2024-11-16 00:09:00,586 (client:354) INFO: {'Role': 'Client #7', 'Round': 6, 'Results_raw': {'train_loss': 10.345153, 'val_loss': 9.525482, 'test_loss': 9.358889}}
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2024-11-16 00:12:51,451 (client:354) INFO: {'Role': 'Client #1', 'Round': 6, 'Results_raw': {'train_loss': 10.346789, 'val_loss': 9.828312, 'test_loss': 9.447114}}
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2024-11-16 00:16:43,265 (client:354) INFO: {'Role': 'Client #3', 'Round': 6, 'Results_raw': {'train_loss': 10.244649, 'val_loss': 9.583608, 'test_loss': 9.77252}}
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2024-11-16 00:20:42,769 (client:354) INFO: {'Role': 'Client #10', 'Round': 6, 'Results_raw': {'train_loss': 9.780737, 'val_loss': 9.372054, 'test_loss': 9.159771}}
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2024-11-16 00:20:42,775 (server:615) INFO: {'Role': 'Server #', 'Round': 5, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(83622.186764), 'test_avg_loss': np.float64(14.847689), 'val_total': np.float64(5632.0), 'val_loss': np.float64(86578.500429), 'val_avg_loss': np.float64(15.372603)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(83622.186764), 'test_avg_loss': np.float64(14.847689), 'val_total': np.float64(5632.0), 'val_loss': np.float64(86578.500429), 'val_avg_loss': np.float64(15.372603)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(11114.231217), 'test_loss_bottom_decile': np.float64(71131.516098), 'test_loss_top_decile': np.float64(100539.736176), 'test_loss_min': np.float64(69078.934357), 'test_loss_max': np.float64(100539.736176), 'test_loss_bottom10%': np.float64(69078.934357), 'test_loss_top10%': np.float64(100539.736176), 'test_loss_cos1': np.float64(0.991283), 'test_loss_entropy': np.float64(2.293847), 'test_avg_loss_std': np.float64(1.973408), 'test_avg_loss_bottom_decile': np.float64(12.629886), 'test_avg_loss_top_decile': np.float64(17.851516), 'test_avg_loss_min': np.float64(12.265436), 'test_avg_loss_max': np.float64(17.851516), 'test_avg_loss_bottom10%': np.float64(12.265436), 'test_avg_loss_top10%': np.float64(17.851516), 'test_avg_loss_cos1': np.float64(0.991283), 'test_avg_loss_entropy': np.float64(2.293847), 'val_loss_std': np.float64(12352.604103), 'val_loss_bottom_decile': np.float64(71409.434074), 'val_loss_top_decile': np.float64(104809.480103), 'val_loss_min': np.float64(71094.048584), 'val_loss_max': np.float64(104809.480103), 'val_loss_bottom10%': np.float64(71094.048584), 'val_loss_top10%': np.float64(104809.480103), 'val_loss_cos1': np.float64(0.989975), 'val_loss_entropy': np.float64(2.292505), 'val_avg_loss_std': np.float64(2.193289), 'val_avg_loss_bottom_decile': np.float64(12.679232), 'val_avg_loss_top_decile': np.float64(18.609638), 'val_avg_loss_min': np.float64(12.623233), 'val_avg_loss_max': np.float64(18.609638), 'val_avg_loss_bottom10%': np.float64(12.623233), 'val_avg_loss_top10%': np.float64(18.609638), 'val_avg_loss_cos1': np.float64(0.989975), 'val_avg_loss_entropy': np.float64(2.292505)}}
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2024-11-16 00:20:42,829 (server:353) INFO: Server: Starting evaluation at the end of round 6.
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2024-11-16 00:20:42,830 (server:359) INFO: ----------- Starting a new training round (Round #7) -------------
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2024-11-16 00:32:28,939 (client:354) INFO: {'Role': 'Client #8', 'Round': 7, 'Results_raw': {'train_loss': 9.774645, 'val_loss': 9.744537, 'test_loss': 9.588231}}
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2024-11-16 00:36:17,749 (client:354) INFO: {'Role': 'Client #4', 'Round': 7, 'Results_raw': {'train_loss': 10.028927, 'val_loss': 10.16708, 'test_loss': 9.615957}}
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2024-11-16 00:40:13,960 (client:354) INFO: {'Role': 'Client #6', 'Round': 7, 'Results_raw': {'train_loss': 10.347358, 'val_loss': 9.814259, 'test_loss': 9.585942}}
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2024-11-16 00:44:06,264 (client:354) INFO: {'Role': 'Client #10', 'Round': 7, 'Results_raw': {'train_loss': 9.679151, 'val_loss': 9.228008, 'test_loss': 8.991603}}
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2024-11-16 00:47:59,260 (client:354) INFO: {'Role': 'Client #9', 'Round': 7, 'Results_raw': {'train_loss': 9.998298, 'val_loss': 9.91895, 'test_loss': 9.581155}}
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2024-11-16 00:51:53,390 (client:354) INFO: {'Role': 'Client #7', 'Round': 7, 'Results_raw': {'train_loss': 10.221076, 'val_loss': 9.343221, 'test_loss': 9.191922}}
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2024-11-16 00:55:42,855 (client:354) INFO: {'Role': 'Client #1', 'Round': 7, 'Results_raw': {'train_loss': 10.255835, 'val_loss': 9.797911, 'test_loss': 9.421287}}
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2024-11-16 00:59:36,615 (client:354) INFO: {'Role': 'Client #3', 'Round': 7, 'Results_raw': {'train_loss': 10.128151, 'val_loss': 10.108021, 'test_loss': 10.214373}}
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2024-11-16 01:03:29,572 (client:354) INFO: {'Role': 'Client #5', 'Round': 7, 'Results_raw': {'train_loss': 9.830575, 'val_loss': 9.571278, 'test_loss': 9.424776}}
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2024-11-16 01:07:21,878 (client:354) INFO: {'Role': 'Client #2', 'Round': 7, 'Results_raw': {'train_loss': 9.158766, 'val_loss': 8.797552, 'test_loss': 8.643675}}
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2024-11-16 01:07:21,881 (server:615) INFO: {'Role': 'Server #', 'Round': 6, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(83619.991026), 'test_avg_loss': np.float64(14.8473), 'val_total': np.float64(5632.0), 'val_loss': np.float64(86587.190981), 'val_avg_loss': np.float64(15.374146)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(83619.991026), 'test_avg_loss': np.float64(14.8473), 'val_total': np.float64(5632.0), 'val_loss': np.float64(86587.190981), 'val_avg_loss': np.float64(15.374146)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(10861.114426), 'test_loss_bottom_decile': np.float64(70942.2062), 'test_loss_top_decile': np.float64(98903.709015), 'test_loss_min': np.float64(69556.083801), 'test_loss_max': np.float64(98903.709015), 'test_loss_bottom10%': np.float64(69556.083801), 'test_loss_top10%': np.float64(98903.709015), 'test_loss_cos1': np.float64(0.99167), 'test_loss_entropy': np.float64(2.294237), 'test_avg_loss_std': np.float64(1.928465), 'test_avg_loss_bottom_decile': np.float64(12.596272), 'test_avg_loss_top_decile': np.float64(17.561028), 'test_avg_loss_min': np.float64(12.350157), 'test_avg_loss_max': np.float64(17.561028), 'test_avg_loss_bottom10%': np.float64(12.350157), 'test_avg_loss_top10%': np.float64(17.561028), 'test_avg_loss_cos1': np.float64(0.99167), 'test_avg_loss_entropy': np.float64(2.294237), 'val_loss_std': np.float64(12054.422573), 'val_loss_bottom_decile': np.float64(71711.250954), 'val_loss_top_decile': np.float64(103974.318802), 'val_loss_min': np.float64(71158.256195), 'val_loss_max': np.float64(103974.318802), 'val_loss_bottom10%': np.float64(71158.256195), 'val_loss_top10%': np.float64(103974.318802), 'val_loss_cos1': np.float64(0.990448), 'val_loss_entropy': np.float64(2.292981), 'val_avg_loss_std': np.float64(2.140345), 'val_avg_loss_bottom_decile': np.float64(12.732822), 'val_avg_loss_top_decile': np.float64(18.461349), 'val_avg_loss_min': np.float64(12.634634), 'val_avg_loss_max': np.float64(18.461349), 'val_avg_loss_bottom10%': np.float64(12.634634), 'val_avg_loss_top10%': np.float64(18.461349), 'val_avg_loss_cos1': np.float64(0.990448), 'val_avg_loss_entropy': np.float64(2.292981)}}
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2024-11-16 01:07:21,923 (server:353) INFO: Server: Starting evaluation at the end of round 7.
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2024-11-16 01:07:21,923 (server:359) INFO: ----------- Starting a new training round (Round #8) -------------
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2024-11-16 01:18:04,576 (client:354) INFO: {'Role': 'Client #6', 'Round': 8, 'Results_raw': {'train_loss': 10.247128, 'val_loss': 9.839514, 'test_loss': 9.637047}}
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2024-11-16 01:21:53,320 (client:354) INFO: {'Role': 'Client #7', 'Round': 8, 'Results_raw': {'train_loss': 10.09118, 'val_loss': 9.259249, 'test_loss': 9.10617}}
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2024-11-16 01:25:55,381 (client:354) INFO: {'Role': 'Client #2', 'Round': 8, 'Results_raw': {'train_loss': 9.066809, 'val_loss': 8.573149, 'test_loss': 8.426706}}
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2024-11-16 01:29:46,342 (client:354) INFO: {'Role': 'Client #4', 'Round': 8, 'Results_raw': {'train_loss': 9.935055, 'val_loss': 9.899861, 'test_loss': 9.311774}}
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2024-11-16 01:33:36,431 (client:354) INFO: {'Role': 'Client #1', 'Round': 8, 'Results_raw': {'train_loss': 10.133408, 'val_loss': 9.639802, 'test_loss': 9.332148}}
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2024-11-16 01:37:27,080 (client:354) INFO: {'Role': 'Client #8', 'Round': 8, 'Results_raw': {'train_loss': 9.667364, 'val_loss': 9.385851, 'test_loss': 9.263581}}
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2024-11-16 01:41:20,801 (client:354) INFO: {'Role': 'Client #5', 'Round': 8, 'Results_raw': {'train_loss': 9.718405, 'val_loss': 9.381688, 'test_loss': 9.209099}}
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2024-11-16 01:45:14,191 (client:354) INFO: {'Role': 'Client #3', 'Round': 8, 'Results_raw': {'train_loss': 10.03574, 'val_loss': 9.431606, 'test_loss': 9.643684}}
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2024-11-16 01:49:18,499 (client:354) INFO: {'Role': 'Client #10', 'Round': 8, 'Results_raw': {'train_loss': 9.592335, 'val_loss': 9.025878, 'test_loss': 8.828828}}
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2024-11-16 01:53:14,985 (client:354) INFO: {'Role': 'Client #9', 'Round': 8, 'Results_raw': {'train_loss': 9.910009, 'val_loss': 9.584162, 'test_loss': 9.190952}}
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2024-11-16 01:53:14,988 (server:615) INFO: {'Role': 'Server #', 'Round': 7, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(83356.131174), 'test_avg_loss': np.float64(14.800449), 'val_total': np.float64(5632.0), 'val_loss': np.float64(86263.930406), 'val_avg_loss': np.float64(15.316749)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(83356.131174), 'test_avg_loss': np.float64(14.800449), 'val_total': np.float64(5632.0), 'val_loss': np.float64(86263.930406), 'val_avg_loss': np.float64(15.316749)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(10777.314314), 'test_loss_bottom_decile': np.float64(70989.349228), 'test_loss_top_decile': np.float64(98922.225784), 'test_loss_min': np.float64(69615.784775), 'test_loss_max': np.float64(98922.225784), 'test_loss_bottom10%': np.float64(69615.784775), 'test_loss_top10%': np.float64(98922.225784), 'test_loss_cos1': np.float64(0.991745), 'test_loss_entropy': np.float64(2.29433), 'test_avg_loss_std': np.float64(1.913586), 'test_avg_loss_bottom_decile': np.float64(12.604643), 'test_avg_loss_top_decile': np.float64(17.564316), 'test_avg_loss_min': np.float64(12.360757), 'test_avg_loss_max': np.float64(17.564316), 'test_avg_loss_bottom10%': np.float64(12.360757), 'test_avg_loss_top10%': np.float64(17.564316), 'test_avg_loss_cos1': np.float64(0.991745), 'test_avg_loss_entropy': np.float64(2.29433), 'val_loss_std': np.float64(11989.455954), 'val_loss_bottom_decile': np.float64(71711.75872), 'val_loss_top_decile': np.float64(104123.659195), 'val_loss_min': np.float64(71165.050659), 'val_loss_max': np.float64(104123.659195), 'val_loss_bottom10%': np.float64(71165.050659), 'val_loss_top10%': np.float64(104123.659195), 'val_loss_cos1': np.float64(0.990479), 'val_loss_entropy': np.float64(2.293038), 'val_avg_loss_std': np.float64(2.12881), 'val_avg_loss_bottom_decile': np.float64(12.732912), 'val_avg_loss_top_decile': np.float64(18.487866), 'val_avg_loss_min': np.float64(12.63584), 'val_avg_loss_max': np.float64(18.487866), 'val_avg_loss_bottom10%': np.float64(12.63584), 'val_avg_loss_top10%': np.float64(18.487866), 'val_avg_loss_cos1': np.float64(0.990479), 'val_avg_loss_entropy': np.float64(2.293038)}}
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2024-11-16 01:53:15,025 (server:353) INFO: Server: Starting evaluation at the end of round 8.
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2024-11-16 01:53:15,025 (server:359) INFO: ----------- Starting a new training round (Round #9) -------------
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2024-11-16 02:03:56,047 (client:354) INFO: {'Role': 'Client #2', 'Round': 9, 'Results_raw': {'train_loss': 8.950542, 'val_loss': 8.639232, 'test_loss': 8.49391}}
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2024-11-16 02:07:47,066 (client:354) INFO: {'Role': 'Client #9', 'Round': 9, 'Results_raw': {'train_loss': 9.792689, 'val_loss': 9.51687, 'test_loss': 9.134987}}
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2024-11-16 02:11:40,049 (client:354) INFO: {'Role': 'Client #8', 'Round': 9, 'Results_raw': {'train_loss': 9.554315, 'val_loss': 9.397338, 'test_loss': 9.264703}}
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2024-11-16 02:15:31,925 (client:354) INFO: {'Role': 'Client #5', 'Round': 9, 'Results_raw': {'train_loss': 9.591836, 'val_loss': 9.23297, 'test_loss': 9.082583}}
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2024-11-16 02:19:24,868 (client:354) INFO: {'Role': 'Client #1', 'Round': 9, 'Results_raw': {'train_loss': 10.051561, 'val_loss': 9.573495, 'test_loss': 9.234349}}
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2024-11-16 02:23:27,956 (client:354) INFO: {'Role': 'Client #4', 'Round': 9, 'Results_raw': {'train_loss': 9.822318, 'val_loss': 9.765484, 'test_loss': 9.183165}}
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2024-11-16 02:27:27,918 (client:354) INFO: {'Role': 'Client #3', 'Round': 9, 'Results_raw': {'train_loss': 9.934393, 'val_loss': 9.329274, 'test_loss': 9.576901}}
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2024-11-16 02:31:22,135 (client:354) INFO: {'Role': 'Client #6', 'Round': 9, 'Results_raw': {'train_loss': 10.172298, 'val_loss': 9.78217, 'test_loss': 9.5709}}
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2024-11-16 02:35:12,954 (client:354) INFO: {'Role': 'Client #10', 'Round': 9, 'Results_raw': {'train_loss': 9.50641, 'val_loss': 9.085725, 'test_loss': 8.915997}}
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2024-11-16 02:38:52,090 (client:354) INFO: {'Role': 'Client #7', 'Round': 9, 'Results_raw': {'train_loss': 10.035478, 'val_loss': 10.090737, 'test_loss': 9.802887}}
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2024-11-16 02:38:52,093 (server:615) INFO: {'Role': 'Server #', 'Round': 8, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(83008.027909), 'test_avg_loss': np.float64(14.738641), 'val_total': np.float64(5632.0), 'val_loss': np.float64(85818.974063), 'val_avg_loss': np.float64(15.237744)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(83008.027909), 'test_avg_loss': np.float64(14.738641), 'val_total': np.float64(5632.0), 'val_loss': np.float64(85818.974063), 'val_avg_loss': np.float64(15.237744)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(10235.824034), 'test_loss_bottom_decile': np.float64(71106.641861), 'test_loss_top_decile': np.float64(97644.619827), 'test_loss_min': np.float64(69947.867302), 'test_loss_max': np.float64(97644.619827), 'test_loss_bottom10%': np.float64(69947.867302), 'test_loss_top10%': np.float64(97644.619827), 'test_loss_cos1': np.float64(0.992483), 'test_loss_entropy': np.float64(2.295064), 'test_avg_loss_std': np.float64(1.81744), 'test_avg_loss_bottom_decile': np.float64(12.625469), 'test_avg_loss_top_decile': np.float64(17.337468), 'test_avg_loss_min': np.float64(12.419721), 'test_avg_loss_max': np.float64(17.337468), 'test_avg_loss_bottom10%': np.float64(12.419721), 'test_avg_loss_top10%': np.float64(17.337468), 'test_avg_loss_cos1': np.float64(0.992483), 'test_avg_loss_entropy': np.float64(2.295064), 'val_loss_std': np.float64(11348.137194), 'val_loss_bottom_decile': np.float64(72058.416527), 'val_loss_top_decile': np.float64(102721.106644), 'val_loss_min': np.float64(71238.743187), 'val_loss_max': np.float64(102721.106644), 'val_loss_bottom10%': np.float64(71238.743187), 'val_loss_top10%': np.float64(102721.106644), 'val_loss_cos1': np.float64(0.99137), 'val_loss_entropy': np.float64(2.293928), 'val_avg_loss_std': np.float64(2.014939), 'val_avg_loss_bottom_decile': np.float64(12.794463), 'val_avg_loss_top_decile': np.float64(18.238833), 'val_avg_loss_min': np.float64(12.648925), 'val_avg_loss_max': np.float64(18.238833), 'val_avg_loss_bottom10%': np.float64(12.648925), 'val_avg_loss_top10%': np.float64(18.238833), 'val_avg_loss_cos1': np.float64(0.99137), 'val_avg_loss_entropy': np.float64(2.293928)}}
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2024-11-16 02:38:52,139 (server:353) INFO: Server: Starting evaluation at the end of round 9.
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2024-11-16 02:38:52,139 (server:359) INFO: ----------- Starting a new training round (Round #10) -------------
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2024-11-16 02:48:47,468 (client:354) INFO: {'Role': 'Client #6', 'Round': 10, 'Results_raw': {'train_loss': 10.08705, 'val_loss': 9.573212, 'test_loss': 9.341546}}
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2024-11-16 02:52:12,512 (client:354) INFO: {'Role': 'Client #10', 'Round': 10, 'Results_raw': {'train_loss': 9.418891, 'val_loss': 8.910323, 'test_loss': 8.684367}}
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2024-11-16 02:55:39,192 (client:354) INFO: {'Role': 'Client #4', 'Round': 10, 'Results_raw': {'train_loss': 9.769006, 'val_loss': 9.874019, 'test_loss': 9.267125}}
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2024-11-16 02:59:05,192 (client:354) INFO: {'Role': 'Client #9', 'Round': 10, 'Results_raw': {'train_loss': 9.766187, 'val_loss': 9.462001, 'test_loss': 9.051512}}
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2024-11-16 03:02:30,837 (client:354) INFO: {'Role': 'Client #3', 'Round': 10, 'Results_raw': {'train_loss': 9.823638, 'val_loss': 9.410822, 'test_loss': 9.625911}}
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2024-11-16 03:05:53,933 (client:354) INFO: {'Role': 'Client #1', 'Round': 10, 'Results_raw': {'train_loss': 9.954355, 'val_loss': 9.572414, 'test_loss': 9.239275}}
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2024-11-16 03:09:19,752 (client:354) INFO: {'Role': 'Client #5', 'Round': 10, 'Results_raw': {'train_loss': 9.490408, 'val_loss': 9.150589, 'test_loss': 8.992174}}
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2024-11-16 03:12:47,096 (client:354) INFO: {'Role': 'Client #8', 'Round': 10, 'Results_raw': {'train_loss': 9.492031, 'val_loss': 9.380782, 'test_loss': 9.237769}}
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2024-11-16 03:16:14,582 (client:354) INFO: {'Role': 'Client #2', 'Round': 10, 'Results_raw': {'train_loss': 8.886438, 'val_loss': 8.543968, 'test_loss': 8.407721}}
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2024-11-16 03:19:41,090 (client:354) INFO: {'Role': 'Client #7', 'Round': 10, 'Results_raw': {'train_loss': 9.944343, 'val_loss': 9.235014, 'test_loss': 9.084841}}
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2024-11-16 03:19:41,094 (server:615) INFO: {'Role': 'Server #', 'Round': 9, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(82201.198737), 'test_avg_loss': np.float64(14.595383), 'val_total': np.float64(5632.0), 'val_loss': np.float64(84909.830291), 'val_avg_loss': np.float64(15.076319)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(82201.198737), 'test_avg_loss': np.float64(14.595383), 'val_total': np.float64(5632.0), 'val_loss': np.float64(84909.830291), 'val_avg_loss': np.float64(15.076319)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(10233.629786), 'test_loss_bottom_decile': np.float64(70676.918182), 'test_loss_top_decile': np.float64(97962.248474), 'test_loss_min': np.float64(69254.954285), 'test_loss_max': np.float64(97962.248474), 'test_loss_bottom10%': np.float64(69254.954285), 'test_loss_top10%': np.float64(97962.248474), 'test_loss_cos1': np.float64(0.992339), 'test_loss_entropy': np.float64(2.294932), 'test_avg_loss_std': np.float64(1.817051), 'test_avg_loss_bottom_decile': np.float64(12.549169), 'test_avg_loss_top_decile': np.float64(17.393865), 'test_avg_loss_min': np.float64(12.296689), 'test_avg_loss_max': np.float64(17.393865), 'test_avg_loss_bottom10%': np.float64(12.296689), 'test_avg_loss_top10%': np.float64(17.393865), 'test_avg_loss_cos1': np.float64(0.992339), 'test_avg_loss_entropy': np.float64(2.294932), 'val_loss_std': np.float64(11345.350481), 'val_loss_bottom_decile': np.float64(71283.330956), 'val_loss_top_decile': np.float64(102456.782921), 'val_loss_min': np.float64(70788.049629), 'val_loss_max': np.float64(102456.782921), 'val_loss_bottom10%': np.float64(70788.049629), 'val_loss_top10%': np.float64(102456.782921), 'val_loss_cos1': np.float64(0.991191), 'val_loss_entropy': np.float64(2.293767), 'val_avg_loss_std': np.float64(2.014444), 'val_avg_loss_bottom_decile': np.float64(12.656841), 'val_avg_loss_top_decile': np.float64(18.1919), 'val_avg_loss_min': np.float64(12.568901), 'val_avg_loss_max': np.float64(18.1919), 'val_avg_loss_bottom10%': np.float64(12.568901), 'val_avg_loss_top10%': np.float64(18.1919), 'val_avg_loss_cos1': np.float64(0.991191), 'val_avg_loss_entropy': np.float64(2.293767)}}
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2024-11-16 03:19:41,128 (server:353) INFO: Server: Starting evaluation at the end of round 10.
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2024-11-16 03:19:41,128 (server:359) INFO: ----------- Starting a new training round (Round #11) -------------
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2024-11-16 03:30:07,878 (client:354) INFO: {'Role': 'Client #4', 'Round': 11, 'Results_raw': {'train_loss': 9.687046, 'val_loss': 9.715323, 'test_loss': 9.109114}}
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2024-11-16 03:33:58,273 (client:354) INFO: {'Role': 'Client #9', 'Round': 11, 'Results_raw': {'train_loss': 9.660135, 'val_loss': 9.431888, 'test_loss': 9.042128}}
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2024-11-16 03:37:47,034 (client:354) INFO: {'Role': 'Client #7', 'Round': 11, 'Results_raw': {'train_loss': 9.842496, 'val_loss': 9.62287, 'test_loss': 9.400462}}
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2024-11-16 03:41:36,399 (client:354) INFO: {'Role': 'Client #5', 'Round': 11, 'Results_raw': {'train_loss': 9.458015, 'val_loss': 9.22451, 'test_loss': 9.094759}}
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2024-11-16 03:45:23,854 (client:354) INFO: {'Role': 'Client #8', 'Round': 11, 'Results_raw': {'train_loss': 9.429309, 'val_loss': 9.317675, 'test_loss': 9.156957}}
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2024-11-16 03:49:12,523 (client:354) INFO: {'Role': 'Client #10', 'Round': 11, 'Results_raw': {'train_loss': 9.349343, 'val_loss': 9.02374, 'test_loss': 8.831522}}
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2024-11-16 03:53:00,394 (client:354) INFO: {'Role': 'Client #1', 'Round': 11, 'Results_raw': {'train_loss': 9.88113, 'val_loss': 9.445664, 'test_loss': 9.123092}}
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2024-11-16 03:56:54,187 (client:354) INFO: {'Role': 'Client #6', 'Round': 11, 'Results_raw': {'train_loss': 9.989958, 'val_loss': 9.639701, 'test_loss': 9.391535}}
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2024-11-16 04:00:41,331 (client:354) INFO: {'Role': 'Client #3', 'Round': 11, 'Results_raw': {'train_loss': 9.733928, 'val_loss': 9.16977, 'test_loss': 9.406333}}
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2024-11-16 04:04:29,267 (client:354) INFO: {'Role': 'Client #2', 'Round': 11, 'Results_raw': {'train_loss': 8.765504, 'val_loss': 8.5131, 'test_loss': 8.357134}}
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2024-11-16 04:04:29,272 (server:615) INFO: {'Role': 'Server #', 'Round': 10, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(81545.034008), 'test_avg_loss': np.float64(14.478877), 'val_total': np.float64(5632.0), 'val_loss': np.float64(84236.453825), 'val_avg_loss': np.float64(14.956757)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(81545.034008), 'test_avg_loss': np.float64(14.478877), 'val_total': np.float64(5632.0), 'val_loss': np.float64(84236.453825), 'val_avg_loss': np.float64(14.956757)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(9994.641098), 'test_loss_bottom_decile': np.float64(70565.717812), 'test_loss_top_decile': np.float64(97584.64267), 'test_loss_min': np.float64(68772.026077), 'test_loss_max': np.float64(97584.64267), 'test_loss_bottom10%': np.float64(68772.026077), 'test_loss_top10%': np.float64(97584.64267), 'test_loss_cos1': np.float64(0.992572), 'test_loss_entropy': np.float64(2.295165), 'test_avg_loss_std': np.float64(1.774617), 'test_avg_loss_bottom_decile': np.float64(12.529424), 'test_avg_loss_top_decile': np.float64(17.326819), 'test_avg_loss_min': np.float64(12.210942), 'test_avg_loss_max': np.float64(17.326819), 'test_avg_loss_bottom10%': np.float64(12.210942), 'test_avg_loss_top10%': np.float64(17.326819), 'test_avg_loss_cos1': np.float64(0.992572), 'test_avg_loss_entropy': np.float64(2.295165), 'val_loss_std': np.float64(11091.847681), 'val_loss_bottom_decile': np.float64(70756.256897), 'val_loss_top_decile': np.float64(102077.277222), 'val_loss_min': np.float64(70690.427986), 'val_loss_max': np.float64(102077.277222), 'val_loss_bottom10%': np.float64(70690.427986), 'val_loss_top10%': np.float64(102077.277222), 'val_loss_cos1': np.float64(0.991442), 'val_loss_entropy': np.float64(2.294018), 'val_avg_loss_std': np.float64(1.969433), 'val_avg_loss_bottom_decile': np.float64(12.563256), 'val_avg_loss_top_decile': np.float64(18.124517), 'val_avg_loss_min': np.float64(12.551567), 'val_avg_loss_max': np.float64(18.124517), 'val_avg_loss_bottom10%': np.float64(12.551567), 'val_avg_loss_top10%': np.float64(18.124517), 'val_avg_loss_cos1': np.float64(0.991442), 'val_avg_loss_entropy': np.float64(2.294018)}}
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2024-11-16 04:04:29,309 (server:353) INFO: Server: Starting evaluation at the end of round 11.
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2024-11-16 04:04:29,310 (server:359) INFO: ----------- Starting a new training round (Round #12) -------------
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2024-11-16 04:15:20,333 (client:354) INFO: {'Role': 'Client #9', 'Round': 12, 'Results_raw': {'train_loss': 9.58692, 'val_loss': 9.561641, 'test_loss': 9.124526}}
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2024-11-16 04:19:17,080 (client:354) INFO: {'Role': 'Client #2', 'Round': 12, 'Results_raw': {'train_loss': 8.712732, 'val_loss': 8.366753, 'test_loss': 8.258223}}
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2024-11-16 04:23:06,247 (client:354) INFO: {'Role': 'Client #10', 'Round': 12, 'Results_raw': {'train_loss': 9.290835, 'val_loss': 8.825823, 'test_loss': 8.59407}}
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2024-11-16 04:27:04,079 (client:354) INFO: {'Role': 'Client #7', 'Round': 12, 'Results_raw': {'train_loss': 9.786301, 'val_loss': 9.192188, 'test_loss': 9.042196}}
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2024-11-16 04:31:10,463 (client:354) INFO: {'Role': 'Client #3', 'Round': 12, 'Results_raw': {'train_loss': 9.634057, 'val_loss': 9.250747, 'test_loss': 9.466877}}
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2024-11-16 04:34:54,571 (client:354) INFO: {'Role': 'Client #5', 'Round': 12, 'Results_raw': {'train_loss': 9.374234, 'val_loss': 9.122865, 'test_loss': 9.02824}}
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2024-11-16 04:38:42,562 (client:354) INFO: {'Role': 'Client #1', 'Round': 12, 'Results_raw': {'train_loss': 9.809476, 'val_loss': 9.333143, 'test_loss': 9.002977}}
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2024-11-16 04:42:31,182 (client:354) INFO: {'Role': 'Client #6', 'Round': 12, 'Results_raw': {'train_loss': 9.928187, 'val_loss': 9.54826, 'test_loss': 9.309309}}
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2024-11-16 04:46:21,886 (client:354) INFO: {'Role': 'Client #4', 'Round': 12, 'Results_raw': {'train_loss': 9.603878, 'val_loss': 9.587582, 'test_loss': 8.972783}}
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2024-11-16 04:50:11,706 (client:354) INFO: {'Role': 'Client #8', 'Round': 12, 'Results_raw': {'train_loss': 9.365343, 'val_loss': 9.278686, 'test_loss': 9.155241}}
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2024-11-16 04:50:11,709 (server:615) INFO: {'Role': 'Server #', 'Round': 11, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(79367.61103), 'test_avg_loss': np.float64(14.09226), 'val_total': np.float64(5632.0), 'val_loss': np.float64(81841.780817), 'val_avg_loss': np.float64(14.531566)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(79367.61103), 'test_avg_loss': np.float64(14.09226), 'val_total': np.float64(5632.0), 'val_loss': np.float64(81841.780817), 'val_avg_loss': np.float64(14.531566)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(9526.715254), 'test_loss_bottom_decile': np.float64(68863.469666), 'test_loss_top_decile': np.float64(95492.205849), 'test_loss_min': np.float64(68053.776886), 'test_loss_max': np.float64(95492.205849), 'test_loss_bottom10%': np.float64(68053.776886), 'test_loss_top10%': np.float64(95492.205849), 'test_loss_cos1': np.float64(0.992873), 'test_loss_entropy': np.float64(2.295488), 'test_avg_loss_std': np.float64(1.691533), 'test_avg_loss_bottom_decile': np.float64(12.227179), 'test_avg_loss_top_decile': np.float64(16.955292), 'test_avg_loss_min': np.float64(12.083412), 'test_avg_loss_max': np.float64(16.955292), 'test_avg_loss_bottom10%': np.float64(12.083412), 'test_avg_loss_top10%': np.float64(16.955292), 'test_avg_loss_cos1': np.float64(0.992873), 'test_avg_loss_entropy': np.float64(2.295488), 'val_loss_std': np.float64(10512.112855), 'val_loss_bottom_decile': np.float64(69948.298103), 'val_loss_top_decile': np.float64(99500.349907), 'val_loss_min': np.float64(68930.329086), 'val_loss_max': np.float64(99500.349907), 'val_loss_bottom10%': np.float64(68930.329086), 'val_loss_top10%': np.float64(99500.349907), 'val_loss_cos1': np.float64(0.991852), 'val_loss_entropy': np.float64(2.294449), 'val_avg_loss_std': np.float64(1.866497), 'val_avg_loss_bottom_decile': np.float64(12.419797), 'val_avg_loss_top_decile': np.float64(17.666966), 'val_avg_loss_min': np.float64(12.23905), 'val_avg_loss_max': np.float64(17.666966), 'val_avg_loss_bottom10%': np.float64(12.23905), 'val_avg_loss_top10%': np.float64(17.666966), 'val_avg_loss_cos1': np.float64(0.991852), 'val_avg_loss_entropy': np.float64(2.294449)}}
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2024-11-16 04:50:11,745 (server:353) INFO: Server: Starting evaluation at the end of round 12.
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2024-11-16 04:50:11,745 (server:359) INFO: ----------- Starting a new training round (Round #13) -------------
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2024-11-16 05:00:53,565 (client:354) INFO: {'Role': 'Client #10', 'Round': 13, 'Results_raw': {'train_loss': 9.211396, 'val_loss': 8.819894, 'test_loss': 8.594118}}
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2024-11-16 05:04:39,091 (client:354) INFO: {'Role': 'Client #5', 'Round': 13, 'Results_raw': {'train_loss': 9.336855, 'val_loss': 9.305242, 'test_loss': 9.140666}}
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2024-11-16 05:08:23,041 (client:354) INFO: {'Role': 'Client #7', 'Round': 13, 'Results_raw': {'train_loss': 9.739366, 'val_loss': 9.000526, 'test_loss': 8.870499}}
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2024-11-16 05:12:10,247 (client:354) INFO: {'Role': 'Client #8', 'Round': 13, 'Results_raw': {'train_loss': 9.328588, 'val_loss': 9.2151, 'test_loss': 9.075838}}
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2024-11-16 05:16:02,493 (client:354) INFO: {'Role': 'Client #1', 'Round': 13, 'Results_raw': {'train_loss': 9.745975, 'val_loss': 9.325589, 'test_loss': 8.972582}}
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2024-11-16 05:19:51,866 (client:354) INFO: {'Role': 'Client #9', 'Round': 13, 'Results_raw': {'train_loss': 9.545249, 'val_loss': 9.454025, 'test_loss': 8.988233}}
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2024-11-16 05:23:37,758 (client:354) INFO: {'Role': 'Client #6', 'Round': 13, 'Results_raw': {'train_loss': 9.838927, 'val_loss': 9.503715, 'test_loss': 9.278493}}
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2024-11-16 05:27:22,687 (client:354) INFO: {'Role': 'Client #3', 'Round': 13, 'Results_raw': {'train_loss': 9.586352, 'val_loss': 9.16985, 'test_loss': 9.396702}}
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2024-11-16 05:31:23,237 (client:354) INFO: {'Role': 'Client #2', 'Round': 13, 'Results_raw': {'train_loss': 8.648522, 'val_loss': 8.719376, 'test_loss': 8.541813}}
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2024-11-16 05:35:26,441 (client:354) INFO: {'Role': 'Client #4', 'Round': 13, 'Results_raw': {'train_loss': 9.561349, 'val_loss': 9.555288, 'test_loss': 8.905924}}
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2024-11-16 05:35:26,446 (server:615) INFO: {'Role': 'Server #', 'Round': 12, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(79099.388416), 'test_avg_loss': np.float64(14.044636), 'val_total': np.float64(5632.0), 'val_loss': np.float64(81555.55802), 'val_avg_loss': np.float64(14.480745)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(79099.388416), 'test_avg_loss': np.float64(14.044636), 'val_total': np.float64(5632.0), 'val_loss': np.float64(81555.55802), 'val_avg_loss': np.float64(14.480745)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(9644.555215), 'test_loss_bottom_decile': np.float64(68749.004028), 'test_loss_top_decile': np.float64(96195.923286), 'test_loss_min': np.float64(67881.154877), 'test_loss_max': np.float64(96195.923286), 'test_loss_bottom10%': np.float64(67881.154877), 'test_loss_top10%': np.float64(96195.923286), 'test_loss_cos1': np.float64(0.992648), 'test_loss_entropy': np.float64(2.29528), 'test_avg_loss_std': np.float64(1.712457), 'test_avg_loss_bottom_decile': np.float64(12.206854), 'test_avg_loss_top_decile': np.float64(17.080242), 'test_avg_loss_min': np.float64(12.052762), 'test_avg_loss_max': np.float64(17.080242), 'test_avg_loss_bottom10%': np.float64(12.052762), 'test_avg_loss_top10%': np.float64(17.080242), 'test_avg_loss_cos1': np.float64(0.992648), 'test_avg_loss_entropy': np.float64(2.29528), 'val_loss_std': np.float64(10645.780499), 'val_loss_bottom_decile': np.float64(69804.712822), 'val_loss_top_decile': np.float64(100203.914398), 'val_loss_min': np.float64(68791.808708), 'val_loss_max': np.float64(100203.914398), 'val_loss_bottom10%': np.float64(68791.808708), 'val_loss_top10%': np.float64(100203.914398), 'val_loss_cos1': np.float64(0.991588), 'val_loss_entropy': np.float64(2.294204), 'val_avg_loss_std': np.float64(1.890231), 'val_avg_loss_bottom_decile': np.float64(12.394303), 'val_avg_loss_top_decile': np.float64(17.791888), 'val_avg_loss_min': np.float64(12.214455), 'val_avg_loss_max': np.float64(17.791888), 'val_avg_loss_bottom10%': np.float64(12.214455), 'val_avg_loss_top10%': np.float64(17.791888), 'val_avg_loss_cos1': np.float64(0.991588), 'val_avg_loss_entropy': np.float64(2.294204)}}
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2024-11-16 05:35:26,480 (server:353) INFO: Server: Starting evaluation at the end of round 13.
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2024-11-16 05:35:26,480 (server:359) INFO: ----------- Starting a new training round (Round #14) -------------
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2024-11-16 05:46:08,581 (client:354) INFO: {'Role': 'Client #9', 'Round': 14, 'Results_raw': {'train_loss': 9.496819, 'val_loss': 9.596501, 'test_loss': 9.144053}}
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2024-11-16 05:50:01,113 (client:354) INFO: {'Role': 'Client #4', 'Round': 14, 'Results_raw': {'train_loss': 9.515477, 'val_loss': 9.731904, 'test_loss': 9.084226}}
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2024-11-16 05:53:52,283 (client:354) INFO: {'Role': 'Client #8', 'Round': 14, 'Results_raw': {'train_loss': 9.250285, 'val_loss': 9.341506, 'test_loss': 9.205833}}
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2024-11-16 05:57:45,461 (client:354) INFO: {'Role': 'Client #6', 'Round': 14, 'Results_raw': {'train_loss': 9.785571, 'val_loss': 9.580005, 'test_loss': 9.320883}}
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2024-11-16 06:01:51,870 (client:354) INFO: {'Role': 'Client #7', 'Round': 14, 'Results_raw': {'train_loss': 9.654194, 'val_loss': 9.090008, 'test_loss': 8.934971}}
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2024-11-16 06:05:59,428 (client:354) INFO: {'Role': 'Client #5', 'Round': 14, 'Results_raw': {'train_loss': 9.229882, 'val_loss': 9.034827, 'test_loss': 8.886121}}
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2024-11-16 06:09:36,948 (client:354) INFO: {'Role': 'Client #3', 'Round': 14, 'Results_raw': {'train_loss': 9.501896, 'val_loss': 8.943027, 'test_loss': 9.177187}}
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2024-11-16 06:13:20,346 (client:354) INFO: {'Role': 'Client #10', 'Round': 14, 'Results_raw': {'train_loss': 9.170799, 'val_loss': 8.758698, 'test_loss': 8.551911}}
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2024-11-16 06:17:13,736 (client:354) INFO: {'Role': 'Client #1', 'Round': 14, 'Results_raw': {'train_loss': 9.676504, 'val_loss': 9.316428, 'test_loss': 8.963453}}
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2024-11-16 06:20:54,971 (client:354) INFO: {'Role': 'Client #2', 'Round': 14, 'Results_raw': {'train_loss': 8.572602, 'val_loss': 8.469327, 'test_loss': 8.345224}}
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2024-11-16 06:20:54,974 (server:615) INFO: {'Role': 'Server #', 'Round': 13, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(79661.217912), 'test_avg_loss': np.float64(14.144392), 'val_total': np.float64(5632.0), 'val_loss': np.float64(82213.174148), 'val_avg_loss': np.float64(14.59751)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(79661.217912), 'test_avg_loss': np.float64(14.144392), 'val_total': np.float64(5632.0), 'val_loss': np.float64(82213.174148), 'val_avg_loss': np.float64(14.59751)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(9861.407799), 'test_loss_bottom_decile': np.float64(69171.968414), 'test_loss_top_decile': np.float64(97664.694443), 'test_loss_min': np.float64(68600.390358), 'test_loss_max': np.float64(97664.694443), 'test_loss_bottom10%': np.float64(68600.390358), 'test_loss_top10%': np.float64(97664.694443), 'test_loss_cos1': np.float64(0.992425), 'test_loss_entropy': np.float64(2.295076), 'test_avg_loss_std': np.float64(1.75096), 'test_avg_loss_bottom_decile': np.float64(12.281955), 'test_avg_loss_top_decile': np.float64(17.341032), 'test_avg_loss_min': np.float64(12.180467), 'test_avg_loss_max': np.float64(17.341032), 'test_avg_loss_bottom10%': np.float64(12.180467), 'test_avg_loss_top10%': np.float64(17.341032), 'test_avg_loss_cos1': np.float64(0.992425), 'test_avg_loss_entropy': np.float64(2.295076), 'val_loss_std': np.float64(10927.142342), 'val_loss_bottom_decile': np.float64(70542.8685), 'val_loss_top_decile': np.float64(102065.810371), 'val_loss_min': np.float64(69178.262939), 'val_loss_max': np.float64(102065.810371), 'val_loss_bottom10%': np.float64(69178.262939), 'val_loss_top10%': np.float64(102065.810371), 'val_loss_cos1': np.float64(0.991282), 'val_loss_entropy': np.float64(2.293922), 'val_avg_loss_std': np.float64(1.940189), 'val_avg_loss_bottom_decile': np.float64(12.525367), 'val_avg_loss_top_decile': np.float64(18.122481), 'val_avg_loss_min': np.float64(12.283072), 'val_avg_loss_max': np.float64(18.122481), 'val_avg_loss_bottom10%': np.float64(12.283072), 'val_avg_loss_top10%': np.float64(18.122481), 'val_avg_loss_cos1': np.float64(0.991282), 'val_avg_loss_entropy': np.float64(2.293922)}}
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2024-11-16 06:20:55,011 (server:353) INFO: Server: Starting evaluation at the end of round 14.
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2024-11-16 06:20:55,011 (server:359) INFO: ----------- Starting a new training round (Round #15) -------------
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2024-11-16 06:31:10,833 (client:354) INFO: {'Role': 'Client #7', 'Round': 15, 'Results_raw': {'train_loss': 9.611811, 'val_loss': 9.055809, 'test_loss': 8.92852}}
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2024-11-16 06:35:04,933 (client:354) INFO: {'Role': 'Client #3', 'Round': 15, 'Results_raw': {'train_loss': 9.487493, 'val_loss': 9.623863, 'test_loss': 9.811385}}
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2024-11-16 06:38:39,793 (client:354) INFO: {'Role': 'Client #4', 'Round': 15, 'Results_raw': {'train_loss': 9.447739, 'val_loss': 9.493347, 'test_loss': 8.841207}}
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2024-11-16 06:42:13,887 (client:354) INFO: {'Role': 'Client #5', 'Round': 15, 'Results_raw': {'train_loss': 9.223987, 'val_loss': 9.218284, 'test_loss': 9.113916}}
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2024-11-16 06:45:49,594 (client:354) INFO: {'Role': 'Client #9', 'Round': 15, 'Results_raw': {'train_loss': 9.44686, 'val_loss': 9.263246, 'test_loss': 8.874459}}
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2024-11-16 06:49:25,185 (client:354) INFO: {'Role': 'Client #2', 'Round': 15, 'Results_raw': {'train_loss': 8.546198, 'val_loss': 8.40746, 'test_loss': 8.272931}}
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2024-11-16 06:52:59,672 (client:354) INFO: {'Role': 'Client #6', 'Round': 15, 'Results_raw': {'train_loss': 9.76005, 'val_loss': 9.605453, 'test_loss': 9.357871}}
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2024-11-16 06:56:32,118 (client:354) INFO: {'Role': 'Client #8', 'Round': 15, 'Results_raw': {'train_loss': 9.222143, 'val_loss': 9.397005, 'test_loss': 9.23416}}
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2024-11-16 07:00:04,253 (client:354) INFO: {'Role': 'Client #10', 'Round': 15, 'Results_raw': {'train_loss': 9.120929, 'val_loss': 8.756092, 'test_loss': 8.559809}}
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2024-11-16 07:03:36,103 (client:354) INFO: {'Role': 'Client #1', 'Round': 15, 'Results_raw': {'train_loss': 9.65343, 'val_loss': 9.362818, 'test_loss': 9.032441}}
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2024-11-16 07:03:36,106 (server:615) INFO: {'Role': 'Server #', 'Round': 14, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(77266.354768), 'test_avg_loss': np.float64(13.719168), 'val_total': np.float64(5632.0), 'val_loss': np.float64(79513.12191), 'val_avg_loss': np.float64(14.118097)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(77266.354768), 'test_avg_loss': np.float64(13.719168), 'val_total': np.float64(5632.0), 'val_loss': np.float64(79513.12191), 'val_avg_loss': np.float64(14.118097)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(8838.490096), 'test_loss_bottom_decile': np.float64(67998.871765), 'test_loss_top_decile': np.float64(93082.504906), 'test_loss_min': np.float64(67528.167946), 'test_loss_max': np.float64(93082.504906), 'test_loss_bottom10%': np.float64(67528.167946), 'test_loss_top10%': np.float64(93082.504906), 'test_loss_cos1': np.float64(0.993521), 'test_loss_entropy': np.float64(2.296166), 'test_avg_loss_std': np.float64(1.569334), 'test_avg_loss_bottom_decile': np.float64(12.073663), 'test_avg_loss_top_decile': np.float64(16.527433), 'test_avg_loss_min': np.float64(11.990087), 'test_avg_loss_max': np.float64(16.527433), 'test_avg_loss_bottom10%': np.float64(11.990087), 'test_avg_loss_top10%': np.float64(16.527433), 'test_avg_loss_cos1': np.float64(0.993521), 'test_avg_loss_entropy': np.float64(2.296166), 'val_loss_std': np.float64(9721.15866), 'val_loss_bottom_decile': np.float64(69363.946861), 'val_loss_top_decile': np.float64(96709.454338), 'val_loss_min': np.float64(68072.24147), 'val_loss_max': np.float64(96709.454338), 'val_loss_bottom10%': np.float64(68072.24147), 'val_loss_top10%': np.float64(96709.454338), 'val_loss_cos1': np.float64(0.992609), 'val_loss_entropy': np.float64(2.295243), 'val_avg_loss_std': np.float64(1.726058), 'val_avg_loss_bottom_decile': np.float64(12.316042), 'val_avg_loss_top_decile': np.float64(17.171423), 'val_avg_loss_min': np.float64(12.086691), 'val_avg_loss_max': np.float64(17.171423), 'val_avg_loss_bottom10%': np.float64(12.086691), 'val_avg_loss_top10%': np.float64(17.171423), 'val_avg_loss_cos1': np.float64(0.992609), 'val_avg_loss_entropy': np.float64(2.295243)}}
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2024-11-16 07:03:36,145 (server:353) INFO: Server: Starting evaluation at the end of round 15.
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2024-11-16 07:03:36,145 (server:359) INFO: ----------- Starting a new training round (Round #16) -------------
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2024-11-16 07:13:45,197 (client:354) INFO: {'Role': 'Client #2', 'Round': 16, 'Results_raw': {'train_loss': 8.499218, 'val_loss': 8.513466, 'test_loss': 8.36285}}
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2024-11-16 07:17:19,195 (client:354) INFO: {'Role': 'Client #9', 'Round': 16, 'Results_raw': {'train_loss': 9.41034, 'val_loss': 9.184785, 'test_loss': 8.805594}}
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2024-11-16 07:20:54,007 (client:354) INFO: {'Role': 'Client #5', 'Round': 16, 'Results_raw': {'train_loss': 9.162641, 'val_loss': 8.930046, 'test_loss': 8.809183}}
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2024-11-16 07:24:25,293 (client:354) INFO: {'Role': 'Client #8', 'Round': 16, 'Results_raw': {'train_loss': 9.173705, 'val_loss': 9.091929, 'test_loss': 8.996253}}
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2024-11-16 07:27:57,425 (client:354) INFO: {'Role': 'Client #6', 'Round': 16, 'Results_raw': {'train_loss': 9.708639, 'val_loss': 9.775708, 'test_loss': 9.539683}}
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2024-11-16 07:31:30,614 (client:354) INFO: {'Role': 'Client #10', 'Round': 16, 'Results_raw': {'train_loss': 9.065488, 'val_loss': 8.746005, 'test_loss': 8.541031}}
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2024-11-16 07:35:04,714 (client:354) INFO: {'Role': 'Client #4', 'Round': 16, 'Results_raw': {'train_loss': 9.389918, 'val_loss': 9.719415, 'test_loss': 9.029469}}
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2024-11-16 07:38:40,077 (client:354) INFO: {'Role': 'Client #3', 'Round': 16, 'Results_raw': {'train_loss': 9.392769, 'val_loss': 8.983262, 'test_loss': 9.215623}}
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2024-11-16 07:42:18,203 (client:354) INFO: {'Role': 'Client #1', 'Round': 16, 'Results_raw': {'train_loss': 9.604151, 'val_loss': 9.230621, 'test_loss': 8.912843}}
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2024-11-16 07:45:52,177 (client:354) INFO: {'Role': 'Client #7', 'Round': 16, 'Results_raw': {'train_loss': 9.560034, 'val_loss': 9.017348, 'test_loss': 8.907308}}
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2024-11-16 07:45:52,181 (server:615) INFO: {'Role': 'Server #', 'Round': 15, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(77316.436716), 'test_avg_loss': np.float64(13.72806), 'val_total': np.float64(5632.0), 'val_loss': np.float64(79640.127263), 'val_avg_loss': np.float64(14.140648)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(77316.436716), 'test_avg_loss': np.float64(13.72806), 'val_total': np.float64(5632.0), 'val_loss': np.float64(79640.127263), 'val_avg_loss': np.float64(14.140648)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(8850.494502), 'test_loss_bottom_decile': np.float64(67687.897171), 'test_loss_top_decile': np.float64(93131.229927), 'test_loss_min': np.float64(67629.109283), 'test_loss_max': np.float64(93131.229927), 'test_loss_bottom10%': np.float64(67629.109283), 'test_loss_top10%': np.float64(93131.229927), 'test_loss_cos1': np.float64(0.993512), 'test_loss_entropy': np.float64(2.296159), 'test_avg_loss_std': np.float64(1.571466), 'test_avg_loss_bottom_decile': np.float64(12.018448), 'test_avg_loss_top_decile': np.float64(16.536085), 'test_avg_loss_min': np.float64(12.008009), 'test_avg_loss_max': np.float64(16.536085), 'test_avg_loss_bottom10%': np.float64(12.008009), 'test_avg_loss_top10%': np.float64(16.536085), 'test_avg_loss_cos1': np.float64(0.993512), 'test_avg_loss_entropy': np.float64(2.296159), 'val_loss_std': np.float64(9747.33081), 'val_loss_bottom_decile': np.float64(69484.303207), 'val_loss_top_decile': np.float64(96957.201965), 'val_loss_min': np.float64(67830.494827), 'val_loss_max': np.float64(96957.201965), 'val_loss_bottom10%': np.float64(67830.494827), 'val_loss_top10%': np.float64(96957.201965), 'val_loss_cos1': np.float64(0.992593), 'val_loss_entropy': np.float64(2.295232), 'val_avg_loss_std': np.float64(1.730705), 'val_avg_loss_bottom_decile': np.float64(12.337412), 'val_avg_loss_top_decile': np.float64(17.215412), 'val_avg_loss_min': np.float64(12.043767), 'val_avg_loss_max': np.float64(17.215412), 'val_avg_loss_bottom10%': np.float64(12.043767), 'val_avg_loss_top10%': np.float64(17.215412), 'val_avg_loss_cos1': np.float64(0.992593), 'val_avg_loss_entropy': np.float64(2.295232)}}
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2024-11-16 07:45:52,221 (server:353) INFO: Server: Starting evaluation at the end of round 16.
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2024-11-16 07:45:52,222 (server:359) INFO: ----------- Starting a new training round (Round #17) -------------
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2024-11-16 07:56:07,991 (client:354) INFO: {'Role': 'Client #10', 'Round': 17, 'Results_raw': {'train_loss': 9.028856, 'val_loss': 8.662155, 'test_loss': 8.43216}}
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2024-11-16 07:59:42,440 (client:354) INFO: {'Role': 'Client #5', 'Round': 17, 'Results_raw': {'train_loss': 9.144337, 'val_loss': 8.997076, 'test_loss': 8.852094}}
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2024-11-16 08:03:16,400 (client:354) INFO: {'Role': 'Client #3', 'Round': 17, 'Results_raw': {'train_loss': 9.335266, 'val_loss': 9.091428, 'test_loss': 9.301648}}
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2024-11-16 08:06:51,680 (client:354) INFO: {'Role': 'Client #7', 'Round': 17, 'Results_raw': {'train_loss': 9.475971, 'val_loss': 8.841244, 'test_loss': 8.738464}}
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2024-11-16 08:10:25,195 (client:354) INFO: {'Role': 'Client #2', 'Round': 17, 'Results_raw': {'train_loss': 8.440341, 'val_loss': 8.120308, 'test_loss': 8.000714}}
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2024-11-16 08:13:57,959 (client:354) INFO: {'Role': 'Client #4', 'Round': 17, 'Results_raw': {'train_loss': 9.391502, 'val_loss': 9.459346, 'test_loss': 8.792597}}
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2024-11-16 08:17:31,187 (client:354) INFO: {'Role': 'Client #8', 'Round': 17, 'Results_raw': {'train_loss': 9.140474, 'val_loss': 9.175347, 'test_loss': 9.072749}}
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2024-11-16 08:21:04,382 (client:354) INFO: {'Role': 'Client #9', 'Round': 17, 'Results_raw': {'train_loss': 9.367027, 'val_loss': 9.170975, 'test_loss': 8.808824}}
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2024-11-16 08:24:37,665 (client:354) INFO: {'Role': 'Client #6', 'Round': 17, 'Results_raw': {'train_loss': 9.65303, 'val_loss': 9.299711, 'test_loss': 9.088414}}
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2024-11-16 08:28:10,950 (client:354) INFO: {'Role': 'Client #1', 'Round': 17, 'Results_raw': {'train_loss': 9.514651, 'val_loss': 9.200095, 'test_loss': 8.855974}}
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2024-11-16 08:28:10,953 (server:615) INFO: {'Role': 'Server #', 'Round': 16, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(77309.910742), 'test_avg_loss': np.float64(13.726902), 'val_total': np.float64(5632.0), 'val_loss': np.float64(79642.173736), 'val_avg_loss': np.float64(14.141011)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(77309.910742), 'test_avg_loss': np.float64(13.726902), 'val_total': np.float64(5632.0), 'val_loss': np.float64(79642.173736), 'val_avg_loss': np.float64(14.141011)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(8604.302669), 'test_loss_bottom_decile': np.float64(68052.48262), 'test_loss_top_decile': np.float64(92245.553764), 'test_loss_min': np.float64(67834.54911), 'test_loss_max': np.float64(92245.553764), 'test_loss_bottom10%': np.float64(67834.54911), 'test_loss_top10%': np.float64(92245.553764), 'test_loss_cos1': np.float64(0.993864), 'test_loss_entropy': np.float64(2.296505), 'test_avg_loss_std': np.float64(1.527753), 'test_avg_loss_bottom_decile': np.float64(12.083182), 'test_avg_loss_top_decile': np.float64(16.378827), 'test_avg_loss_min': np.float64(12.044487), 'test_avg_loss_max': np.float64(16.378827), 'test_avg_loss_bottom10%': np.float64(12.044487), 'test_avg_loss_top10%': np.float64(16.378827), 'test_avg_loss_cos1': np.float64(0.993864), 'test_avg_loss_entropy': np.float64(2.296505), 'val_loss_std': np.float64(9523.491115), 'val_loss_bottom_decile': np.float64(69937.270706), 'val_loss_top_decile': np.float64(96120.235611), 'val_loss_min': np.float64(67871.172691), 'val_loss_max': np.float64(96120.235611), 'val_loss_bottom10%': np.float64(67871.172691), 'val_loss_top10%': np.float64(96120.235611), 'val_loss_cos1': np.float64(0.992926), 'val_loss_entropy': np.float64(2.295559), 'val_avg_loss_std': np.float64(1.690961), 'val_avg_loss_bottom_decile': np.float64(12.417839), 'val_avg_loss_top_decile': np.float64(17.066803), 'val_avg_loss_min': np.float64(12.050989), 'val_avg_loss_max': np.float64(17.066803), 'val_avg_loss_bottom10%': np.float64(12.050989), 'val_avg_loss_top10%': np.float64(17.066803), 'val_avg_loss_cos1': np.float64(0.992926), 'val_avg_loss_entropy': np.float64(2.295559)}}
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2024-11-16 08:28:10,985 (server:353) INFO: Server: Starting evaluation at the end of round 17.
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2024-11-16 08:28:10,986 (server:359) INFO: ----------- Starting a new training round (Round #18) -------------
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2024-11-16 08:38:45,393 (client:354) INFO: {'Role': 'Client #3', 'Round': 18, 'Results_raw': {'train_loss': 9.347938, 'val_loss': 8.967697, 'test_loss': 9.136672}}
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2024-11-16 08:42:21,200 (client:354) INFO: {'Role': 'Client #5', 'Round': 18, 'Results_raw': {'train_loss': 9.072812, 'val_loss': 9.060579, 'test_loss': 8.954223}}
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2024-11-16 08:45:54,072 (client:354) INFO: {'Role': 'Client #7', 'Round': 18, 'Results_raw': {'train_loss': 9.472322, 'val_loss': 8.79217, 'test_loss': 8.70327}}
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2024-11-16 08:49:38,476 (client:354) INFO: {'Role': 'Client #9', 'Round': 18, 'Results_raw': {'train_loss': 9.324991, 'val_loss': 9.331066, 'test_loss': 8.947538}}
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2024-11-16 08:56:32,216 (client:354) INFO: {'Role': 'Client #10', 'Round': 18, 'Results_raw': {'train_loss': 8.980529, 'val_loss': 8.677357, 'test_loss': 8.491513}}
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2024-11-16 09:03:39,308 (client:354) INFO: {'Role': 'Client #1', 'Round': 18, 'Results_raw': {'train_loss': 9.493553, 'val_loss': 9.175373, 'test_loss': 8.844747}}
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2024-11-16 09:10:51,521 (client:354) INFO: {'Role': 'Client #4', 'Round': 18, 'Results_raw': {'train_loss': 9.326943, 'val_loss': 9.354492, 'test_loss': 8.747233}}
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2024-11-16 09:17:45,170 (client:354) INFO: {'Role': 'Client #8', 'Round': 18, 'Results_raw': {'train_loss': 9.0833, 'val_loss': 9.039457, 'test_loss': 8.92691}}
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2024-11-16 09:24:49,273 (client:354) INFO: {'Role': 'Client #2', 'Round': 18, 'Results_raw': {'train_loss': 8.407265, 'val_loss': 8.153396, 'test_loss': 8.009613}}
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2024-11-16 09:31:13,015 (client:354) INFO: {'Role': 'Client #6', 'Round': 18, 'Results_raw': {'train_loss': 9.613879, 'val_loss': 9.290468, 'test_loss': 9.069644}}
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2024-11-16 09:31:13,046 (server:615) INFO: {'Role': 'Server #', 'Round': 17, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(76859.2722), 'test_avg_loss': np.float64(13.646888), 'val_total': np.float64(5632.0), 'val_loss': np.float64(79088.401159), 'val_avg_loss': np.float64(14.042685)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(76859.2722), 'test_avg_loss': np.float64(13.646888), 'val_total': np.float64(5632.0), 'val_loss': np.float64(79088.401159), 'val_avg_loss': np.float64(14.042685)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(8473.188019), 'test_loss_bottom_decile': np.float64(67714.117218), 'test_loss_top_decile': np.float64(91613.113403), 'test_loss_min': np.float64(67574.274246), 'test_loss_max': np.float64(91613.113403), 'test_loss_bottom10%': np.float64(67574.274246), 'test_loss_top10%': np.float64(91613.113403), 'test_loss_cos1': np.float64(0.993978), 'test_loss_entropy': np.float64(2.296618), 'test_avg_loss_std': np.float64(1.504472), 'test_avg_loss_bottom_decile': np.float64(12.023103), 'test_avg_loss_top_decile': np.float64(16.266533), 'test_avg_loss_min': np.float64(11.998273), 'test_avg_loss_max': np.float64(16.266533), 'test_avg_loss_bottom10%': np.float64(11.998273), 'test_avg_loss_top10%': np.float64(16.266533), 'test_avg_loss_cos1': np.float64(0.993978), 'test_avg_loss_entropy': np.float64(2.296618), 'val_loss_std': np.float64(9312.827352), 'val_loss_bottom_decile': np.float64(69566.827759), 'val_loss_top_decile': np.float64(95280.227638), 'val_loss_min': np.float64(67607.451027), 'val_loss_max': np.float64(95280.227638), 'val_loss_bottom10%': np.float64(67607.451027), 'val_loss_top10%': np.float64(95280.227638), 'val_loss_cos1': np.float64(0.993138), 'val_loss_entropy': np.float64(2.295768), 'val_avg_loss_std': np.float64(1.653556), 'val_avg_loss_bottom_decile': np.float64(12.352065), 'val_avg_loss_top_decile': np.float64(16.917654), 'val_avg_loss_min': np.float64(12.004164), 'val_avg_loss_max': np.float64(16.917654), 'val_avg_loss_bottom10%': np.float64(12.004164), 'val_avg_loss_top10%': np.float64(16.917654), 'val_avg_loss_cos1': np.float64(0.993138), 'val_avg_loss_entropy': np.float64(2.295768)}}
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2024-11-16 09:31:13,213 (server:353) INFO: Server: Starting evaluation at the end of round 18.
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2024-11-16 09:31:13,215 (server:359) INFO: ----------- Starting a new training round (Round #19) -------------
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2024-11-16 09:48:41,218 (client:354) INFO: {'Role': 'Client #3', 'Round': 19, 'Results_raw': {'train_loss': 9.239668, 'val_loss': 8.91006, 'test_loss': 9.109808}}
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2024-11-16 09:55:44,597 (client:354) INFO: {'Role': 'Client #7', 'Round': 19, 'Results_raw': {'train_loss': 9.377183, 'val_loss': 8.838833, 'test_loss': 8.735331}}
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2024-11-16 10:03:02,622 (client:354) INFO: {'Role': 'Client #2', 'Round': 19, 'Results_raw': {'train_loss': 8.393884, 'val_loss': 8.111693, 'test_loss': 7.954309}}
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2024-11-16 10:10:19,061 (client:354) INFO: {'Role': 'Client #4', 'Round': 19, 'Results_raw': {'train_loss': 9.289198, 'val_loss': 9.394615, 'test_loss': 8.748422}}
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2024-11-16 10:17:10,467 (client:354) INFO: {'Role': 'Client #5', 'Round': 19, 'Results_raw': {'train_loss': 9.065441, 'val_loss': 8.910274, 'test_loss': 8.774071}}
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2024-11-16 10:24:10,230 (client:354) INFO: {'Role': 'Client #9', 'Round': 19, 'Results_raw': {'train_loss': 9.289151, 'val_loss': 9.243179, 'test_loss': 8.841072}}
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2024-11-16 10:31:26,292 (client:354) INFO: {'Role': 'Client #10', 'Round': 19, 'Results_raw': {'train_loss': 8.955862, 'val_loss': 8.7217, 'test_loss': 8.529065}}
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2024-11-16 10:38:53,807 (client:354) INFO: {'Role': 'Client #1', 'Round': 19, 'Results_raw': {'train_loss': 9.443098, 'val_loss': 9.153017, 'test_loss': 8.824918}}
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2024-11-16 10:45:52,779 (client:354) INFO: {'Role': 'Client #8', 'Round': 19, 'Results_raw': {'train_loss': 9.049084, 'val_loss': 9.038941, 'test_loss': 8.944623}}
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2024-11-16 10:52:58,358 (client:354) INFO: {'Role': 'Client #6', 'Round': 19, 'Results_raw': {'train_loss': 9.581652, 'val_loss': 9.373798, 'test_loss': 9.174522}}
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2024-11-16 10:52:58,365 (server:615) INFO: {'Role': 'Server #', 'Round': 18, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74878.476266), 'test_avg_loss': np.float64(13.295184), 'val_total': np.float64(5632.0), 'val_loss': np.float64(77017.765218), 'val_avg_loss': np.float64(13.675029)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74878.476266), 'test_avg_loss': np.float64(13.295184), 'val_total': np.float64(5632.0), 'val_loss': np.float64(77017.765218), 'val_avg_loss': np.float64(13.675029)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7712.932878), 'test_loss_bottom_decile': np.float64(66825.205109), 'test_loss_top_decile': np.float64(88022.99823), 'test_loss_min': np.float64(66283.342331), 'test_loss_max': np.float64(88022.99823), 'test_loss_bottom10%': np.float64(66283.342331), 'test_loss_top10%': np.float64(88022.99823), 'test_loss_cos1': np.float64(0.994737), 'test_loss_entropy': np.float64(2.29737), 'test_avg_loss_std': np.float64(1.369484), 'test_avg_loss_bottom_decile': np.float64(11.865271), 'test_avg_loss_top_decile': np.float64(15.629083), 'test_avg_loss_min': np.float64(11.769059), 'test_avg_loss_max': np.float64(15.629083), 'test_avg_loss_bottom10%': np.float64(11.769059), 'test_avg_loss_top10%': np.float64(15.629083), 'test_avg_loss_cos1': np.float64(0.994737), 'test_avg_loss_entropy': np.float64(2.29737), 'val_loss_std': np.float64(8471.038121), 'val_loss_bottom_decile': np.float64(68629.171692), 'val_loss_top_decile': np.float64(91344.396034), 'val_loss_min': np.float64(66365.37043), 'val_loss_max': np.float64(91344.396034), 'val_loss_bottom10%': np.float64(66365.37043), 'val_loss_top10%': np.float64(91344.396034), 'val_loss_cos1': np.float64(0.994006), 'val_loss_entropy': np.float64(2.296628), 'val_avg_loss_std': np.float64(1.504091), 'val_avg_loss_bottom_decile': np.float64(12.185577), 'val_avg_loss_top_decile': np.float64(16.21882), 'val_avg_loss_min': np.float64(11.783624), 'val_avg_loss_max': np.float64(16.21882), 'val_avg_loss_bottom10%': np.float64(11.783624), 'val_avg_loss_top10%': np.float64(16.21882), 'val_avg_loss_cos1': np.float64(0.994006), 'val_avg_loss_entropy': np.float64(2.296628)}}
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2024-11-16 10:52:58,430 (server:353) INFO: Server: Starting evaluation at the end of round 19.
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2024-11-16 10:52:58,431 (server:359) INFO: ----------- Starting a new training round (Round #20) -------------
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2024-11-16 11:10:54,431 (client:354) INFO: {'Role': 'Client #3', 'Round': 20, 'Results_raw': {'train_loss': 9.205489, 'val_loss': 9.098647, 'test_loss': 9.326339}}
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2024-11-16 11:18:12,324 (client:354) INFO: {'Role': 'Client #4', 'Round': 20, 'Results_raw': {'train_loss': 9.245737, 'val_loss': 9.438858, 'test_loss': 8.795531}}
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2024-11-16 11:22:37,428 (client:354) INFO: {'Role': 'Client #1', 'Round': 20, 'Results_raw': {'train_loss': 9.414898, 'val_loss': 9.109534, 'test_loss': 8.769772}}
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2024-11-16 11:29:57,072 (client:354) INFO: {'Role': 'Client #5', 'Round': 20, 'Results_raw': {'train_loss': 9.028712, 'val_loss': 8.881755, 'test_loss': 8.754532}}
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2024-11-16 11:37:02,067 (client:354) INFO: {'Role': 'Client #9', 'Round': 20, 'Results_raw': {'train_loss': 9.255582, 'val_loss': 9.124933, 'test_loss': 8.71304}}
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2024-11-16 11:44:44,254 (client:354) INFO: {'Role': 'Client #8', 'Round': 20, 'Results_raw': {'train_loss': 9.033181, 'val_loss': 8.963588, 'test_loss': 8.870891}}
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2024-11-16 11:51:35,928 (client:354) INFO: {'Role': 'Client #7', 'Round': 20, 'Results_raw': {'train_loss': 9.338083, 'val_loss': 8.693124, 'test_loss': 8.595412}}
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2024-11-16 11:58:12,482 (client:354) INFO: {'Role': 'Client #6', 'Round': 20, 'Results_raw': {'train_loss': 9.524221, 'val_loss': 9.326235, 'test_loss': 9.099836}}
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2024-11-16 12:05:10,707 (client:354) INFO: {'Role': 'Client #2', 'Round': 20, 'Results_raw': {'train_loss': 8.355911, 'val_loss': 8.025271, 'test_loss': 7.928736}}
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2024-11-16 12:12:05,123 (client:354) INFO: {'Role': 'Client #10', 'Round': 20, 'Results_raw': {'train_loss': 8.892199, 'val_loss': 8.54471, 'test_loss': 8.333955}}
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2024-11-16 12:12:05,130 (server:615) INFO: {'Role': 'Server #', 'Round': 19, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74637.236942), 'test_avg_loss': np.float64(13.25235), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76759.315398), 'val_avg_loss': np.float64(13.62914)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74637.236942), 'test_avg_loss': np.float64(13.25235), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76759.315398), 'val_avg_loss': np.float64(13.62914)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7538.994986), 'test_loss_bottom_decile': np.float64(66654.059982), 'test_loss_top_decile': np.float64(87204.683151), 'test_loss_min': np.float64(66488.54953), 'test_loss_max': np.float64(87204.683151), 'test_loss_bottom10%': np.float64(66488.54953), 'test_loss_top10%': np.float64(87204.683151), 'test_loss_cos1': np.float64(0.994937), 'test_loss_entropy': np.float64(2.297568), 'test_avg_loss_std': np.float64(1.3386), 'test_avg_loss_bottom_decile': np.float64(11.834883), 'test_avg_loss_top_decile': np.float64(15.483786), 'test_avg_loss_min': np.float64(11.805495), 'test_avg_loss_max': np.float64(15.483786), 'test_avg_loss_bottom10%': np.float64(11.805495), 'test_avg_loss_top10%': np.float64(15.483786), 'test_avg_loss_cos1': np.float64(0.994937), 'test_avg_loss_entropy': np.float64(2.297568), 'val_loss_std': np.float64(8284.799613), 'val_loss_bottom_decile': np.float64(68473.298035), 'val_loss_top_decile': np.float64(90665.290138), 'val_loss_min': np.float64(66532.872681), 'val_loss_max': np.float64(90665.290138), 'val_loss_bottom10%': np.float64(66532.872681), 'val_loss_top10%': np.float64(90665.290138), 'val_loss_cos1': np.float64(0.994226), 'val_loss_entropy': np.float64(2.296847), 'val_avg_loss_std': np.float64(1.471023), 'val_avg_loss_bottom_decile': np.float64(12.157901), 'val_avg_loss_top_decile': np.float64(16.09824), 'val_avg_loss_min': np.float64(11.813365), 'val_avg_loss_max': np.float64(16.09824), 'val_avg_loss_bottom10%': np.float64(11.813365), 'val_avg_loss_top10%': np.float64(16.09824), 'val_avg_loss_cos1': np.float64(0.994226), 'val_avg_loss_entropy': np.float64(2.296847)}}
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2024-11-16 12:12:05,255 (server:353) INFO: Server: Starting evaluation at the end of round 20.
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2024-11-16 12:12:05,258 (server:359) INFO: ----------- Starting a new training round (Round #21) -------------
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2024-11-16 12:30:09,411 (client:354) INFO: {'Role': 'Client #5', 'Round': 21, 'Results_raw': {'train_loss': 8.979427, 'val_loss': 8.814969, 'test_loss': 8.697912}}
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2024-11-16 12:37:17,630 (client:354) INFO: {'Role': 'Client #3', 'Round': 21, 'Results_raw': {'train_loss': 9.184329, 'val_loss': 8.772748, 'test_loss': 8.960009}}
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2024-11-16 12:44:27,587 (client:354) INFO: {'Role': 'Client #6', 'Round': 21, 'Results_raw': {'train_loss': 9.518286, 'val_loss': 9.29019, 'test_loss': 9.107766}}
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2024-11-16 12:51:29,344 (client:354) INFO: {'Role': 'Client #9', 'Round': 21, 'Results_raw': {'train_loss': 9.20502, 'val_loss': 9.106906, 'test_loss': 8.768408}}
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2024-11-16 12:58:33,175 (client:354) INFO: {'Role': 'Client #4', 'Round': 21, 'Results_raw': {'train_loss': 9.208493, 'val_loss': 9.396505, 'test_loss': 8.766109}}
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2024-11-16 13:05:27,680 (client:354) INFO: {'Role': 'Client #1', 'Round': 21, 'Results_raw': {'train_loss': 9.364212, 'val_loss': 9.195431, 'test_loss': 8.855059}}
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2024-11-16 13:11:59,402 (client:354) INFO: {'Role': 'Client #7', 'Round': 21, 'Results_raw': {'train_loss': 9.323141, 'val_loss': 8.707, 'test_loss': 8.605494}}
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2024-11-16 13:19:13,467 (client:354) INFO: {'Role': 'Client #2', 'Round': 21, 'Results_raw': {'train_loss': 8.2926, 'val_loss': 8.258904, 'test_loss': 8.163392}}
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2024-11-16 13:25:16,976 (client:354) INFO: {'Role': 'Client #10', 'Round': 21, 'Results_raw': {'train_loss': 8.879157, 'val_loss': 8.697611, 'test_loss': 8.443022}}
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2024-11-16 13:31:43,469 (client:354) INFO: {'Role': 'Client #8', 'Round': 21, 'Results_raw': {'train_loss': 8.996423, 'val_loss': 8.982486, 'test_loss': 8.89624}}
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2024-11-16 13:31:43,500 (server:615) INFO: {'Role': 'Server #', 'Round': 20, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(75441.122461), 'test_avg_loss': np.float64(13.395086), 'val_total': np.float64(5632.0), 'val_loss': np.float64(77605.098515), 'val_avg_loss': np.float64(13.779314)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(75441.122461), 'test_avg_loss': np.float64(13.395086), 'val_total': np.float64(5632.0), 'val_loss': np.float64(77605.098515), 'val_avg_loss': np.float64(13.779314)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7768.624046), 'test_loss_bottom_decile': np.float64(67530.993912), 'test_loss_top_decile': np.float64(88088.755882), 'test_loss_min': np.float64(66739.488388), 'test_loss_max': np.float64(88088.755882), 'test_loss_bottom10%': np.float64(66739.488388), 'test_loss_top10%': np.float64(88088.755882), 'test_loss_cos1': np.float64(0.99474), 'test_loss_entropy': np.float64(2.297375), 'test_avg_loss_std': np.float64(1.379372), 'test_avg_loss_bottom_decile': np.float64(11.990588), 'test_avg_loss_top_decile': np.float64(15.640759), 'test_avg_loss_min': np.float64(11.850051), 'test_avg_loss_max': np.float64(15.640759), 'test_avg_loss_bottom10%': np.float64(11.850051), 'test_avg_loss_top10%': np.float64(15.640759), 'test_avg_loss_cos1': np.float64(0.99474), 'test_avg_loss_entropy': np.float64(2.297375), 'val_loss_std': np.float64(8538.078271), 'val_loss_bottom_decile': np.float64(69390.085304), 'val_loss_top_decile': np.float64(91653.232788), 'val_loss_min': np.float64(66762.882706), 'val_loss_max': np.float64(91653.232788), 'val_loss_bottom10%': np.float64(66762.882706), 'val_loss_top10%': np.float64(91653.232788), 'val_loss_cos1': np.float64(0.994002), 'val_loss_entropy': np.float64(2.296628), 'val_avg_loss_std': np.float64(1.515994), 'val_avg_loss_bottom_decile': np.float64(12.320683), 'val_avg_loss_top_decile': np.float64(16.273656), 'val_avg_loss_min': np.float64(11.854205), 'val_avg_loss_max': np.float64(16.273656), 'val_avg_loss_bottom10%': np.float64(11.854205), 'val_avg_loss_top10%': np.float64(16.273656), 'val_avg_loss_cos1': np.float64(0.994002), 'val_avg_loss_entropy': np.float64(2.296628)}}
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2024-11-16 13:31:43,656 (server:353) INFO: Server: Starting evaluation at the end of round 21.
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2024-11-16 13:31:43,657 (server:359) INFO: ----------- Starting a new training round (Round #22) -------------
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2024-11-16 13:48:42,596 (client:354) INFO: {'Role': 'Client #4', 'Round': 22, 'Results_raw': {'train_loss': 9.177397, 'val_loss': 9.413788, 'test_loss': 8.806867}}
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2024-11-16 13:55:01,489 (client:354) INFO: {'Role': 'Client #8', 'Round': 22, 'Results_raw': {'train_loss': 8.983753, 'val_loss': 9.198739, 'test_loss': 9.095073}}
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2024-11-16 14:01:24,849 (client:354) INFO: {'Role': 'Client #2', 'Round': 22, 'Results_raw': {'train_loss': 8.268904, 'val_loss': 7.98497, 'test_loss': 7.877263}}
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2024-11-16 14:07:45,043 (client:354) INFO: {'Role': 'Client #6', 'Round': 22, 'Results_raw': {'train_loss': 9.465707, 'val_loss': 9.302657, 'test_loss': 9.104543}}
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2024-11-16 14:14:24,277 (client:354) INFO: {'Role': 'Client #3', 'Round': 22, 'Results_raw': {'train_loss': 9.124773, 'val_loss': 8.711509, 'test_loss': 8.955818}}
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2024-11-16 14:21:03,988 (client:354) INFO: {'Role': 'Client #10', 'Round': 22, 'Results_raw': {'train_loss': 8.854547, 'val_loss': 8.595638, 'test_loss': 8.406245}}
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2024-11-16 14:27:28,627 (client:354) INFO: {'Role': 'Client #1', 'Round': 22, 'Results_raw': {'train_loss': 9.341131, 'val_loss': 9.135153, 'test_loss': 8.829487}}
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2024-11-16 14:33:36,306 (client:354) INFO: {'Role': 'Client #9', 'Round': 22, 'Results_raw': {'train_loss': 9.197351, 'val_loss': 9.196118, 'test_loss': 8.794556}}
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2024-11-16 14:39:47,328 (client:354) INFO: {'Role': 'Client #7', 'Round': 22, 'Results_raw': {'train_loss': 9.266078, 'val_loss': 9.084073, 'test_loss': 8.915492}}
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2024-11-16 14:45:45,249 (client:354) INFO: {'Role': 'Client #5', 'Round': 22, 'Results_raw': {'train_loss': 8.972388, 'val_loss': 8.760711, 'test_loss': 8.64776}}
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2024-11-16 14:45:45,256 (server:615) INFO: {'Role': 'Server #', 'Round': 21, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(73805.771739), 'test_avg_loss': np.float64(13.104718), 'val_total': np.float64(5632.0), 'val_loss': np.float64(75829.87576), 'val_avg_loss': np.float64(13.464111)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(73805.771739), 'test_avg_loss': np.float64(13.104718), 'val_total': np.float64(5632.0), 'val_loss': np.float64(75829.87576), 'val_avg_loss': np.float64(13.464111)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7279.136707), 'test_loss_bottom_decile': np.float64(66505.035042), 'test_loss_top_decile': np.float64(86265.746513), 'test_loss_min': np.float64(65651.55835), 'test_loss_max': np.float64(86265.746513), 'test_loss_bottom10%': np.float64(65651.55835), 'test_loss_top10%': np.float64(86265.746513), 'test_loss_cos1': np.float64(0.995172), 'test_loss_entropy': np.float64(2.297808), 'test_avg_loss_std': np.float64(1.29246), 'test_avg_loss_bottom_decile': np.float64(11.808422), 'test_avg_loss_top_decile': np.float64(15.317071), 'test_avg_loss_min': np.float64(11.656882), 'test_avg_loss_max': np.float64(15.317071), 'test_avg_loss_bottom10%': np.float64(11.656882), 'test_avg_loss_top10%': np.float64(15.317071), 'test_avg_loss_cos1': np.float64(0.995172), 'test_avg_loss_entropy': np.float64(2.297808), 'val_loss_std': np.float64(8001.225543), 'val_loss_bottom_decile': np.float64(68271.400909), 'val_loss_top_decile': np.float64(89625.510139), 'val_loss_min': np.float64(65668.284462), 'val_loss_max': np.float64(89625.510139), 'val_loss_bottom10%': np.float64(65668.284462), 'val_loss_top10%': np.float64(89625.510139), 'val_loss_cos1': np.float64(0.994479), 'val_loss_entropy': np.float64(2.29711), 'val_avg_loss_std': np.float64(1.420672), 'val_avg_loss_bottom_decile': np.float64(12.122053), 'val_avg_loss_top_decile': np.float64(15.91362), 'val_avg_loss_min': np.float64(11.659852), 'val_avg_loss_max': np.float64(15.91362), 'val_avg_loss_bottom10%': np.float64(11.659852), 'val_avg_loss_top10%': np.float64(15.91362), 'val_avg_loss_cos1': np.float64(0.994479), 'val_avg_loss_entropy': np.float64(2.29711)}}
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2024-11-16 14:45:45,382 (server:353) INFO: Server: Starting evaluation at the end of round 22.
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2024-11-16 14:45:45,382 (server:359) INFO: ----------- Starting a new training round (Round #23) -------------
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2024-11-16 15:01:38,345 (client:354) INFO: {'Role': 'Client #5', 'Round': 23, 'Results_raw': {'train_loss': 8.938045, 'val_loss': 8.922629, 'test_loss': 8.81604}}
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2024-11-16 15:07:43,362 (client:354) INFO: {'Role': 'Client #6', 'Round': 23, 'Results_raw': {'train_loss': 9.441911, 'val_loss': 9.213586, 'test_loss': 9.031188}}
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2024-11-16 15:13:39,504 (client:354) INFO: {'Role': 'Client #9', 'Round': 23, 'Results_raw': {'train_loss': 9.14204, 'val_loss': 9.054715, 'test_loss': 8.649111}}
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2024-11-16 15:16:49,364 (client:354) INFO: {'Role': 'Client #1', 'Round': 23, 'Results_raw': {'train_loss': 9.306154, 'val_loss': 8.995365, 'test_loss': 8.663134}}
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2024-11-16 15:22:22,045 (client:354) INFO: {'Role': 'Client #8', 'Round': 23, 'Results_raw': {'train_loss': 8.955321, 'val_loss': 8.964803, 'test_loss': 8.860947}}
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2024-11-16 15:28:24,399 (client:354) INFO: {'Role': 'Client #3', 'Round': 23, 'Results_raw': {'train_loss': 9.071164, 'val_loss': 8.702043, 'test_loss': 8.91462}}
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2024-11-16 15:34:57,234 (client:354) INFO: {'Role': 'Client #4', 'Round': 23, 'Results_raw': {'train_loss': 9.138543, 'val_loss': 9.405709, 'test_loss': 8.786896}}
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2024-11-16 15:41:41,301 (client:354) INFO: {'Role': 'Client #7', 'Round': 23, 'Results_raw': {'train_loss': 9.226488, 'val_loss': 8.701695, 'test_loss': 8.603008}}
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2024-11-16 15:47:54,431 (client:354) INFO: {'Role': 'Client #10', 'Round': 23, 'Results_raw': {'train_loss': 8.811688, 'val_loss': 8.713767, 'test_loss': 8.466587}}
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2024-11-16 15:54:34,858 (client:354) INFO: {'Role': 'Client #2', 'Round': 23, 'Results_raw': {'train_loss': 8.231744, 'val_loss': 8.046437, 'test_loss': 7.922219}}
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2024-11-16 15:54:34,861 (server:615) INFO: {'Role': 'Server #', 'Round': 22, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74258.122852), 'test_avg_loss': np.float64(13.185036), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76360.239156), 'val_avg_loss': np.float64(13.558281)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74258.122852), 'test_avg_loss': np.float64(13.185036), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76360.239156), 'val_avg_loss': np.float64(13.558281)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7565.490689), 'test_loss_bottom_decile': np.float64(66625.649574), 'test_loss_top_decile': np.float64(87196.800552), 'test_loss_min': np.float64(65645.877388), 'test_loss_max': np.float64(87196.800552), 'test_loss_bottom10%': np.float64(65645.877388), 'test_loss_top10%': np.float64(87196.800552), 'test_loss_cos1': np.float64(0.99485), 'test_loss_entropy': np.float64(2.29749), 'test_avg_loss_std': np.float64(1.343304), 'test_avg_loss_bottom_decile': np.float64(11.829838), 'test_avg_loss_top_decile': np.float64(15.482386), 'test_avg_loss_min': np.float64(11.655873), 'test_avg_loss_max': np.float64(15.482386), 'test_avg_loss_bottom10%': np.float64(11.655873), 'test_avg_loss_top10%': np.float64(15.482386), 'test_avg_loss_cos1': np.float64(0.99485), 'test_avg_loss_entropy': np.float64(2.29749), 'val_loss_std': np.float64(8274.808293), 'val_loss_bottom_decile': np.float64(68458.503281), 'val_loss_top_decile': np.float64(90575.038902), 'val_loss_min': np.float64(65699.462265), 'val_loss_max': np.float64(90575.038902), 'val_loss_bottom10%': np.float64(65699.462265), 'val_loss_top10%': np.float64(90575.038902), 'val_loss_cos1': np.float64(0.99418), 'val_loss_entropy': np.float64(2.296811), 'val_avg_loss_std': np.float64(1.469249), 'val_avg_loss_bottom_decile': np.float64(12.155274), 'val_avg_loss_top_decile': np.float64(16.082216), 'val_avg_loss_min': np.float64(11.665387), 'val_avg_loss_max': np.float64(16.082216), 'val_avg_loss_bottom10%': np.float64(11.665387), 'val_avg_loss_top10%': np.float64(16.082216), 'val_avg_loss_cos1': np.float64(0.99418), 'val_avg_loss_entropy': np.float64(2.296811)}}
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2024-11-16 15:54:34,900 (server:353) INFO: Server: Starting evaluation at the end of round 23.
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2024-11-16 15:54:34,900 (server:359) INFO: ----------- Starting a new training round (Round #24) -------------
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2024-11-16 16:10:13,947 (client:354) INFO: {'Role': 'Client #4', 'Round': 24, 'Results_raw': {'train_loss': 9.111713, 'val_loss': 9.470715, 'test_loss': 8.845766}}
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2024-11-16 16:16:46,102 (client:354) INFO: {'Role': 'Client #2', 'Round': 24, 'Results_raw': {'train_loss': 8.181161, 'val_loss': 7.988046, 'test_loss': 7.888915}}
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2024-11-16 16:23:24,102 (client:354) INFO: {'Role': 'Client #10', 'Round': 24, 'Results_raw': {'train_loss': 8.791155, 'val_loss': 8.521536, 'test_loss': 8.321694}}
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2024-11-16 16:29:48,222 (client:354) INFO: {'Role': 'Client #6', 'Round': 24, 'Results_raw': {'train_loss': 9.397568, 'val_loss': 9.215276, 'test_loss': 9.037244}}
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2024-11-16 16:36:13,336 (client:354) INFO: {'Role': 'Client #9', 'Round': 24, 'Results_raw': {'train_loss': 9.148993, 'val_loss': 9.130541, 'test_loss': 8.789157}}
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2024-11-16 16:42:37,166 (client:354) INFO: {'Role': 'Client #8', 'Round': 24, 'Results_raw': {'train_loss': 8.919624, 'val_loss': 8.931278, 'test_loss': 8.829632}}
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2024-11-16 16:46:00,428 (client:354) INFO: {'Role': 'Client #3', 'Round': 24, 'Results_raw': {'train_loss': 9.073331, 'val_loss': 8.623683, 'test_loss': 8.867315}}
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2024-11-16 16:50:39,368 (client:354) INFO: {'Role': 'Client #7', 'Round': 24, 'Results_raw': {'train_loss': 9.244203, 'val_loss': 8.660229, 'test_loss': 8.67488}}
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2024-11-16 16:57:07,880 (client:354) INFO: {'Role': 'Client #1', 'Round': 24, 'Results_raw': {'train_loss': 9.270654, 'val_loss': 9.049032, 'test_loss': 8.739636}}
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2024-11-16 17:03:38,626 (client:354) INFO: {'Role': 'Client #5', 'Round': 24, 'Results_raw': {'train_loss': 8.902032, 'val_loss': 8.758531, 'test_loss': 8.642232}}
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2024-11-16 17:03:38,635 (server:615) INFO: {'Role': 'Server #', 'Round': 23, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74107.631824), 'test_avg_loss': np.float64(13.158315), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76174.438418), 'val_avg_loss': np.float64(13.525291)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74107.631824), 'test_avg_loss': np.float64(13.158315), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76174.438418), 'val_avg_loss': np.float64(13.525291)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7541.801732), 'test_loss_bottom_decile': np.float64(66491.521858), 'test_loss_top_decile': np.float64(86814.687347), 'test_loss_min': np.float64(65726.118988), 'test_loss_max': np.float64(86814.687347), 'test_loss_bottom10%': np.float64(65726.118988), 'test_loss_top10%': np.float64(86814.687347), 'test_loss_cos1': np.float64(0.994861), 'test_loss_entropy': np.float64(2.297502), 'test_avg_loss_std': np.float64(1.339098), 'test_avg_loss_bottom_decile': np.float64(11.806023), 'test_avg_loss_top_decile': np.float64(15.41454), 'test_avg_loss_min': np.float64(11.670121), 'test_avg_loss_max': np.float64(15.41454), 'test_avg_loss_bottom10%': np.float64(11.670121), 'test_avg_loss_top10%': np.float64(15.41454), 'test_avg_loss_cos1': np.float64(0.994861), 'test_avg_loss_entropy': np.float64(2.297502), 'val_loss_std': np.float64(8224.462892), 'val_loss_bottom_decile': np.float64(68314.513916), 'val_loss_top_decile': np.float64(90195.899925), 'val_loss_min': np.float64(65721.022461), 'val_loss_max': np.float64(90195.899925), 'val_loss_bottom10%': np.float64(65721.022461), 'val_loss_top10%': np.float64(90195.899925), 'val_loss_cos1': np.float64(0.994222), 'val_loss_entropy': np.float64(2.296854), 'val_avg_loss_std': np.float64(1.460309), 'val_avg_loss_bottom_decile': np.float64(12.129708), 'val_avg_loss_top_decile': np.float64(16.014897), 'val_avg_loss_min': np.float64(11.669216), 'val_avg_loss_max': np.float64(16.014897), 'val_avg_loss_bottom10%': np.float64(11.669216), 'val_avg_loss_top10%': np.float64(16.014897), 'val_avg_loss_cos1': np.float64(0.994222), 'val_avg_loss_entropy': np.float64(2.296854)}}
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2024-11-16 17:03:38,698 (server:353) INFO: Server: Starting evaluation at the end of round 24.
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2024-11-16 17:03:38,699 (server:359) INFO: ----------- Starting a new training round (Round #25) -------------
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2024-11-16 17:21:18,771 (client:354) INFO: {'Role': 'Client #5', 'Round': 25, 'Results_raw': {'train_loss': 8.88806, 'val_loss': 8.817563, 'test_loss': 8.717874}}
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2024-11-16 17:27:32,377 (client:354) INFO: {'Role': 'Client #2', 'Round': 25, 'Results_raw': {'train_loss': 8.170687, 'val_loss': 8.061378, 'test_loss': 7.94798}}
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2024-11-16 17:34:03,607 (client:354) INFO: {'Role': 'Client #4', 'Round': 25, 'Results_raw': {'train_loss': 9.09284, 'val_loss': 9.218665, 'test_loss': 8.601601}}
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2024-11-16 17:39:53,017 (client:354) INFO: {'Role': 'Client #8', 'Round': 25, 'Results_raw': {'train_loss': 8.893103, 'val_loss': 8.933116, 'test_loss': 8.834493}}
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2024-11-16 17:45:30,652 (client:354) INFO: {'Role': 'Client #10', 'Round': 25, 'Results_raw': {'train_loss': 8.764814, 'val_loss': 8.527643, 'test_loss': 8.329048}}
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2024-11-16 17:51:43,263 (client:354) INFO: {'Role': 'Client #3', 'Round': 25, 'Results_raw': {'train_loss': 9.027198, 'val_loss': 8.868721, 'test_loss': 9.096842}}
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2024-11-16 17:58:28,584 (client:354) INFO: {'Role': 'Client #9', 'Round': 25, 'Results_raw': {'train_loss': 9.126826, 'val_loss': 9.139891, 'test_loss': 8.790081}}
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2024-11-16 18:05:15,010 (client:354) INFO: {'Role': 'Client #1', 'Round': 25, 'Results_raw': {'train_loss': 9.253265, 'val_loss': 9.048259, 'test_loss': 8.716468}}
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2024-11-16 18:11:38,109 (client:354) INFO: {'Role': 'Client #6', 'Round': 25, 'Results_raw': {'train_loss': 9.371836, 'val_loss': 9.167216, 'test_loss': 8.952548}}
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2024-11-16 18:18:04,925 (client:354) INFO: {'Role': 'Client #7', 'Round': 25, 'Results_raw': {'train_loss': 9.173891, 'val_loss': 8.659004, 'test_loss': 8.588189}}
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2024-11-16 18:18:04,958 (server:615) INFO: {'Role': 'Server #', 'Round': 24, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74343.845407), 'test_avg_loss': np.float64(13.200257), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76435.277583), 'val_avg_loss': np.float64(13.571605)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74343.845407), 'test_avg_loss': np.float64(13.200257), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76435.277583), 'val_avg_loss': np.float64(13.571605)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7596.865391), 'test_loss_bottom_decile': np.float64(66659.772751), 'test_loss_top_decile': np.float64(87691.519783), 'test_loss_min': np.float64(66022.783722), 'test_loss_max': np.float64(87691.519783), 'test_loss_bottom10%': np.float64(66022.783722), 'test_loss_top10%': np.float64(87691.519783), 'test_loss_cos1': np.float64(0.99482), 'test_loss_entropy': np.float64(2.297466), 'test_avg_loss_std': np.float64(1.348875), 'test_avg_loss_bottom_decile': np.float64(11.835897), 'test_avg_loss_top_decile': np.float64(15.570227), 'test_avg_loss_min': np.float64(11.722795), 'test_avg_loss_max': np.float64(15.570227), 'test_avg_loss_bottom10%': np.float64(11.722795), 'test_avg_loss_top10%': np.float64(15.570227), 'test_avg_loss_cos1': np.float64(0.99482), 'test_avg_loss_entropy': np.float64(2.297466), 'val_loss_std': np.float64(8324.855528), 'val_loss_bottom_decile': np.float64(68469.429443), 'val_loss_top_decile': np.float64(91251.983795), 'val_loss_min': np.float64(65949.571716), 'val_loss_max': np.float64(91251.983795), 'val_loss_bottom10%': np.float64(65949.571716), 'val_loss_top10%': np.float64(91251.983795), 'val_loss_cos1': np.float64(0.994121), 'val_loss_entropy': np.float64(2.296761), 'val_avg_loss_std': np.float64(1.478135), 'val_avg_loss_bottom_decile': np.float64(12.157214), 'val_avg_loss_top_decile': np.float64(16.202412), 'val_avg_loss_min': np.float64(11.709796), 'val_avg_loss_max': np.float64(16.202412), 'val_avg_loss_bottom10%': np.float64(11.709796), 'val_avg_loss_top10%': np.float64(16.202412), 'val_avg_loss_cos1': np.float64(0.994121), 'val_avg_loss_entropy': np.float64(2.296761)}}
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2024-11-16 18:18:05,075 (server:353) INFO: Server: Starting evaluation at the end of round 25.
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2024-11-16 18:18:05,076 (server:359) INFO: ----------- Starting a new training round (Round #26) -------------
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2024-11-16 18:35:24,894 (client:354) INFO: {'Role': 'Client #8', 'Round': 26, 'Results_raw': {'train_loss': 8.86652, 'val_loss': 8.978123, 'test_loss': 8.89709}}
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2024-11-16 18:41:51,347 (client:354) INFO: {'Role': 'Client #9', 'Round': 26, 'Results_raw': {'train_loss': 9.089008, 'val_loss': 9.059748, 'test_loss': 8.711509}}
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2024-11-16 18:48:22,916 (client:354) INFO: {'Role': 'Client #7', 'Round': 26, 'Results_raw': {'train_loss': 9.168359, 'val_loss': 8.629413, 'test_loss': 8.567099}}
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2024-11-16 18:54:37,129 (client:354) INFO: {'Role': 'Client #10', 'Round': 26, 'Results_raw': {'train_loss': 8.727873, 'val_loss': 8.560485, 'test_loss': 8.358221}}
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2024-11-16 19:00:31,923 (client:354) INFO: {'Role': 'Client #2', 'Round': 26, 'Results_raw': {'train_loss': 8.149734, 'val_loss': 7.940698, 'test_loss': 7.878212}}
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2024-11-16 19:06:53,768 (client:354) INFO: {'Role': 'Client #5', 'Round': 26, 'Results_raw': {'train_loss': 8.863222, 'val_loss': 8.947198, 'test_loss': 8.824801}}
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2024-11-16 19:12:46,085 (client:354) INFO: {'Role': 'Client #1', 'Round': 26, 'Results_raw': {'train_loss': 9.219541, 'val_loss': 8.911236, 'test_loss': 8.60096}}
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2024-11-16 19:16:09,409 (client:354) INFO: {'Role': 'Client #6', 'Round': 26, 'Results_raw': {'train_loss': 9.354503, 'val_loss': 9.111812, 'test_loss': 8.95936}}
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2024-11-16 19:19:30,415 (client:354) INFO: {'Role': 'Client #4', 'Round': 26, 'Results_raw': {'train_loss': 9.065559, 'val_loss': 9.263273, 'test_loss': 8.646522}}
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2024-11-16 19:22:51,841 (client:354) INFO: {'Role': 'Client #3', 'Round': 26, 'Results_raw': {'train_loss': 8.991867, 'val_loss': 8.681885, 'test_loss': 8.908298}}
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2024-11-16 19:22:51,844 (server:615) INFO: {'Role': 'Server #', 'Round': 25, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74399.810699), 'test_avg_loss': np.float64(13.210194), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76558.822368), 'val_avg_loss': np.float64(13.593541)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74399.810699), 'test_avg_loss': np.float64(13.210194), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76558.822368), 'val_avg_loss': np.float64(13.593541)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7595.401278), 'test_loss_bottom_decile': np.float64(66790.650284), 'test_loss_top_decile': np.float64(87465.264557), 'test_loss_min': np.float64(65744.898033), 'test_loss_max': np.float64(87465.264557), 'test_loss_bottom10%': np.float64(65744.898033), 'test_loss_top10%': np.float64(87465.264557), 'test_loss_cos1': np.float64(0.994829), 'test_loss_entropy': np.float64(2.29747), 'test_avg_loss_std': np.float64(1.348615), 'test_avg_loss_bottom_decile': np.float64(11.859135), 'test_avg_loss_top_decile': np.float64(15.530054), 'test_avg_loss_min': np.float64(11.673455), 'test_avg_loss_max': np.float64(15.530054), 'test_avg_loss_bottom10%': np.float64(11.673455), 'test_avg_loss_top10%': np.float64(15.530054), 'test_avg_loss_cos1': np.float64(0.994829), 'test_avg_loss_entropy': np.float64(2.29747), 'val_loss_std': np.float64(8325.626675), 'val_loss_bottom_decile': np.float64(68611.838272), 'val_loss_top_decile': np.float64(91159.646515), 'val_loss_min': np.float64(65707.521935), 'val_loss_max': np.float64(91159.646515), 'val_loss_bottom10%': np.float64(65707.521935), 'val_loss_top10%': np.float64(91159.646515), 'val_loss_cos1': np.float64(0.994139), 'val_loss_entropy': np.float64(2.296771), 'val_avg_loss_std': np.float64(1.478272), 'val_avg_loss_bottom_decile': np.float64(12.1825), 'val_avg_loss_top_decile': np.float64(16.186017), 'val_avg_loss_min': np.float64(11.666819), 'val_avg_loss_max': np.float64(16.186017), 'val_avg_loss_bottom10%': np.float64(11.666819), 'val_avg_loss_top10%': np.float64(16.186017), 'val_avg_loss_cos1': np.float64(0.994139), 'val_avg_loss_entropy': np.float64(2.296771)}}
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2024-11-16 19:22:51,881 (server:353) INFO: Server: Starting evaluation at the end of round 26.
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2024-11-16 19:22:51,882 (server:359) INFO: ----------- Starting a new training round (Round #27) -------------
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2024-11-16 19:32:11,516 (client:354) INFO: {'Role': 'Client #9', 'Round': 27, 'Results_raw': {'train_loss': 9.065592, 'val_loss': 9.088415, 'test_loss': 8.703635}}
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2024-11-16 19:38:40,179 (client:354) INFO: {'Role': 'Client #8', 'Round': 27, 'Results_raw': {'train_loss': 8.855737, 'val_loss': 8.920469, 'test_loss': 8.828428}}
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2024-11-16 19:45:10,671 (client:354) INFO: {'Role': 'Client #4', 'Round': 27, 'Results_raw': {'train_loss': 9.019289, 'val_loss': 9.216227, 'test_loss': 8.599704}}
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2024-11-16 19:51:45,488 (client:354) INFO: {'Role': 'Client #1', 'Round': 27, 'Results_raw': {'train_loss': 9.184457, 'val_loss': 8.964812, 'test_loss': 8.679526}}
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2024-11-16 19:57:48,813 (client:354) INFO: {'Role': 'Client #2', 'Round': 27, 'Results_raw': {'train_loss': 8.108279, 'val_loss': 7.853096, 'test_loss': 7.750714}}
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2024-11-16 20:03:59,336 (client:354) INFO: {'Role': 'Client #3', 'Round': 27, 'Results_raw': {'train_loss': 8.954098, 'val_loss': 8.866438, 'test_loss': 9.156078}}
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2024-11-16 20:10:14,678 (client:354) INFO: {'Role': 'Client #7', 'Round': 27, 'Results_raw': {'train_loss': 9.10402, 'val_loss': 8.979912, 'test_loss': 8.890977}}
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2024-11-16 20:16:13,207 (client:354) INFO: {'Role': 'Client #10', 'Round': 27, 'Results_raw': {'train_loss': 8.719868, 'val_loss': 8.497247, 'test_loss': 8.301894}}
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2024-11-16 20:22:23,641 (client:354) INFO: {'Role': 'Client #6', 'Round': 27, 'Results_raw': {'train_loss': 9.321466, 'val_loss': 9.081247, 'test_loss': 8.898264}}
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2024-11-16 20:28:25,837 (client:354) INFO: {'Role': 'Client #5', 'Round': 27, 'Results_raw': {'train_loss': 8.834586, 'val_loss': 8.720921, 'test_loss': 8.640967}}
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2024-11-16 20:28:25,845 (server:615) INFO: {'Role': 'Server #', 'Round': 26, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(73112.842879), 'test_avg_loss': np.float64(12.981684), 'val_total': np.float64(5632.0), 'val_loss': np.float64(75116.267719), 'val_avg_loss': np.float64(13.337405)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(73112.842879), 'test_avg_loss': np.float64(12.981684), 'val_total': np.float64(5632.0), 'val_loss': np.float64(75116.267719), 'val_avg_loss': np.float64(13.337405)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7159.350775), 'test_loss_bottom_decile': np.float64(66071.2061), 'test_loss_top_decile': np.float64(85403.146675), 'test_loss_min': np.float64(65012.134651), 'test_loss_max': np.float64(85403.146675), 'test_loss_bottom10%': np.float64(65012.134651), 'test_loss_top10%': np.float64(85403.146675), 'test_loss_cos1': np.float64(0.99524), 'test_loss_entropy': np.float64(2.297877), 'test_avg_loss_std': np.float64(1.271192), 'test_avg_loss_bottom_decile': np.float64(11.731393), 'test_avg_loss_top_decile': np.float64(15.163911), 'test_avg_loss_min': np.float64(11.543348), 'test_avg_loss_max': np.float64(15.163911), 'test_avg_loss_bottom10%': np.float64(11.543348), 'test_avg_loss_top10%': np.float64(15.163911), 'test_avg_loss_cos1': np.float64(0.99524), 'test_avg_loss_entropy': np.float64(2.297877), 'val_loss_std': np.float64(7820.348719), 'val_loss_bottom_decile': np.float64(67742.41835), 'val_loss_top_decile': np.float64(88635.35907), 'val_loss_min': np.float64(64924.833191), 'val_loss_max': np.float64(88635.35907), 'val_loss_bottom10%': np.float64(64924.833191), 'val_loss_top10%': np.float64(88635.35907), 'val_loss_cos1': np.float64(0.994624), 'val_loss_entropy': np.float64(2.297252), 'val_avg_loss_std': np.float64(1.388556), 'val_avg_loss_bottom_decile': np.float64(12.028128), 'val_avg_loss_top_decile': np.float64(15.737812), 'val_avg_loss_min': np.float64(11.527847), 'val_avg_loss_max': np.float64(15.737812), 'val_avg_loss_bottom10%': np.float64(11.527847), 'val_avg_loss_top10%': np.float64(15.737812), 'val_avg_loss_cos1': np.float64(0.994624), 'val_avg_loss_entropy': np.float64(2.297252)}}
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2024-11-16 20:28:25,892 (server:353) INFO: Server: Starting evaluation at the end of round 27.
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2024-11-16 20:28:25,893 (server:359) INFO: ----------- Starting a new training round (Round #28) -------------
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2024-11-16 20:43:50,966 (client:354) INFO: {'Role': 'Client #8', 'Round': 28, 'Results_raw': {'train_loss': 8.827524, 'val_loss': 8.859989, 'test_loss': 8.779333}}
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2024-11-16 20:50:10,294 (client:354) INFO: {'Role': 'Client #9', 'Round': 28, 'Results_raw': {'train_loss': 9.04296, 'val_loss': 9.028573, 'test_loss': 8.674675}}
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2024-11-16 20:56:40,980 (client:354) INFO: {'Role': 'Client #3', 'Round': 28, 'Results_raw': {'train_loss': 8.957243, 'val_loss': 8.620415, 'test_loss': 8.875258}}
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2024-11-16 21:03:03,401 (client:354) INFO: {'Role': 'Client #4', 'Round': 28, 'Results_raw': {'train_loss': 9.019843, 'val_loss': 9.12837, 'test_loss': 8.541049}}
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2024-11-16 21:08:55,361 (client:354) INFO: {'Role': 'Client #6', 'Round': 28, 'Results_raw': {'train_loss': 9.291774, 'val_loss': 9.102824, 'test_loss': 8.880076}}
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2024-11-16 21:15:13,620 (client:354) INFO: {'Role': 'Client #1', 'Round': 28, 'Results_raw': {'train_loss': 9.165089, 'val_loss': 8.93569, 'test_loss': 8.623696}}
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2024-11-16 21:21:13,237 (client:354) INFO: {'Role': 'Client #2', 'Round': 28, 'Results_raw': {'train_loss': 8.102373, 'val_loss': 7.991949, 'test_loss': 7.945112}}
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2024-11-16 21:27:09,170 (client:354) INFO: {'Role': 'Client #10', 'Round': 28, 'Results_raw': {'train_loss': 8.685345, 'val_loss': 8.430718, 'test_loss': 8.251024}}
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2024-11-16 21:33:04,618 (client:354) INFO: {'Role': 'Client #7', 'Round': 28, 'Results_raw': {'train_loss': 9.089783, 'val_loss': 8.703093, 'test_loss': 8.700579}}
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2024-11-16 21:38:44,934 (client:354) INFO: {'Role': 'Client #5', 'Round': 28, 'Results_raw': {'train_loss': 8.817398, 'val_loss': 8.849999, 'test_loss': 8.801449}}
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2024-11-16 21:38:44,943 (server:615) INFO: {'Role': 'Server #', 'Round': 27, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74392.580538), 'test_avg_loss': np.float64(13.20891), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76506.442914), 'val_avg_loss': np.float64(13.584241)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(74392.580538), 'test_avg_loss': np.float64(13.20891), 'val_total': np.float64(5632.0), 'val_loss': np.float64(76506.442914), 'val_avg_loss': np.float64(13.584241)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7605.390595), 'test_loss_bottom_decile': np.float64(66759.266716), 'test_loss_top_decile': np.float64(87387.040894), 'test_loss_min': np.float64(65804.680359), 'test_loss_max': np.float64(87387.040894), 'test_loss_bottom10%': np.float64(65804.680359), 'test_loss_top10%': np.float64(87387.040894), 'test_loss_cos1': np.float64(0.994815), 'test_loss_entropy': np.float64(2.297457), 'test_avg_loss_std': np.float64(1.350389), 'test_avg_loss_bottom_decile': np.float64(11.853563), 'test_avg_loss_top_decile': np.float64(15.516165), 'test_avg_loss_min': np.float64(11.68407), 'test_avg_loss_max': np.float64(15.516165), 'test_avg_loss_bottom10%': np.float64(11.68407), 'test_avg_loss_top10%': np.float64(15.516165), 'test_avg_loss_cos1': np.float64(0.994815), 'test_avg_loss_entropy': np.float64(2.297457), 'val_loss_std': np.float64(8299.764563), 'val_loss_bottom_decile': np.float64(68484.05674), 'val_loss_top_decile': np.float64(90964.604546), 'val_loss_min': np.float64(65760.573189), 'val_loss_max': np.float64(90964.604546), 'val_loss_bottom10%': np.float64(65760.573189), 'val_loss_top10%': np.float64(90964.604546), 'val_loss_cos1': np.float64(0.994167), 'val_loss_entropy': np.float64(2.296799), 'val_avg_loss_std': np.float64(1.47368), 'val_avg_loss_bottom_decile': np.float64(12.159811), 'val_avg_loss_top_decile': np.float64(16.151386), 'val_avg_loss_min': np.float64(11.676238), 'val_avg_loss_max': np.float64(16.151386), 'val_avg_loss_bottom10%': np.float64(11.676238), 'val_avg_loss_top10%': np.float64(16.151386), 'val_avg_loss_cos1': np.float64(0.994167), 'val_avg_loss_entropy': np.float64(2.296799)}}
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2024-11-16 21:38:45,009 (server:353) INFO: Server: Starting evaluation at the end of round 28.
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2024-11-16 21:38:45,009 (server:359) INFO: ----------- Starting a new training round (Round #29) -------------
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2024-11-16 21:53:46,045 (client:354) INFO: {'Role': 'Client #1', 'Round': 29, 'Results_raw': {'train_loss': 9.138735, 'val_loss': 8.856382, 'test_loss': 8.579184}}
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2024-11-16 21:59:34,709 (client:354) INFO: {'Role': 'Client #5', 'Round': 29, 'Results_raw': {'train_loss': 8.784187, 'val_loss': 8.761011, 'test_loss': 8.66474}}
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2024-11-16 22:05:21,214 (client:354) INFO: {'Role': 'Client #7', 'Round': 29, 'Results_raw': {'train_loss': 9.054583, 'val_loss': 8.724908, 'test_loss': 8.728347}}
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2024-11-16 22:11:29,568 (client:354) INFO: {'Role': 'Client #6', 'Round': 29, 'Results_raw': {'train_loss': 9.2753, 'val_loss': 9.233491, 'test_loss': 9.034445}}
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2024-11-16 22:17:47,633 (client:354) INFO: {'Role': 'Client #3', 'Round': 29, 'Results_raw': {'train_loss': 8.897343, 'val_loss': 8.764743, 'test_loss': 8.982131}}
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2024-11-16 22:24:03,314 (client:354) INFO: {'Role': 'Client #4', 'Round': 29, 'Results_raw': {'train_loss': 8.97421, 'val_loss': 9.256054, 'test_loss': 8.609252}}
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2024-11-16 22:31:02,546 (client:354) INFO: {'Role': 'Client #9', 'Round': 29, 'Results_raw': {'train_loss': 9.025347, 'val_loss': 9.045554, 'test_loss': 8.674381}}
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2024-11-16 22:37:26,579 (client:354) INFO: {'Role': 'Client #10', 'Round': 29, 'Results_raw': {'train_loss': 8.662232, 'val_loss': 8.341011, 'test_loss': 8.152305}}
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2024-11-16 22:43:53,980 (client:354) INFO: {'Role': 'Client #2', 'Round': 29, 'Results_raw': {'train_loss': 8.05377, 'val_loss': 7.82295, 'test_loss': 7.72218}}
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2024-11-16 22:50:12,313 (client:354) INFO: {'Role': 'Client #8', 'Round': 29, 'Results_raw': {'train_loss': 8.806752, 'val_loss': 8.880949, 'test_loss': 8.808669}}
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2024-11-16 22:50:12,321 (server:615) INFO: {'Role': 'Server #', 'Round': 28, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(73741.403675), 'test_avg_loss': np.float64(13.093289), 'val_total': np.float64(5632.0), 'val_loss': np.float64(75803.370278), 'val_avg_loss': np.float64(13.459405)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(73741.403675), 'test_avg_loss': np.float64(13.093289), 'val_total': np.float64(5632.0), 'val_loss': np.float64(75803.370278), 'val_avg_loss': np.float64(13.459405)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7631.870472), 'test_loss_bottom_decile': np.float64(66224.005135), 'test_loss_top_decile': np.float64(87397.177513), 'test_loss_min': np.float64(65261.965858), 'test_loss_max': np.float64(87397.177513), 'test_loss_bottom10%': np.float64(65261.965858), 'test_loss_top10%': np.float64(87397.177513), 'test_loss_cos1': np.float64(0.994687), 'test_loss_entropy': np.float64(2.297337), 'test_avg_loss_std': np.float64(1.355091), 'test_avg_loss_bottom_decile': np.float64(11.758524), 'test_avg_loss_top_decile': np.float64(15.517965), 'test_avg_loss_min': np.float64(11.587707), 'test_avg_loss_max': np.float64(15.517965), 'test_avg_loss_bottom10%': np.float64(11.587707), 'test_avg_loss_top10%': np.float64(15.517965), 'test_avg_loss_cos1': np.float64(0.994687), 'test_avg_loss_entropy': np.float64(2.297337), 'val_loss_std': np.float64(8341.767221), 'val_loss_bottom_decile': np.float64(67936.029564), 'val_loss_top_decile': np.float64(90952.393944), 'val_loss_min': np.float64(65123.094116), 'val_loss_max': np.float64(90952.393944), 'val_loss_bottom10%': np.float64(65123.094116), 'val_loss_top10%': np.float64(90952.393944), 'val_loss_cos1': np.float64(0.994), 'val_loss_entropy': np.float64(2.296642), 'val_avg_loss_std': np.float64(1.481138), 'val_avg_loss_bottom_decile': np.float64(12.062505), 'val_avg_loss_top_decile': np.float64(16.149218), 'val_avg_loss_min': np.float64(11.563049), 'val_avg_loss_max': np.float64(16.149218), 'val_avg_loss_bottom10%': np.float64(11.563049), 'val_avg_loss_top10%': np.float64(16.149218), 'val_avg_loss_cos1': np.float64(0.994), 'val_avg_loss_entropy': np.float64(2.296642)}}
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2024-11-16 22:50:12,443 (server:353) INFO: Server: Starting evaluation at the end of round 29.
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2024-11-16 22:50:12,444 (server:359) INFO: ----------- Starting a new training round (Round #30) -------------
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2024-11-16 23:05:48,898 (client:354) INFO: {'Role': 'Client #7', 'Round': 30, 'Results_raw': {'train_loss': 9.053836, 'val_loss': 8.616388, 'test_loss': 8.590505}}
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2024-11-16 23:11:43,590 (client:354) INFO: {'Role': 'Client #6', 'Round': 30, 'Results_raw': {'train_loss': 9.248297, 'val_loss': 9.161067, 'test_loss': 8.958803}}
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2024-11-16 23:18:12,175 (client:354) INFO: {'Role': 'Client #10', 'Round': 30, 'Results_raw': {'train_loss': 8.631959, 'val_loss': 8.362667, 'test_loss': 8.176266}}
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2024-11-16 23:24:04,481 (client:354) INFO: {'Role': 'Client #8', 'Round': 30, 'Results_raw': {'train_loss': 8.779845, 'val_loss': 8.875364, 'test_loss': 8.77197}}
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2024-11-16 23:29:49,862 (client:354) INFO: {'Role': 'Client #3', 'Round': 30, 'Results_raw': {'train_loss': 8.872526, 'val_loss': 8.646428, 'test_loss': 8.92387}}
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2024-11-16 23:36:23,092 (client:354) INFO: {'Role': 'Client #2', 'Round': 30, 'Results_raw': {'train_loss': 8.025669, 'val_loss': 7.886739, 'test_loss': 7.82035}}
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2024-11-16 23:42:19,637 (client:354) INFO: {'Role': 'Client #5', 'Round': 30, 'Results_raw': {'train_loss': 8.792412, 'val_loss': 8.924681, 'test_loss': 8.839238}}
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2024-11-16 23:48:08,080 (client:354) INFO: {'Role': 'Client #4', 'Round': 30, 'Results_raw': {'train_loss': 8.965329, 'val_loss': 9.242566, 'test_loss': 8.623474}}
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2024-11-16 23:55:03,413 (client:354) INFO: {'Role': 'Client #9', 'Round': 30, 'Results_raw': {'train_loss': 8.997751, 'val_loss': 8.926653, 'test_loss': 8.586748}}
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2024-11-17 00:01:57,243 (client:354) INFO: {'Role': 'Client #1', 'Round': 30, 'Results_raw': {'train_loss': 9.146508, 'val_loss': 8.97512, 'test_loss': 8.689247}}
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2024-11-17 00:01:57,262 (server:615) INFO: {'Role': 'Server #', 'Round': 29, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72685.981907), 'test_avg_loss': np.float64(12.905892), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74708.026363), 'val_avg_loss': np.float64(13.264919)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72685.981907), 'test_avg_loss': np.float64(12.905892), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74708.026363), 'val_avg_loss': np.float64(13.264919)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6898.501194), 'test_loss_bottom_decile': np.float64(65905.81266), 'test_loss_top_decile': np.float64(84548.441452), 'test_loss_min': np.float64(64562.202232), 'test_loss_max': np.float64(84548.441452), 'test_loss_bottom10%': np.float64(64562.202232), 'test_loss_top10%': np.float64(84548.441452), 'test_loss_cos1': np.float64(0.995526), 'test_loss_entropy': np.float64(2.298157), 'test_avg_loss_std': np.float64(1.224876), 'test_avg_loss_bottom_decile': np.float64(11.702026), 'test_avg_loss_top_decile': np.float64(15.012152), 'test_avg_loss_min': np.float64(11.463459), 'test_avg_loss_max': np.float64(15.012152), 'test_avg_loss_bottom10%': np.float64(11.463459), 'test_avg_loss_top10%': np.float64(15.012152), 'test_avg_loss_cos1': np.float64(0.995526), 'test_avg_loss_entropy': np.float64(2.298157), 'val_loss_std': np.float64(7493.374818), 'val_loss_bottom_decile': np.float64(67585.419159), 'val_loss_top_decile': np.float64(87816.9207), 'val_loss_min': np.float64(64542.204903), 'val_loss_max': np.float64(87816.9207), 'val_loss_bottom10%': np.float64(64542.204903), 'val_loss_top10%': np.float64(87816.9207), 'val_loss_cos1': np.float64(0.995007), 'val_loss_entropy': np.float64(2.297627), 'val_avg_loss_std': np.float64(1.3305), 'val_avg_loss_bottom_decile': np.float64(12.000252), 'val_avg_loss_top_decile': np.float64(15.592493), 'val_avg_loss_min': np.float64(11.459909), 'val_avg_loss_max': np.float64(15.592493), 'val_avg_loss_bottom10%': np.float64(11.459909), 'val_avg_loss_top10%': np.float64(15.592493), 'val_avg_loss_cos1': np.float64(0.995007), 'val_avg_loss_entropy': np.float64(2.297627)}}
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2024-11-17 00:01:57,401 (server:353) INFO: Server: Starting evaluation at the end of round 30.
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2024-11-17 00:01:57,402 (server:359) INFO: ----------- Starting a new training round (Round #31) -------------
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2024-11-17 00:18:26,552 (client:354) INFO: {'Role': 'Client #4', 'Round': 31, 'Results_raw': {'train_loss': 8.930347, 'val_loss': 9.136361, 'test_loss': 8.544083}}
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2024-11-17 00:25:25,098 (client:354) INFO: {'Role': 'Client #2', 'Round': 31, 'Results_raw': {'train_loss': 8.029527, 'val_loss': 7.912403, 'test_loss': 7.841198}}
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2024-11-17 00:32:23,599 (client:354) INFO: {'Role': 'Client #5', 'Round': 31, 'Results_raw': {'train_loss': 8.757982, 'val_loss': 8.633672, 'test_loss': 8.511029}}
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2024-11-17 00:39:23,379 (client:354) INFO: {'Role': 'Client #10', 'Round': 31, 'Results_raw': {'train_loss': 8.617906, 'val_loss': 8.310089, 'test_loss': 8.093925}}
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2024-11-17 00:45:24,128 (client:354) INFO: {'Role': 'Client #3', 'Round': 31, 'Results_raw': {'train_loss': 8.847149, 'val_loss': 8.515943, 'test_loss': 8.78375}}
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2024-11-17 00:51:43,092 (client:354) INFO: {'Role': 'Client #9', 'Round': 31, 'Results_raw': {'train_loss': 8.987295, 'val_loss': 9.021692, 'test_loss': 8.687186}}
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2024-11-17 00:58:12,519 (client:354) INFO: {'Role': 'Client #1', 'Round': 31, 'Results_raw': {'train_loss': 9.090428, 'val_loss': 8.946359, 'test_loss': 8.661277}}
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2024-11-17 01:05:05,622 (client:354) INFO: {'Role': 'Client #6', 'Round': 31, 'Results_raw': {'train_loss': 9.230574, 'val_loss': 8.984393, 'test_loss': 8.807835}}
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2024-11-17 01:11:48,676 (client:354) INFO: {'Role': 'Client #7', 'Round': 31, 'Results_raw': {'train_loss': 9.02182, 'val_loss': 8.418559, 'test_loss': 8.392633}}
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2024-11-17 01:18:47,689 (client:354) INFO: {'Role': 'Client #8', 'Round': 31, 'Results_raw': {'train_loss': 8.766371, 'val_loss': 8.846955, 'test_loss': 8.749701}}
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2024-11-17 01:18:47,693 (server:615) INFO: {'Role': 'Server #', 'Round': 30, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(73035.810902), 'test_avg_loss': np.float64(12.968006), 'val_total': np.float64(5632.0), 'val_loss': np.float64(75037.596138), 'val_avg_loss': np.float64(13.323437)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(73035.810902), 'test_avg_loss': np.float64(12.968006), 'val_total': np.float64(5632.0), 'val_loss': np.float64(75037.596138), 'val_avg_loss': np.float64(13.323437)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7152.585053), 'test_loss_bottom_decile': np.float64(65934.260796), 'test_loss_top_decile': np.float64(84944.020851), 'test_loss_min': np.float64(64769.143967), 'test_loss_max': np.float64(84944.020851), 'test_loss_bottom10%': np.float64(64769.143967), 'test_loss_top10%': np.float64(84944.020851), 'test_loss_cos1': np.float64(0.995239), 'test_loss_entropy': np.float64(2.297873), 'test_avg_loss_std': np.float64(1.26999), 'test_avg_loss_bottom_decile': np.float64(11.707078), 'test_avg_loss_top_decile': np.float64(15.08239), 'test_avg_loss_min': np.float64(11.500203), 'test_avg_loss_max': np.float64(15.08239), 'test_avg_loss_bottom10%': np.float64(11.500203), 'test_avg_loss_top10%': np.float64(15.08239), 'test_avg_loss_cos1': np.float64(0.995239), 'test_avg_loss_entropy': np.float64(2.297873), 'val_loss_std': np.float64(7787.383969), 'val_loss_bottom_decile': np.float64(67561.406639), 'val_loss_top_decile': np.float64(88155.563194), 'val_loss_min': np.float64(64665.991722), 'val_loss_max': np.float64(88155.563194), 'val_loss_bottom10%': np.float64(64665.991722), 'val_loss_top10%': np.float64(88155.563194), 'val_loss_cos1': np.float64(0.994658), 'val_loss_entropy': np.float64(2.297281), 'val_avg_loss_std': np.float64(1.382703), 'val_avg_loss_bottom_decile': np.float64(11.995988), 'val_avg_loss_top_decile': np.float64(15.652621), 'val_avg_loss_min': np.float64(11.481888), 'val_avg_loss_max': np.float64(15.652621), 'val_avg_loss_bottom10%': np.float64(11.481888), 'val_avg_loss_top10%': np.float64(15.652621), 'val_avg_loss_cos1': np.float64(0.994658), 'val_avg_loss_entropy': np.float64(2.297281)}}
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2024-11-17 01:18:47,735 (server:353) INFO: Server: Starting evaluation at the end of round 31.
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2024-11-17 01:18:47,736 (server:359) INFO: ----------- Starting a new training round (Round #32) -------------
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2024-11-17 01:35:23,514 (client:354) INFO: {'Role': 'Client #5', 'Round': 32, 'Results_raw': {'train_loss': 8.741951, 'val_loss': 8.640592, 'test_loss': 8.548051}}
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2024-11-17 01:42:09,178 (client:354) INFO: {'Role': 'Client #10', 'Round': 32, 'Results_raw': {'train_loss': 8.604644, 'val_loss': 8.341405, 'test_loss': 8.168044}}
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2024-11-17 01:48:45,926 (client:354) INFO: {'Role': 'Client #3', 'Round': 32, 'Results_raw': {'train_loss': 8.826102, 'val_loss': 8.714343, 'test_loss': 8.974502}}
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2024-11-17 01:55:06,577 (client:354) INFO: {'Role': 'Client #2', 'Round': 32, 'Results_raw': {'train_loss': 7.98492, 'val_loss': 7.848697, 'test_loss': 7.785619}}
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2024-11-17 02:02:17,013 (client:354) INFO: {'Role': 'Client #7', 'Round': 32, 'Results_raw': {'train_loss': 8.980482, 'val_loss': 8.548731, 'test_loss': 8.513754}}
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2024-11-17 02:08:56,490 (client:354) INFO: {'Role': 'Client #8', 'Round': 32, 'Results_raw': {'train_loss': 8.746209, 'val_loss': 8.798516, 'test_loss': 8.754154}}
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2024-11-17 02:15:28,873 (client:354) INFO: {'Role': 'Client #9', 'Round': 32, 'Results_raw': {'train_loss': 8.957352, 'val_loss': 9.03052, 'test_loss': 8.694766}}
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2024-11-17 02:21:58,487 (client:354) INFO: {'Role': 'Client #1', 'Round': 32, 'Results_raw': {'train_loss': 9.07878, 'val_loss': 8.899663, 'test_loss': 8.570855}}
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2024-11-17 02:28:10,350 (client:354) INFO: {'Role': 'Client #4', 'Round': 32, 'Results_raw': {'train_loss': 8.91386, 'val_loss': 9.07117, 'test_loss': 8.481596}}
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2024-11-17 02:34:12,211 (client:354) INFO: {'Role': 'Client #6', 'Round': 32, 'Results_raw': {'train_loss': 9.242784, 'val_loss': 9.06167, 'test_loss': 8.865535}}
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2024-11-17 02:34:12,215 (server:615) INFO: {'Role': 'Server #', 'Round': 31, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(73006.217612), 'test_avg_loss': np.float64(12.962752), 'val_total': np.float64(5632.0), 'val_loss': np.float64(75049.618336), 'val_avg_loss': np.float64(13.325571)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(73006.217612), 'test_avg_loss': np.float64(12.962752), 'val_total': np.float64(5632.0), 'val_loss': np.float64(75049.618336), 'val_avg_loss': np.float64(13.325571)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7250.612305), 'test_loss_bottom_decile': np.float64(65694.856033), 'test_loss_top_decile': np.float64(85172.748642), 'test_loss_min': np.float64(64639.751053), 'test_loss_max': np.float64(85172.748642), 'test_loss_bottom10%': np.float64(64639.751053), 'test_loss_top10%': np.float64(85172.748642), 'test_loss_cos1': np.float64(0.995104), 'test_loss_entropy': np.float64(2.297739), 'test_avg_loss_std': np.float64(1.287396), 'test_avg_loss_bottom_decile': np.float64(11.66457), 'test_avg_loss_top_decile': np.float64(15.123002), 'test_avg_loss_min': np.float64(11.477229), 'test_avg_loss_max': np.float64(15.123002), 'test_avg_loss_bottom10%': np.float64(11.477229), 'test_avg_loss_top10%': np.float64(15.123002), 'test_avg_loss_cos1': np.float64(0.995104), 'test_avg_loss_entropy': np.float64(2.297739), 'val_loss_std': np.float64(7894.864837), 'val_loss_bottom_decile': np.float64(67338.748482), 'val_loss_top_decile': np.float64(88528.216957), 'val_loss_min': np.float64(64590.0308), 'val_loss_max': np.float64(88528.216957), 'val_loss_bottom10%': np.float64(64590.0308), 'val_loss_top10%': np.float64(88528.216957), 'val_loss_cos1': np.float64(0.994512), 'val_loss_entropy': np.float64(2.297137), 'val_avg_loss_std': np.float64(1.401787), 'val_avg_loss_bottom_decile': np.float64(11.956454), 'val_avg_loss_top_decile': np.float64(15.718789), 'val_avg_loss_min': np.float64(11.4684), 'val_avg_loss_max': np.float64(15.718789), 'val_avg_loss_bottom10%': np.float64(11.4684), 'val_avg_loss_top10%': np.float64(15.718789), 'val_avg_loss_cos1': np.float64(0.994512), 'val_avg_loss_entropy': np.float64(2.297137)}}
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2024-11-17 02:34:12,257 (server:353) INFO: Server: Starting evaluation at the end of round 32.
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2024-11-17 02:34:12,258 (server:359) INFO: ----------- Starting a new training round (Round #33) -------------
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2024-11-17 02:49:01,694 (client:354) INFO: {'Role': 'Client #9', 'Round': 33, 'Results_raw': {'train_loss': 8.947782, 'val_loss': 8.944794, 'test_loss': 8.57966}}
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2024-11-17 02:55:33,219 (client:354) INFO: {'Role': 'Client #8', 'Round': 33, 'Results_raw': {'train_loss': 8.739095, 'val_loss': 8.908689, 'test_loss': 8.834656}}
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2024-11-17 03:01:35,332 (client:354) INFO: {'Role': 'Client #7', 'Round': 33, 'Results_raw': {'train_loss': 8.969058, 'val_loss': 8.37605, 'test_loss': 8.386077}}
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2024-11-17 03:07:47,464 (client:354) INFO: {'Role': 'Client #10', 'Round': 33, 'Results_raw': {'train_loss': 8.572958, 'val_loss': 8.334252, 'test_loss': 8.160723}}
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2024-11-17 03:13:43,451 (client:354) INFO: {'Role': 'Client #1', 'Round': 33, 'Results_raw': {'train_loss': 9.061891, 'val_loss': 8.764393, 'test_loss': 8.465286}}
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2024-11-17 03:20:05,902 (client:354) INFO: {'Role': 'Client #5', 'Round': 33, 'Results_raw': {'train_loss': 8.710527, 'val_loss': 8.696909, 'test_loss': 8.620231}}
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2024-11-17 03:26:21,808 (client:354) INFO: {'Role': 'Client #3', 'Round': 33, 'Results_raw': {'train_loss': 8.830349, 'val_loss': 8.580156, 'test_loss': 8.841634}}
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2024-11-17 03:32:16,074 (client:354) INFO: {'Role': 'Client #6', 'Round': 33, 'Results_raw': {'train_loss': 9.20439, 'val_loss': 9.012047, 'test_loss': 8.82554}}
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2024-11-17 03:39:02,776 (client:354) INFO: {'Role': 'Client #2', 'Round': 33, 'Results_raw': {'train_loss': 7.959795, 'val_loss': 7.934939, 'test_loss': 7.832236}}
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2024-11-17 03:46:13,688 (client:354) INFO: {'Role': 'Client #4', 'Round': 33, 'Results_raw': {'train_loss': 8.878825, 'val_loss': 9.249336, 'test_loss': 8.66108}}
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2024-11-17 03:46:13,691 (server:615) INFO: {'Role': 'Server #', 'Round': 32, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72056.382498), 'test_avg_loss': np.float64(12.794102), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74025.925713), 'val_avg_loss': np.float64(13.143808)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72056.382498), 'test_avg_loss': np.float64(12.794102), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74025.925713), 'val_avg_loss': np.float64(13.143808)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6903.536444), 'test_loss_bottom_decile': np.float64(65175.073875), 'test_loss_top_decile': np.float64(84208.056419), 'test_loss_min': np.float64(64106.382614), 'test_loss_max': np.float64(84208.056419), 'test_loss_bottom10%': np.float64(64106.382614), 'test_loss_top10%': np.float64(84208.056419), 'test_loss_cos1': np.float64(0.995442), 'test_loss_entropy': np.float64(2.298074), 'test_avg_loss_std': np.float64(1.22577), 'test_avg_loss_bottom_decile': np.float64(11.572279), 'test_avg_loss_top_decile': np.float64(14.951715), 'test_avg_loss_min': np.float64(11.382525), 'test_avg_loss_max': np.float64(14.951715), 'test_avg_loss_bottom10%': np.float64(11.382525), 'test_avg_loss_top10%': np.float64(14.951715), 'test_avg_loss_cos1': np.float64(0.995442), 'test_avg_loss_entropy': np.float64(2.298074), 'val_loss_std': np.float64(7532.484324), 'val_loss_bottom_decile': np.float64(66797.04644), 'val_loss_top_decile': np.float64(87380.708504), 'val_loss_min': np.float64(64007.2435), 'val_loss_max': np.float64(87380.708504), 'val_loss_bottom10%': np.float64(64007.2435), 'val_loss_top10%': np.float64(87380.708504), 'val_loss_cos1': np.float64(0.994863), 'val_loss_entropy': np.float64(2.297486), 'val_avg_loss_std': np.float64(1.337444), 'val_avg_loss_bottom_decile': np.float64(11.860271), 'val_avg_loss_top_decile': np.float64(15.515041), 'val_avg_loss_min': np.float64(11.364922), 'val_avg_loss_max': np.float64(15.515041), 'val_avg_loss_bottom10%': np.float64(11.364922), 'val_avg_loss_top10%': np.float64(15.515041), 'val_avg_loss_cos1': np.float64(0.994863), 'val_avg_loss_entropy': np.float64(2.297486)}}
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2024-11-17 03:46:13,754 (server:353) INFO: Server: Starting evaluation at the end of round 33.
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2024-11-17 03:46:13,755 (server:359) INFO: ----------- Starting a new training round (Round #34) -------------
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2024-11-17 04:01:57,460 (client:354) INFO: {'Role': 'Client #4', 'Round': 34, 'Results_raw': {'train_loss': 8.897286, 'val_loss': 9.158414, 'test_loss': 8.542029}}
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2024-11-17 04:08:55,850 (client:354) INFO: {'Role': 'Client #7', 'Round': 34, 'Results_raw': {'train_loss': 8.954839, 'val_loss': 8.461059, 'test_loss': 8.502044}}
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2024-11-17 04:16:32,623 (client:354) INFO: {'Role': 'Client #8', 'Round': 34, 'Results_raw': {'train_loss': 8.70685, 'val_loss': 8.770841, 'test_loss': 8.698806}}
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2024-11-17 04:23:04,957 (client:354) INFO: {'Role': 'Client #10', 'Round': 34, 'Results_raw': {'train_loss': 8.569036, 'val_loss': 8.315751, 'test_loss': 8.151772}}
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2024-11-17 04:29:25,797 (client:354) INFO: {'Role': 'Client #5', 'Round': 34, 'Results_raw': {'train_loss': 8.687814, 'val_loss': 8.651518, 'test_loss': 8.576211}}
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2024-11-17 04:35:47,591 (client:354) INFO: {'Role': 'Client #2', 'Round': 34, 'Results_raw': {'train_loss': 7.955377, 'val_loss': 7.797654, 'test_loss': 7.767188}}
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2024-11-17 04:42:02,586 (client:354) INFO: {'Role': 'Client #9', 'Round': 34, 'Results_raw': {'train_loss': 8.936809, 'val_loss': 9.022004, 'test_loss': 8.636169}}
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2024-11-17 04:48:29,502 (client:354) INFO: {'Role': 'Client #3', 'Round': 34, 'Results_raw': {'train_loss': 8.790142, 'val_loss': 8.691109, 'test_loss': 8.97359}}
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2024-11-17 04:55:11,894 (client:354) INFO: {'Role': 'Client #1', 'Round': 34, 'Results_raw': {'train_loss': 9.041972, 'val_loss': 8.848219, 'test_loss': 8.551185}}
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2024-11-17 05:01:29,099 (client:354) INFO: {'Role': 'Client #6', 'Round': 34, 'Results_raw': {'train_loss': 9.167094, 'val_loss': 9.025184, 'test_loss': 8.831401}}
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2024-11-17 05:01:29,106 (server:615) INFO: {'Role': 'Server #', 'Round': 33, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72764.569807), 'test_avg_loss': np.float64(12.919845), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74809.368805), 'val_avg_loss': np.float64(13.282913)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72764.569807), 'test_avg_loss': np.float64(12.919845), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74809.368805), 'val_avg_loss': np.float64(13.282913)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7291.002861), 'test_loss_bottom_decile': np.float64(65479.109016), 'test_loss_top_decile': np.float64(85745.594955), 'test_loss_min': np.float64(64472.114265), 'test_loss_max': np.float64(85745.594955), 'test_loss_bottom10%': np.float64(64472.114265), 'test_loss_top10%': np.float64(85745.594955), 'test_loss_cos1': np.float64(0.995017), 'test_loss_entropy': np.float64(2.297656), 'test_avg_loss_std': np.float64(1.294567), 'test_avg_loss_bottom_decile': np.float64(11.626262), 'test_avg_loss_top_decile': np.float64(15.224715), 'test_avg_loss_min': np.float64(11.447463), 'test_avg_loss_max': np.float64(15.224715), 'test_avg_loss_bottom10%': np.float64(11.447463), 'test_avg_loss_top10%': np.float64(15.224715), 'test_avg_loss_cos1': np.float64(0.995017), 'test_avg_loss_entropy': np.float64(2.297656), 'val_loss_std': np.float64(7913.995908), 'val_loss_bottom_decile': np.float64(67137.894508), 'val_loss_top_decile': np.float64(88971.653976), 'val_loss_min': np.float64(64364.471733), 'val_loss_max': np.float64(88971.653976), 'val_loss_bottom10%': np.float64(64364.471733), 'val_loss_top10%': np.float64(88971.653976), 'val_loss_cos1': np.float64(0.994451), 'val_loss_entropy': np.float64(2.297078), 'val_avg_loss_std': np.float64(1.405184), 'val_avg_loss_bottom_decile': np.float64(11.920791), 'val_avg_loss_top_decile': np.float64(15.797524), 'val_avg_loss_min': np.float64(11.428351), 'val_avg_loss_max': np.float64(15.797524), 'val_avg_loss_bottom10%': np.float64(11.428351), 'val_avg_loss_top10%': np.float64(15.797524), 'val_avg_loss_cos1': np.float64(0.994451), 'val_avg_loss_entropy': np.float64(2.297078)}}
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2024-11-17 05:01:29,190 (server:353) INFO: Server: Starting evaluation at the end of round 34.
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2024-11-17 05:01:29,191 (server:359) INFO: ----------- Starting a new training round (Round #35) -------------
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2024-11-17 05:17:41,434 (client:354) INFO: {'Role': 'Client #3', 'Round': 35, 'Results_raw': {'train_loss': 8.81808, 'val_loss': 8.548211, 'test_loss': 8.829627}}
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2024-11-17 05:23:57,405 (client:354) INFO: {'Role': 'Client #9', 'Round': 35, 'Results_raw': {'train_loss': 8.900373, 'val_loss': 8.898314, 'test_loss': 8.566309}}
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2024-11-17 05:30:32,437 (client:354) INFO: {'Role': 'Client #2', 'Round': 35, 'Results_raw': {'train_loss': 7.932914, 'val_loss': 7.697841, 'test_loss': 7.640667}}
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2024-11-17 05:37:03,141 (client:354) INFO: {'Role': 'Client #8', 'Round': 35, 'Results_raw': {'train_loss': 8.69851, 'val_loss': 8.828095, 'test_loss': 8.795301}}
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2024-11-17 05:43:58,413 (client:354) INFO: {'Role': 'Client #5', 'Round': 35, 'Results_raw': {'train_loss': 8.688536, 'val_loss': 8.656642, 'test_loss': 8.533213}}
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2024-11-17 05:51:20,086 (client:354) INFO: {'Role': 'Client #4', 'Round': 35, 'Results_raw': {'train_loss': 8.866191, 'val_loss': 9.168565, 'test_loss': 8.585018}}
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2024-11-17 05:58:03,549 (client:354) INFO: {'Role': 'Client #6', 'Round': 35, 'Results_raw': {'train_loss': 9.154775, 'val_loss': 9.018936, 'test_loss': 8.845537}}
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2024-11-17 06:04:33,249 (client:354) INFO: {'Role': 'Client #7', 'Round': 35, 'Results_raw': {'train_loss': 8.92132, 'val_loss': 8.557131, 'test_loss': 8.512543}}
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2024-11-17 06:11:05,694 (client:354) INFO: {'Role': 'Client #10', 'Round': 35, 'Results_raw': {'train_loss': 8.552567, 'val_loss': 8.279611, 'test_loss': 8.106303}}
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2024-11-17 06:18:26,446 (client:354) INFO: {'Role': 'Client #1', 'Round': 35, 'Results_raw': {'train_loss': 9.012471, 'val_loss': 8.905056, 'test_loss': 8.627679}}
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2024-11-17 06:18:26,457 (server:615) INFO: {'Role': 'Server #', 'Round': 34, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71429.599676), 'test_avg_loss': np.float64(12.682812), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73316.455045), 'val_avg_loss': np.float64(13.017836)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71429.599676), 'test_avg_loss': np.float64(12.682812), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73316.455045), 'val_avg_loss': np.float64(13.017836)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6869.235323), 'test_loss_bottom_decile': np.float64(64634.543015), 'test_loss_top_decile': np.float64(84112.229469), 'test_loss_min': np.float64(63519.166359), 'test_loss_max': np.float64(84112.229469), 'test_loss_bottom10%': np.float64(63519.166359), 'test_loss_top10%': np.float64(84112.229469), 'test_loss_cos1': np.float64(0.995408), 'test_loss_entropy': np.float64(2.298046), 'test_avg_loss_std': np.float64(1.21968), 'test_avg_loss_bottom_decile': np.float64(11.476304), 'test_avg_loss_top_decile': np.float64(14.9347), 'test_avg_loss_min': np.float64(11.278261), 'test_avg_loss_max': np.float64(14.9347), 'test_avg_loss_bottom10%': np.float64(11.278261), 'test_avg_loss_top10%': np.float64(14.9347), 'test_avg_loss_cos1': np.float64(0.995408), 'test_avg_loss_entropy': np.float64(2.298046), 'val_loss_std': np.float64(7458.170008), 'val_loss_bottom_decile': np.float64(66158.514938), 'val_loss_top_decile': np.float64(87057.583138), 'val_loss_min': np.float64(63442.400696), 'val_loss_max': np.float64(87057.583138), 'val_loss_bottom10%': np.float64(63442.400696), 'val_loss_top10%': np.float64(87057.583138), 'val_loss_cos1': np.float64(0.994866), 'val_loss_entropy': np.float64(2.297493), 'val_avg_loss_std': np.float64(1.324249), 'val_avg_loss_bottom_decile': np.float64(11.746895), 'val_avg_loss_top_decile': np.float64(15.457667), 'val_avg_loss_min': np.float64(11.264631), 'val_avg_loss_max': np.float64(15.457667), 'val_avg_loss_bottom10%': np.float64(11.264631), 'val_avg_loss_top10%': np.float64(15.457667), 'val_avg_loss_cos1': np.float64(0.994866), 'val_avg_loss_entropy': np.float64(2.297493)}}
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2024-11-17 06:18:26,588 (server:353) INFO: Server: Starting evaluation at the end of round 35.
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2024-11-17 06:18:26,592 (server:359) INFO: ----------- Starting a new training round (Round #36) -------------
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2024-11-17 06:35:18,204 (client:354) INFO: {'Role': 'Client #9', 'Round': 36, 'Results_raw': {'train_loss': 8.884371, 'val_loss': 8.906704, 'test_loss': 8.552227}}
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2024-11-17 06:41:36,071 (client:354) INFO: {'Role': 'Client #6', 'Round': 36, 'Results_raw': {'train_loss': 9.120202, 'val_loss': 9.101946, 'test_loss': 8.950147}}
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2024-11-17 06:48:34,437 (client:354) INFO: {'Role': 'Client #8', 'Round': 36, 'Results_raw': {'train_loss': 8.680886, 'val_loss': 8.823581, 'test_loss': 8.747589}}
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2024-11-17 06:55:04,725 (client:354) INFO: {'Role': 'Client #5', 'Round': 36, 'Results_raw': {'train_loss': 8.675378, 'val_loss': 8.671132, 'test_loss': 8.597911}}
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2024-11-17 07:01:28,823 (client:354) INFO: {'Role': 'Client #7', 'Round': 36, 'Results_raw': {'train_loss': 8.898761, 'val_loss': 8.652382, 'test_loss': 8.624309}}
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2024-11-17 07:08:06,275 (client:354) INFO: {'Role': 'Client #4', 'Round': 36, 'Results_raw': {'train_loss': 8.850456, 'val_loss': 9.136703, 'test_loss': 8.530398}}
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2024-11-17 07:14:37,750 (client:354) INFO: {'Role': 'Client #3', 'Round': 36, 'Results_raw': {'train_loss': 8.751317, 'val_loss': 8.528291, 'test_loss': 8.811811}}
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2024-11-17 07:21:05,797 (client:354) INFO: {'Role': 'Client #2', 'Round': 36, 'Results_raw': {'train_loss': 7.900135, 'val_loss': 7.835805, 'test_loss': 7.743822}}
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2024-11-17 07:28:07,254 (client:354) INFO: {'Role': 'Client #10', 'Round': 36, 'Results_raw': {'train_loss': 8.527021, 'val_loss': 8.317467, 'test_loss': 8.095592}}
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2024-11-17 07:35:11,135 (client:354) INFO: {'Role': 'Client #1', 'Round': 36, 'Results_raw': {'train_loss': 9.000321, 'val_loss': 8.882652, 'test_loss': 8.614483}}
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2024-11-17 07:35:11,139 (server:615) INFO: {'Role': 'Server #', 'Round': 35, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72536.146425), 'test_avg_loss': np.float64(12.879287), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74536.392567), 'val_avg_loss': np.float64(13.234445)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72536.146425), 'test_avg_loss': np.float64(12.879287), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74536.392567), 'val_avg_loss': np.float64(13.234445)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7195.01196), 'test_loss_bottom_decile': np.float64(65338.041931), 'test_loss_top_decile': np.float64(85224.769585), 'test_loss_min': np.float64(64282.987709), 'test_loss_max': np.float64(85224.769585), 'test_loss_bottom10%': np.float64(64282.987709), 'test_loss_top10%': np.float64(85224.769585), 'test_loss_cos1': np.float64(0.995116), 'test_loss_entropy': np.float64(2.297756), 'test_avg_loss_std': np.float64(1.277523), 'test_avg_loss_bottom_decile': np.float64(11.601215), 'test_avg_loss_top_decile': np.float64(15.132239), 'test_avg_loss_min': np.float64(11.413883), 'test_avg_loss_max': np.float64(15.132239), 'test_avg_loss_bottom10%': np.float64(11.413883), 'test_avg_loss_top10%': np.float64(15.132239), 'test_avg_loss_cos1': np.float64(0.995116), 'test_avg_loss_entropy': np.float64(2.297756), 'val_loss_std': np.float64(7799.436708), 'val_loss_bottom_decile': np.float64(66889.423775), 'val_loss_top_decile': np.float64(88485.221634), 'val_loss_min': np.float64(64192.649788), 'val_loss_max': np.float64(88485.221634), 'val_loss_bottom10%': np.float64(64192.649788), 'val_loss_top10%': np.float64(88485.221634), 'val_loss_cos1': np.float64(0.99457), 'val_loss_entropy': np.float64(2.297198), 'val_avg_loss_std': np.float64(1.384843), 'val_avg_loss_bottom_decile': np.float64(11.876673), 'val_avg_loss_top_decile': np.float64(15.711154), 'val_avg_loss_min': np.float64(11.397843), 'val_avg_loss_max': np.float64(15.711154), 'val_avg_loss_bottom10%': np.float64(11.397843), 'val_avg_loss_top10%': np.float64(15.711154), 'val_avg_loss_cos1': np.float64(0.99457), 'val_avg_loss_entropy': np.float64(2.297198)}}
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2024-11-17 07:35:11,199 (server:353) INFO: Server: Starting evaluation at the end of round 36.
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2024-11-17 07:35:11,201 (server:359) INFO: ----------- Starting a new training round (Round #37) -------------
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2024-11-17 07:51:01,291 (client:354) INFO: {'Role': 'Client #9', 'Round': 37, 'Results_raw': {'train_loss': 8.883636, 'val_loss': 8.906954, 'test_loss': 8.589549}}
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2024-11-17 07:57:33,591 (client:354) INFO: {'Role': 'Client #8', 'Round': 37, 'Results_raw': {'train_loss': 8.667142, 'val_loss': 8.884942, 'test_loss': 8.79597}}
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2024-11-17 08:04:23,218 (client:354) INFO: {'Role': 'Client #1', 'Round': 37, 'Results_raw': {'train_loss': 8.984919, 'val_loss': 8.835187, 'test_loss': 8.529355}}
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2024-11-17 08:11:53,823 (client:354) INFO: {'Role': 'Client #2', 'Round': 37, 'Results_raw': {'train_loss': 7.922059, 'val_loss': 7.786874, 'test_loss': 7.713455}}
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2024-11-17 08:19:14,024 (client:354) INFO: {'Role': 'Client #4', 'Round': 37, 'Results_raw': {'train_loss': 8.810002, 'val_loss': 9.356955, 'test_loss': 8.731564}}
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2024-11-17 08:26:01,948 (client:354) INFO: {'Role': 'Client #5', 'Round': 37, 'Results_raw': {'train_loss': 8.649471, 'val_loss': 8.622205, 'test_loss': 8.50777}}
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2024-11-17 08:33:36,149 (client:354) INFO: {'Role': 'Client #3', 'Round': 37, 'Results_raw': {'train_loss': 8.741456, 'val_loss': 8.470599, 'test_loss': 8.778077}}
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2024-11-17 08:40:36,088 (client:354) INFO: {'Role': 'Client #6', 'Round': 37, 'Results_raw': {'train_loss': 9.107137, 'val_loss': 9.097777, 'test_loss': 8.935751}}
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2024-11-17 08:46:51,197 (client:354) INFO: {'Role': 'Client #10', 'Round': 37, 'Results_raw': {'train_loss': 8.517156, 'val_loss': 8.329542, 'test_loss': 8.13736}}
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2024-11-17 08:53:40,178 (client:354) INFO: {'Role': 'Client #7', 'Round': 37, 'Results_raw': {'train_loss': 8.919881, 'val_loss': 8.299707, 'test_loss': 8.304461}}
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2024-11-17 08:53:40,192 (server:615) INFO: {'Role': 'Server #', 'Round': 36, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72942.657854), 'test_avg_loss': np.float64(12.951466), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74984.185204), 'val_avg_loss': np.float64(13.313953)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72942.657854), 'test_avg_loss': np.float64(12.951466), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74984.185204), 'val_avg_loss': np.float64(13.313953)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7309.414355), 'test_loss_bottom_decile': np.float64(65665.063148), 'test_loss_top_decile': np.float64(85537.727661), 'test_loss_min': np.float64(64679.567833), 'test_loss_max': np.float64(85537.727661), 'test_loss_bottom10%': np.float64(64679.567833), 'test_loss_top10%': np.float64(85537.727661), 'test_loss_cos1': np.float64(0.995017), 'test_loss_entropy': np.float64(2.297654), 'test_avg_loss_std': np.float64(1.297836), 'test_avg_loss_bottom_decile': np.float64(11.65928), 'test_avg_loss_top_decile': np.float64(15.187807), 'test_avg_loss_min': np.float64(11.484298), 'test_avg_loss_max': np.float64(15.187807), 'test_avg_loss_bottom10%': np.float64(11.484298), 'test_avg_loss_top10%': np.float64(15.187807), 'test_avg_loss_cos1': np.float64(0.995017), 'test_avg_loss_entropy': np.float64(2.297654), 'val_loss_std': np.float64(7976.716653), 'val_loss_bottom_decile': np.float64(67288.818901), 'val_loss_top_decile': np.float64(88947.642189), 'val_loss_min': np.float64(64477.995171), 'val_loss_max': np.float64(88947.642189), 'val_loss_bottom10%': np.float64(64477.995171), 'val_loss_top10%': np.float64(88947.642189), 'val_loss_cos1': np.float64(0.994389), 'val_loss_entropy': np.float64(2.297017), 'val_avg_loss_std': np.float64(1.41632), 'val_avg_loss_bottom_decile': np.float64(11.947589), 'val_avg_loss_top_decile': np.float64(15.79326), 'val_avg_loss_min': np.float64(11.448508), 'val_avg_loss_max': np.float64(15.79326), 'val_avg_loss_bottom10%': np.float64(11.448508), 'val_avg_loss_top10%': np.float64(15.79326), 'val_avg_loss_cos1': np.float64(0.994389), 'val_avg_loss_entropy': np.float64(2.297017)}}
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2024-11-17 08:53:40,294 (server:353) INFO: Server: Starting evaluation at the end of round 37.
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2024-11-17 08:53:40,297 (server:359) INFO: ----------- Starting a new training round (Round #38) -------------
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2024-11-17 09:09:44,275 (client:354) INFO: {'Role': 'Client #4', 'Round': 38, 'Results_raw': {'train_loss': 8.822273, 'val_loss': 9.125012, 'test_loss': 8.543493}}
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2024-11-17 09:16:12,031 (client:354) INFO: {'Role': 'Client #2', 'Round': 38, 'Results_raw': {'train_loss': 7.864521, 'val_loss': 7.74483, 'test_loss': 7.662137}}
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2024-11-17 09:23:14,567 (client:354) INFO: {'Role': 'Client #9', 'Round': 38, 'Results_raw': {'train_loss': 8.862121, 'val_loss': 9.098115, 'test_loss': 8.676877}}
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2024-11-17 09:29:35,215 (client:354) INFO: {'Role': 'Client #6', 'Round': 38, 'Results_raw': {'train_loss': 9.085005, 'val_loss': 8.965285, 'test_loss': 8.813238}}
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2024-11-17 09:36:36,696 (client:354) INFO: {'Role': 'Client #5', 'Round': 38, 'Results_raw': {'train_loss': 8.6535, 'val_loss': 8.629483, 'test_loss': 8.526495}}
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2024-11-17 09:43:29,275 (client:354) INFO: {'Role': 'Client #7', 'Round': 38, 'Results_raw': {'train_loss': 8.877808, 'val_loss': 8.381819, 'test_loss': 8.465529}}
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2024-11-17 09:49:49,331 (client:354) INFO: {'Role': 'Client #10', 'Round': 38, 'Results_raw': {'train_loss': 8.489458, 'val_loss': 8.377399, 'test_loss': 8.223868}}
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2024-11-17 09:55:57,912 (client:354) INFO: {'Role': 'Client #3', 'Round': 38, 'Results_raw': {'train_loss': 8.708608, 'val_loss': 8.501205, 'test_loss': 8.795339}}
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2024-11-17 10:02:09,068 (client:354) INFO: {'Role': 'Client #1', 'Round': 38, 'Results_raw': {'train_loss': 8.974263, 'val_loss': 8.758995, 'test_loss': 8.490597}}
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2024-11-17 10:08:29,306 (client:354) INFO: {'Role': 'Client #8', 'Round': 38, 'Results_raw': {'train_loss': 8.659107, 'val_loss': 8.737229, 'test_loss': 8.648245}}
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2024-11-17 10:08:29,309 (server:615) INFO: {'Role': 'Server #', 'Round': 37, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71288.440396), 'test_avg_loss': np.float64(12.657749), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73196.153422), 'val_avg_loss': np.float64(12.996476)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71288.440396), 'test_avg_loss': np.float64(12.657749), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73196.153422), 'val_avg_loss': np.float64(12.996476)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6575.489035), 'test_loss_bottom_decile': np.float64(64396.7146), 'test_loss_top_decile': np.float64(82770.633263), 'test_loss_min': np.float64(63364.291359), 'test_loss_max': np.float64(82770.633263), 'test_loss_bottom10%': np.float64(63364.291359), 'test_loss_top10%': np.float64(82770.633263), 'test_loss_cos1': np.float64(0.995773), 'test_loss_entropy': np.float64(2.298394), 'test_avg_loss_std': np.float64(1.167523), 'test_avg_loss_bottom_decile': np.float64(11.434076), 'test_avg_loss_top_decile': np.float64(14.69649), 'test_avg_loss_min': np.float64(11.250762), 'test_avg_loss_max': np.float64(14.69649), 'test_avg_loss_bottom10%': np.float64(11.250762), 'test_avg_loss_top10%': np.float64(14.69649), 'test_avg_loss_cos1': np.float64(0.995773), 'test_avg_loss_entropy': np.float64(2.298394), 'val_loss_std': np.float64(7182.396263), 'val_loss_bottom_decile': np.float64(65916.283203), 'val_loss_top_decile': np.float64(85810.179169), 'val_loss_min': np.float64(63226.912041), 'val_loss_max': np.float64(85810.179169), 'val_loss_bottom10%': np.float64(63226.912041), 'val_loss_top10%': np.float64(85810.179169), 'val_loss_cos1': np.float64(0.99522), 'val_loss_entropy': np.float64(2.297829), 'val_avg_loss_std': np.float64(1.275283), 'val_avg_loss_bottom_decile': np.float64(11.703886), 'val_avg_loss_top_decile': np.float64(15.236182), 'val_avg_loss_min': np.float64(11.226369), 'val_avg_loss_max': np.float64(15.236182), 'val_avg_loss_bottom10%': np.float64(11.226369), 'val_avg_loss_top10%': np.float64(15.236182), 'val_avg_loss_cos1': np.float64(0.99522), 'val_avg_loss_entropy': np.float64(2.297829)}}
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2024-11-17 10:08:29,350 (server:353) INFO: Server: Starting evaluation at the end of round 38.
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2024-11-17 10:08:29,351 (server:359) INFO: ----------- Starting a new training round (Round #39) -------------
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2024-11-17 10:24:21,663 (client:354) INFO: {'Role': 'Client #5', 'Round': 39, 'Results_raw': {'train_loss': 8.623272, 'val_loss': 8.667378, 'test_loss': 8.562937}}
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2024-11-17 10:30:56,061 (client:354) INFO: {'Role': 'Client #10', 'Round': 39, 'Results_raw': {'train_loss': 8.467894, 'val_loss': 8.250079, 'test_loss': 8.057586}}
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2024-11-17 10:37:50,454 (client:354) INFO: {'Role': 'Client #7', 'Round': 39, 'Results_raw': {'train_loss': 8.869631, 'val_loss': 8.69263, 'test_loss': 8.707474}}
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2024-11-17 10:44:44,608 (client:354) INFO: {'Role': 'Client #3', 'Round': 39, 'Results_raw': {'train_loss': 8.694086, 'val_loss': 8.550398, 'test_loss': 8.840471}}
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2024-11-17 10:52:00,624 (client:354) INFO: {'Role': 'Client #8', 'Round': 39, 'Results_raw': {'train_loss': 8.628341, 'val_loss': 8.757695, 'test_loss': 8.712077}}
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2024-11-17 10:58:30,509 (client:354) INFO: {'Role': 'Client #4', 'Round': 39, 'Results_raw': {'train_loss': 8.802562, 'val_loss': 8.990195, 'test_loss': 8.392772}}
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2024-11-17 11:05:06,257 (client:354) INFO: {'Role': 'Client #2', 'Round': 39, 'Results_raw': {'train_loss': 7.876735, 'val_loss': 7.697502, 'test_loss': 7.642088}}
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2024-11-17 11:11:23,957 (client:354) INFO: {'Role': 'Client #6', 'Round': 39, 'Results_raw': {'train_loss': 9.096653, 'val_loss': 9.005831, 'test_loss': 8.856568}}
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2024-11-17 11:17:45,986 (client:354) INFO: {'Role': 'Client #1', 'Round': 39, 'Results_raw': {'train_loss': 8.957864, 'val_loss': 8.771302, 'test_loss': 8.485205}}
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2024-11-17 11:24:20,295 (client:354) INFO: {'Role': 'Client #9', 'Round': 39, 'Results_raw': {'train_loss': 8.83294, 'val_loss': 8.982923, 'test_loss': 8.657983}}
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2024-11-17 11:24:20,299 (server:615) INFO: {'Role': 'Server #', 'Round': 38, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72332.615008), 'test_avg_loss': np.float64(12.843149), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74404.883562), 'val_avg_loss': np.float64(13.211094)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(72332.615008), 'test_avg_loss': np.float64(12.843149), 'val_total': np.float64(5632.0), 'val_loss': np.float64(74404.883562), 'val_avg_loss': np.float64(13.211094)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7114.860507), 'test_loss_bottom_decile': np.float64(65264.913277), 'test_loss_top_decile': np.float64(84835.088448), 'test_loss_min': np.float64(63823.749672), 'test_loss_max': np.float64(84835.088448), 'test_loss_bottom10%': np.float64(63823.749672), 'test_loss_top10%': np.float64(84835.088448), 'test_loss_cos1': np.float64(0.995197), 'test_loss_entropy': np.float64(2.297827), 'test_avg_loss_std': np.float64(1.263292), 'test_avg_loss_bottom_decile': np.float64(11.58823), 'test_avg_loss_top_decile': np.float64(15.063048), 'test_avg_loss_min': np.float64(11.332342), 'test_avg_loss_max': np.float64(15.063048), 'test_avg_loss_bottom10%': np.float64(11.332342), 'test_avg_loss_top10%': np.float64(15.063048), 'test_avg_loss_cos1': np.float64(0.995197), 'test_avg_loss_entropy': np.float64(2.297827), 'val_loss_std': np.float64(7771.534209), 'val_loss_bottom_decile': np.float64(66878.738266), 'val_loss_top_decile': np.float64(88179.863853), 'val_loss_min': np.float64(63691.287178), 'val_loss_max': np.float64(88179.863853), 'val_loss_bottom10%': np.float64(63691.287178), 'val_loss_top10%': np.float64(88179.863853), 'val_loss_cos1': np.float64(0.994589), 'val_loss_entropy': np.float64(2.297207), 'val_avg_loss_std': np.float64(1.379889), 'val_avg_loss_bottom_decile': np.float64(11.874776), 'val_avg_loss_top_decile': np.float64(15.656936), 'val_avg_loss_min': np.float64(11.308822), 'val_avg_loss_max': np.float64(15.656936), 'val_avg_loss_bottom10%': np.float64(11.308822), 'val_avg_loss_top10%': np.float64(15.656936), 'val_avg_loss_cos1': np.float64(0.994589), 'val_avg_loss_entropy': np.float64(2.297207)}}
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2024-11-17 11:24:20,338 (server:353) INFO: Server: Starting evaluation at the end of round 39.
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2024-11-17 11:24:20,339 (server:359) INFO: ----------- Starting a new training round (Round #40) -------------
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2024-11-17 11:39:41,021 (client:354) INFO: {'Role': 'Client #3', 'Round': 40, 'Results_raw': {'train_loss': 8.706814, 'val_loss': 8.438394, 'test_loss': 8.735944}}
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2024-11-17 11:46:02,641 (client:354) INFO: {'Role': 'Client #5', 'Round': 40, 'Results_raw': {'train_loss': 8.597354, 'val_loss': 8.579752, 'test_loss': 8.463764}}
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2024-11-17 11:52:10,846 (client:354) INFO: {'Role': 'Client #4', 'Round': 40, 'Results_raw': {'train_loss': 8.773494, 'val_loss': 8.993904, 'test_loss': 8.428759}}
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2024-11-17 11:58:37,975 (client:354) INFO: {'Role': 'Client #6', 'Round': 40, 'Results_raw': {'train_loss': 9.05247, 'val_loss': 8.899753, 'test_loss': 8.76013}}
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2024-11-17 12:05:30,162 (client:354) INFO: {'Role': 'Client #7', 'Round': 40, 'Results_raw': {'train_loss': 8.833339, 'val_loss': 8.530149, 'test_loss': 8.570998}}
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2024-11-17 12:11:56,145 (client:354) INFO: {'Role': 'Client #2', 'Round': 40, 'Results_raw': {'train_loss': 7.864915, 'val_loss': 7.733478, 'test_loss': 7.68639}}
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2024-11-17 12:18:46,651 (client:354) INFO: {'Role': 'Client #9', 'Round': 40, 'Results_raw': {'train_loss': 8.827381, 'val_loss': 8.855453, 'test_loss': 8.523451}}
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2024-11-17 12:25:29,393 (client:354) INFO: {'Role': 'Client #1', 'Round': 40, 'Results_raw': {'train_loss': 8.933617, 'val_loss': 8.821639, 'test_loss': 8.505586}}
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2024-11-17 12:32:45,213 (client:354) INFO: {'Role': 'Client #8', 'Round': 40, 'Results_raw': {'train_loss': 8.634963, 'val_loss': 8.74557, 'test_loss': 8.69954}}
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2024-11-17 12:39:53,548 (client:354) INFO: {'Role': 'Client #10', 'Round': 40, 'Results_raw': {'train_loss': 8.479875, 'val_loss': 8.348094, 'test_loss': 8.203926}}
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2024-11-17 12:39:53,556 (server:615) INFO: {'Role': 'Server #', 'Round': 39, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71558.713654), 'test_avg_loss': np.float64(12.705738), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73539.016439), 'val_avg_loss': np.float64(13.057354)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71558.713654), 'test_avg_loss': np.float64(12.705738), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73539.016439), 'val_avg_loss': np.float64(13.057354)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6606.292373), 'test_loss_bottom_decile': np.float64(64627.611343), 'test_loss_top_decile': np.float64(82699.183029), 'test_loss_min': np.float64(63718.489059), 'test_loss_max': np.float64(82699.183029), 'test_loss_bottom10%': np.float64(63718.489059), 'test_loss_top10%': np.float64(82699.183029), 'test_loss_cos1': np.float64(0.995766), 'test_loss_entropy': np.float64(2.298386), 'test_avg_loss_std': np.float64(1.172992), 'test_avg_loss_bottom_decile': np.float64(11.475073), 'test_avg_loss_top_decile': np.float64(14.683804), 'test_avg_loss_min': np.float64(11.313652), 'test_avg_loss_max': np.float64(14.683804), 'test_avg_loss_bottom10%': np.float64(11.313652), 'test_avg_loss_top10%': np.float64(14.683804), 'test_avg_loss_cos1': np.float64(0.995766), 'test_avg_loss_entropy': np.float64(2.298386), 'val_loss_std': np.float64(7239.795219), 'val_loss_bottom_decile': np.float64(66171.376869), 'val_loss_top_decile': np.float64(85888.45031), 'val_loss_min': np.float64(63613.688652), 'val_loss_max': np.float64(85888.45031), 'val_loss_bottom10%': np.float64(63613.688652), 'val_loss_top10%': np.float64(85888.45031), 'val_loss_cos1': np.float64(0.995189), 'val_loss_entropy': np.float64(2.297799), 'val_avg_loss_std': np.float64(1.285475), 'val_avg_loss_bottom_decile': np.float64(11.749179), 'val_avg_loss_top_decile': np.float64(15.25008), 'val_avg_loss_min': np.float64(11.295044), 'val_avg_loss_max': np.float64(15.25008), 'val_avg_loss_bottom10%': np.float64(11.295044), 'val_avg_loss_top10%': np.float64(15.25008), 'val_avg_loss_cos1': np.float64(0.995189), 'val_avg_loss_entropy': np.float64(2.297799)}}
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2024-11-17 12:39:53,648 (server:353) INFO: Server: Starting evaluation at the end of round 40.
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2024-11-17 12:39:53,649 (server:359) INFO: ----------- Starting a new training round (Round #41) -------------
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2024-11-17 12:56:07,774 (client:354) INFO: {'Role': 'Client #6', 'Round': 41, 'Results_raw': {'train_loss': 9.033482, 'val_loss': 8.902696, 'test_loss': 8.721783}}
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2024-11-17 13:02:54,378 (client:354) INFO: {'Role': 'Client #4', 'Round': 41, 'Results_raw': {'train_loss': 8.755393, 'val_loss': 8.972597, 'test_loss': 8.38303}}
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2024-11-17 13:09:59,946 (client:354) INFO: {'Role': 'Client #9', 'Round': 41, 'Results_raw': {'train_loss': 8.815714, 'val_loss': 8.926341, 'test_loss': 8.610572}}
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2024-11-17 13:16:53,252 (client:354) INFO: {'Role': 'Client #8', 'Round': 41, 'Results_raw': {'train_loss': 8.595968, 'val_loss': 8.65325, 'test_loss': 8.617323}}
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2024-11-17 13:23:19,703 (client:354) INFO: {'Role': 'Client #7', 'Round': 41, 'Results_raw': {'train_loss': 8.813686, 'val_loss': 8.433828, 'test_loss': 8.430055}}
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2024-11-17 13:30:03,370 (client:354) INFO: {'Role': 'Client #1', 'Round': 41, 'Results_raw': {'train_loss': 8.943487, 'val_loss': 8.684759, 'test_loss': 8.398346}}
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2024-11-17 13:36:12,025 (client:354) INFO: {'Role': 'Client #10', 'Round': 41, 'Results_raw': {'train_loss': 8.432309, 'val_loss': 8.296818, 'test_loss': 8.137771}}
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2024-11-17 13:42:04,025 (client:354) INFO: {'Role': 'Client #3', 'Round': 41, 'Results_raw': {'train_loss': 8.64621, 'val_loss': 8.483272, 'test_loss': 8.777661}}
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2024-11-17 13:48:29,616 (client:354) INFO: {'Role': 'Client #5', 'Round': 41, 'Results_raw': {'train_loss': 8.5927, 'val_loss': 8.631008, 'test_loss': 8.499212}}
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2024-11-17 13:54:34,329 (client:354) INFO: {'Role': 'Client #2', 'Round': 41, 'Results_raw': {'train_loss': 7.815797, 'val_loss': 7.800282, 'test_loss': 7.725892}}
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2024-11-17 13:54:34,333 (server:615) INFO: {'Role': 'Server #', 'Round': 40, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71359.510047), 'test_avg_loss': np.float64(12.670368), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73315.145235), 'val_avg_loss': np.float64(13.017604)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71359.510047), 'test_avg_loss': np.float64(12.670368), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73315.145235), 'val_avg_loss': np.float64(13.017604)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6786.838604), 'test_loss_bottom_decile': np.float64(64421.380783), 'test_loss_top_decile': np.float64(83102.611137), 'test_loss_min': np.float64(63463.411552), 'test_loss_max': np.float64(83102.611137), 'test_loss_bottom10%': np.float64(63463.411552), 'test_loss_top10%': np.float64(83102.611137), 'test_loss_cos1': np.float64(0.995508), 'test_loss_entropy': np.float64(2.298135), 'test_avg_loss_std': np.float64(1.205049), 'test_avg_loss_bottom_decile': np.float64(11.438455), 'test_avg_loss_top_decile': np.float64(14.755435), 'test_avg_loss_min': np.float64(11.268361), 'test_avg_loss_max': np.float64(14.755435), 'test_avg_loss_bottom10%': np.float64(11.268361), 'test_avg_loss_top10%': np.float64(14.755435), 'test_avg_loss_cos1': np.float64(0.995508), 'test_avg_loss_entropy': np.float64(2.298135), 'val_loss_std': np.float64(7423.847334), 'val_loss_bottom_decile': np.float64(66011.986862), 'val_loss_top_decile': np.float64(86245.897568), 'val_loss_min': np.float64(63281.190239), 'val_loss_max': np.float64(86245.897568), 'val_loss_bottom10%': np.float64(63281.190239), 'val_loss_top10%': np.float64(86245.897568), 'val_loss_cos1': np.float64(0.994912), 'val_loss_entropy': np.float64(2.29753), 'val_avg_loss_std': np.float64(1.318155), 'val_avg_loss_bottom_decile': np.float64(11.720878), 'val_avg_loss_top_decile': np.float64(15.313547), 'val_avg_loss_min': np.float64(11.236007), 'val_avg_loss_max': np.float64(15.313547), 'val_avg_loss_bottom10%': np.float64(11.236007), 'val_avg_loss_top10%': np.float64(15.313547), 'val_avg_loss_cos1': np.float64(0.994912), 'val_avg_loss_entropy': np.float64(2.29753)}}
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2024-11-17 13:54:34,399 (server:353) INFO: Server: Starting evaluation at the end of round 41.
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2024-11-17 13:54:34,400 (server:359) INFO: ----------- Starting a new training round (Round #42) -------------
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2024-11-17 14:09:51,682 (client:354) INFO: {'Role': 'Client #3', 'Round': 42, 'Results_raw': {'train_loss': 8.618901, 'val_loss': 8.418944, 'test_loss': 8.706962}}
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2024-11-17 14:16:04,632 (client:354) INFO: {'Role': 'Client #6', 'Round': 42, 'Results_raw': {'train_loss': 9.044577, 'val_loss': 9.08664, 'test_loss': 8.911053}}
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2024-11-17 14:22:29,014 (client:354) INFO: {'Role': 'Client #10', 'Round': 42, 'Results_raw': {'train_loss': 8.407998, 'val_loss': 8.240038, 'test_loss': 8.043804}}
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2024-11-17 14:28:47,701 (client:354) INFO: {'Role': 'Client #8', 'Round': 42, 'Results_raw': {'train_loss': 8.595913, 'val_loss': 8.768523, 'test_loss': 8.685294}}
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2024-11-17 14:34:36,048 (client:354) INFO: {'Role': 'Client #7', 'Round': 42, 'Results_raw': {'train_loss': 8.797553, 'val_loss': 8.373476, 'test_loss': 8.361438}}
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2024-11-17 14:41:01,449 (client:354) INFO: {'Role': 'Client #4', 'Round': 42, 'Results_raw': {'train_loss': 8.769695, 'val_loss': 9.009312, 'test_loss': 8.431743}}
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2024-11-17 14:47:00,282 (client:354) INFO: {'Role': 'Client #5', 'Round': 42, 'Results_raw': {'train_loss': 8.572241, 'val_loss': 8.583946, 'test_loss': 8.47006}}
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2024-11-17 14:53:57,336 (client:354) INFO: {'Role': 'Client #2', 'Round': 42, 'Results_raw': {'train_loss': 7.806518, 'val_loss': 7.800821, 'test_loss': 7.718757}}
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2024-11-17 15:00:17,880 (client:354) INFO: {'Role': 'Client #9', 'Round': 42, 'Results_raw': {'train_loss': 8.801463, 'val_loss': 9.068434, 'test_loss': 8.739767}}
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2024-11-17 15:06:25,787 (client:354) INFO: {'Role': 'Client #1', 'Round': 42, 'Results_raw': {'train_loss': 8.89758, 'val_loss': 8.79459, 'test_loss': 8.495454}}
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2024-11-17 15:06:25,790 (server:615) INFO: {'Role': 'Server #', 'Round': 41, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70369.266241), 'test_avg_loss': np.float64(12.494543), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72259.457159), 'val_avg_loss': np.float64(12.830159)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70369.266241), 'test_avg_loss': np.float64(12.494543), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72259.457159), 'val_avg_loss': np.float64(12.830159)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6372.959802), 'test_loss_bottom_decile': np.float64(63965.154518), 'test_loss_top_decile': np.float64(81238.138062), 'test_loss_min': np.float64(62887.890991), 'test_loss_max': np.float64(81238.138062), 'test_loss_bottom10%': np.float64(62887.890991), 'test_loss_top10%': np.float64(81238.138062), 'test_loss_cos1': np.float64(0.995924), 'test_loss_entropy': np.float64(2.298546), 'test_avg_loss_std': np.float64(1.131562), 'test_avg_loss_bottom_decile': np.float64(11.357449), 'test_avg_loss_top_decile': np.float64(14.424385), 'test_avg_loss_min': np.float64(11.166174), 'test_avg_loss_max': np.float64(14.424385), 'test_avg_loss_bottom10%': np.float64(11.166174), 'test_avg_loss_top10%': np.float64(14.424385), 'test_avg_loss_cos1': np.float64(0.995924), 'test_avg_loss_entropy': np.float64(2.298546), 'val_loss_std': np.float64(7001.417198), 'val_loss_bottom_decile': np.float64(65430.713997), 'val_loss_top_decile': np.float64(84245.342896), 'val_loss_min': np.float64(62788.237221), 'val_loss_max': np.float64(84245.342896), 'val_loss_bottom10%': np.float64(62788.237221), 'val_loss_top10%': np.float64(84245.342896), 'val_loss_cos1': np.float64(0.995339), 'val_loss_entropy': np.float64(2.297953), 'val_avg_loss_std': np.float64(1.243149), 'val_avg_loss_bottom_decile': np.float64(11.617669), 'val_avg_loss_top_decile': np.float64(14.958335), 'val_avg_loss_min': np.float64(11.14848), 'val_avg_loss_max': np.float64(14.958335), 'val_avg_loss_bottom10%': np.float64(11.14848), 'val_avg_loss_top10%': np.float64(14.958335), 'val_avg_loss_cos1': np.float64(0.995339), 'val_avg_loss_entropy': np.float64(2.297953)}}
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2024-11-17 15:06:25,830 (server:353) INFO: Server: Starting evaluation at the end of round 42.
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2024-11-17 15:06:25,831 (server:359) INFO: ----------- Starting a new training round (Round #43) -------------
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2024-11-17 15:21:59,236 (client:354) INFO: {'Role': 'Client #9', 'Round': 43, 'Results_raw': {'train_loss': 8.785062, 'val_loss': 8.888421, 'test_loss': 8.561625}}
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2024-11-17 15:28:24,907 (client:354) INFO: {'Role': 'Client #3', 'Round': 43, 'Results_raw': {'train_loss': 8.613325, 'val_loss': 8.501251, 'test_loss': 8.80122}}
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2024-11-17 15:35:49,431 (client:354) INFO: {'Role': 'Client #2', 'Round': 43, 'Results_raw': {'train_loss': 7.79603, 'val_loss': 7.635255, 'test_loss': 7.566564}}
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2024-11-17 15:42:40,600 (client:354) INFO: {'Role': 'Client #1', 'Round': 43, 'Results_raw': {'train_loss': 8.897911, 'val_loss': 8.713062, 'test_loss': 8.419694}}
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2024-11-17 15:49:19,953 (client:354) INFO: {'Role': 'Client #10', 'Round': 43, 'Results_raw': {'train_loss': 8.395408, 'val_loss': 8.297583, 'test_loss': 8.136976}}
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2024-11-17 15:55:29,579 (client:354) INFO: {'Role': 'Client #6', 'Round': 43, 'Results_raw': {'train_loss': 9.007004, 'val_loss': 8.946758, 'test_loss': 8.838976}}
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2024-11-17 16:01:45,921 (client:354) INFO: {'Role': 'Client #8', 'Round': 43, 'Results_raw': {'train_loss': 8.575208, 'val_loss': 8.70424, 'test_loss': 8.65164}}
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2024-11-17 16:08:39,398 (client:354) INFO: {'Role': 'Client #7', 'Round': 43, 'Results_raw': {'train_loss': 8.78829, 'val_loss': 8.395932, 'test_loss': 8.40772}}
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2024-11-17 16:15:11,143 (client:354) INFO: {'Role': 'Client #4', 'Round': 43, 'Results_raw': {'train_loss': 8.74724, 'val_loss': 9.087097, 'test_loss': 8.520614}}
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2024-11-17 16:21:29,191 (client:354) INFO: {'Role': 'Client #5', 'Round': 43, 'Results_raw': {'train_loss': 8.576168, 'val_loss': 8.504289, 'test_loss': 8.386358}}
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2024-11-17 16:21:29,210 (server:615) INFO: {'Role': 'Server #', 'Round': 42, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70560.617416), 'test_avg_loss': np.float64(12.528519), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72448.710167), 'val_avg_loss': np.float64(12.863762)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70560.617416), 'test_avg_loss': np.float64(12.528519), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72448.710167), 'val_avg_loss': np.float64(12.863762)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6730.585416), 'test_loss_bottom_decile': np.float64(63531.320862), 'test_loss_top_decile': np.float64(82804.149185), 'test_loss_min': np.float64(62666.013962), 'test_loss_max': np.float64(82804.149185), 'test_loss_bottom10%': np.float64(62666.013962), 'test_loss_top10%': np.float64(82804.149185), 'test_loss_cos1': np.float64(0.995481), 'test_loss_entropy': np.float64(2.298111), 'test_avg_loss_std': np.float64(1.195061), 'test_avg_loss_bottom_decile': np.float64(11.280419), 'test_avg_loss_top_decile': np.float64(14.702441), 'test_avg_loss_min': np.float64(11.126778), 'test_avg_loss_max': np.float64(14.702441), 'test_avg_loss_bottom10%': np.float64(11.126778), 'test_avg_loss_top10%': np.float64(14.702441), 'test_avg_loss_cos1': np.float64(0.995481), 'test_avg_loss_entropy': np.float64(2.298111), 'val_loss_std': np.float64(7322.490266), 'val_loss_bottom_decile': np.float64(65016.110527), 'val_loss_top_decile': np.float64(85835.116013), 'val_loss_min': np.float64(62584.783707), 'val_loss_max': np.float64(85835.116013), 'val_loss_bottom10%': np.float64(62584.783707), 'val_loss_top10%': np.float64(85835.116013), 'val_loss_cos1': np.float64(0.994931), 'val_loss_entropy': np.float64(2.29755), 'val_avg_loss_std': np.float64(1.300158), 'val_avg_loss_bottom_decile': np.float64(11.544054), 'val_avg_loss_top_decile': np.float64(15.24061), 'val_avg_loss_min': np.float64(11.112355), 'val_avg_loss_max': np.float64(15.24061), 'val_avg_loss_bottom10%': np.float64(11.112355), 'val_avg_loss_top10%': np.float64(15.24061), 'val_avg_loss_cos1': np.float64(0.994931), 'val_avg_loss_entropy': np.float64(2.29755)}}
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2024-11-17 16:21:29,258 (server:353) INFO: Server: Starting evaluation at the end of round 43.
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2024-11-17 16:21:29,259 (server:359) INFO: ----------- Starting a new training round (Round #44) -------------
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2024-11-17 16:37:04,926 (client:354) INFO: {'Role': 'Client #1', 'Round': 44, 'Results_raw': {'train_loss': 8.894134, 'val_loss': 8.688782, 'test_loss': 8.398248}}
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2024-11-17 16:44:01,925 (client:354) INFO: {'Role': 'Client #8', 'Round': 44, 'Results_raw': {'train_loss': 8.591436, 'val_loss': 8.693122, 'test_loss': 8.610548}}
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2024-11-17 16:50:19,752 (client:354) INFO: {'Role': 'Client #4', 'Round': 44, 'Results_raw': {'train_loss': 8.729216, 'val_loss': 8.966819, 'test_loss': 8.387952}}
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2024-11-17 16:57:00,966 (client:354) INFO: {'Role': 'Client #7', 'Round': 44, 'Results_raw': {'train_loss': 8.77708, 'val_loss': 8.304422, 'test_loss': 8.341188}}
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2024-11-17 17:03:17,491 (client:354) INFO: {'Role': 'Client #6', 'Round': 44, 'Results_raw': {'train_loss': 9.020881, 'val_loss': 9.235882, 'test_loss': 9.081705}}
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2024-11-17 17:10:06,448 (client:354) INFO: {'Role': 'Client #5', 'Round': 44, 'Results_raw': {'train_loss': 8.575629, 'val_loss': 8.526504, 'test_loss': 8.422653}}
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2024-11-17 17:17:00,926 (client:354) INFO: {'Role': 'Client #10', 'Round': 44, 'Results_raw': {'train_loss': 8.404423, 'val_loss': 8.417762, 'test_loss': 8.240662}}
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2024-11-17 17:24:31,285 (client:354) INFO: {'Role': 'Client #9', 'Round': 44, 'Results_raw': {'train_loss': 8.769284, 'val_loss': 8.957085, 'test_loss': 8.6042}}
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2024-11-17 17:31:50,568 (client:354) INFO: {'Role': 'Client #2', 'Round': 44, 'Results_raw': {'train_loss': 7.789081, 'val_loss': 7.844746, 'test_loss': 7.761865}}
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2024-11-17 17:38:19,753 (client:354) INFO: {'Role': 'Client #3', 'Round': 44, 'Results_raw': {'train_loss': 8.593101, 'val_loss': 8.525017, 'test_loss': 8.827788}}
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2024-11-17 17:38:19,787 (server:615) INFO: {'Role': 'Server #', 'Round': 43, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71183.454559), 'test_avg_loss': np.float64(12.639108), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73134.681187), 'val_avg_loss': np.float64(12.985561)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71183.454559), 'test_avg_loss': np.float64(12.639108), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73134.681187), 'val_avg_loss': np.float64(12.985561)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6930.194053), 'test_loss_bottom_decile': np.float64(64167.425903), 'test_loss_top_decile': np.float64(83729.173515), 'test_loss_min': np.float64(63086.915665), 'test_loss_max': np.float64(83729.173515), 'test_loss_bottom10%': np.float64(63086.915665), 'test_loss_top10%': np.float64(83729.173515), 'test_loss_cos1': np.float64(0.995294), 'test_loss_entropy': np.float64(2.297927), 'test_avg_loss_std': np.float64(1.230503), 'test_avg_loss_bottom_decile': np.float64(11.393364), 'test_avg_loss_top_decile': np.float64(14.866686), 'test_avg_loss_min': np.float64(11.201512), 'test_avg_loss_max': np.float64(14.866686), 'test_avg_loss_bottom10%': np.float64(11.201512), 'test_avg_loss_top10%': np.float64(14.866686), 'test_avg_loss_cos1': np.float64(0.995294), 'test_avg_loss_entropy': np.float64(2.297927), 'val_loss_std': np.float64(7587.160744), 'val_loss_bottom_decile': np.float64(65730.18943), 'val_loss_top_decile': np.float64(86946.113396), 'val_loss_min': np.float64(62969.514114), 'val_loss_max': np.float64(86946.113396), 'val_loss_bottom10%': np.float64(62969.514114), 'val_loss_top10%': np.float64(86946.113396), 'val_loss_cos1': np.float64(0.994662), 'val_loss_entropy': np.float64(2.297288), 'val_avg_loss_std': np.float64(1.347152), 'val_avg_loss_bottom_decile': np.float64(11.670843), 'val_avg_loss_top_decile': np.float64(15.437875), 'val_avg_loss_min': np.float64(11.180667), 'val_avg_loss_max': np.float64(15.437875), 'val_avg_loss_bottom10%': np.float64(11.180667), 'val_avg_loss_top10%': np.float64(15.437875), 'val_avg_loss_cos1': np.float64(0.994662), 'val_avg_loss_entropy': np.float64(2.297288)}}
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2024-11-17 17:38:20,025 (server:353) INFO: Server: Starting evaluation at the end of round 44.
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2024-11-17 17:38:20,027 (server:359) INFO: ----------- Starting a new training round (Round #45) -------------
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2024-11-17 17:53:42,644 (client:354) INFO: {'Role': 'Client #4', 'Round': 45, 'Results_raw': {'train_loss': 8.730861, 'val_loss': 8.977127, 'test_loss': 8.386888}}
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2024-11-17 18:00:46,918 (client:354) INFO: {'Role': 'Client #7', 'Round': 45, 'Results_raw': {'train_loss': 8.769188, 'val_loss': 8.277035, 'test_loss': 8.330629}}
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2024-11-17 18:07:27,411 (client:354) INFO: {'Role': 'Client #5', 'Round': 45, 'Results_raw': {'train_loss': 8.557823, 'val_loss': 8.56955, 'test_loss': 8.456393}}
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2024-11-17 18:14:24,391 (client:354) INFO: {'Role': 'Client #10', 'Round': 45, 'Results_raw': {'train_loss': 8.380606, 'val_loss': 8.277854, 'test_loss': 8.108078}}
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2024-11-17 18:21:31,709 (client:354) INFO: {'Role': 'Client #9', 'Round': 45, 'Results_raw': {'train_loss': 8.766824, 'val_loss': 8.905809, 'test_loss': 8.575878}}
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2024-11-17 18:28:14,572 (client:354) INFO: {'Role': 'Client #2', 'Round': 45, 'Results_raw': {'train_loss': 7.7828, 'val_loss': 7.86195, 'test_loss': 7.803368}}
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2024-11-17 18:35:37,584 (client:354) INFO: {'Role': 'Client #1', 'Round': 45, 'Results_raw': {'train_loss': 8.863934, 'val_loss': 8.723342, 'test_loss': 8.412966}}
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2024-11-17 18:42:06,065 (client:354) INFO: {'Role': 'Client #8', 'Round': 45, 'Results_raw': {'train_loss': 8.568629, 'val_loss': 8.803994, 'test_loss': 8.726948}}
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2024-11-17 18:49:01,864 (client:354) INFO: {'Role': 'Client #6', 'Round': 45, 'Results_raw': {'train_loss': 8.991815, 'val_loss': 9.024522, 'test_loss': 8.886632}}
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2024-11-17 18:55:55,146 (client:354) INFO: {'Role': 'Client #3', 'Round': 45, 'Results_raw': {'train_loss': 8.577197, 'val_loss': 8.397754, 'test_loss': 8.740981}}
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2024-11-17 18:55:55,153 (server:615) INFO: {'Role': 'Server #', 'Round': 44, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70626.77259), 'test_avg_loss': np.float64(12.540265), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72523.829923), 'val_avg_loss': np.float64(12.8771)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70626.77259), 'test_avg_loss': np.float64(12.540265), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72523.829923), 'val_avg_loss': np.float64(12.8771)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6847.125371), 'test_loss_bottom_decile': np.float64(63685.393417), 'test_loss_top_decile': np.float64(83361.575356), 'test_loss_min': np.float64(62872.846596), 'test_loss_max': np.float64(83361.575356), 'test_loss_bottom10%': np.float64(62872.846596), 'test_loss_top10%': np.float64(83361.575356), 'test_loss_cos1': np.float64(0.995333), 'test_loss_entropy': np.float64(2.297972), 'test_avg_loss_std': np.float64(1.215754), 'test_avg_loss_bottom_decile': np.float64(11.307776), 'test_avg_loss_top_decile': np.float64(14.801416), 'test_avg_loss_min': np.float64(11.163503), 'test_avg_loss_max': np.float64(14.801416), 'test_avg_loss_bottom10%': np.float64(11.163503), 'test_avg_loss_top10%': np.float64(14.801416), 'test_avg_loss_cos1': np.float64(0.995333), 'test_avg_loss_entropy': np.float64(2.297972), 'val_loss_std': np.float64(7480.582384), 'val_loss_bottom_decile': np.float64(65201.503288), 'val_loss_top_decile': np.float64(86551.558098), 'val_loss_min': np.float64(62747.59845), 'val_loss_max': np.float64(86551.558098), 'val_loss_bottom10%': np.float64(62747.59845), 'val_loss_top10%': np.float64(86551.558098), 'val_loss_cos1': np.float64(0.994722), 'val_loss_entropy': np.float64(2.297359), 'val_avg_loss_std': np.float64(1.328228), 'val_avg_loss_bottom_decile': np.float64(11.576971), 'val_avg_loss_top_decile': np.float64(15.367819), 'val_avg_loss_min': np.float64(11.141264), 'val_avg_loss_max': np.float64(15.367819), 'val_avg_loss_bottom10%': np.float64(11.141264), 'val_avg_loss_top10%': np.float64(15.367819), 'val_avg_loss_cos1': np.float64(0.994722), 'val_avg_loss_entropy': np.float64(2.297359)}}
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2024-11-17 18:55:55,243 (server:353) INFO: Server: Starting evaluation at the end of round 45.
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2024-11-17 18:55:55,244 (server:359) INFO: ----------- Starting a new training round (Round #46) -------------
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2024-11-17 19:12:32,225 (client:354) INFO: {'Role': 'Client #10', 'Round': 46, 'Results_raw': {'train_loss': 8.38731, 'val_loss': 8.185794, 'test_loss': 8.03076}}
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2024-11-17 19:19:14,823 (client:354) INFO: {'Role': 'Client #8', 'Round': 46, 'Results_raw': {'train_loss': 8.55598, 'val_loss': 8.708069, 'test_loss': 8.655071}}
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2024-11-17 19:26:10,110 (client:354) INFO: {'Role': 'Client #4', 'Round': 46, 'Results_raw': {'train_loss': 8.693521, 'val_loss': 8.911557, 'test_loss': 8.331437}}
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2024-11-17 19:33:14,306 (client:354) INFO: {'Role': 'Client #5', 'Round': 46, 'Results_raw': {'train_loss': 8.531035, 'val_loss': 8.526389, 'test_loss': 8.446489}}
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2024-11-17 19:40:48,807 (client:354) INFO: {'Role': 'Client #3', 'Round': 46, 'Results_raw': {'train_loss': 8.577281, 'val_loss': 8.359341, 'test_loss': 8.687162}}
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2024-11-17 19:47:55,602 (client:354) INFO: {'Role': 'Client #2', 'Round': 46, 'Results_raw': {'train_loss': 7.74673, 'val_loss': 7.72637, 'test_loss': 7.62729}}
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2024-11-17 19:54:49,249 (client:354) INFO: {'Role': 'Client #6', 'Round': 46, 'Results_raw': {'train_loss': 8.971464, 'val_loss': 8.979662, 'test_loss': 8.849855}}
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2024-11-17 20:01:32,567 (client:354) INFO: {'Role': 'Client #7', 'Round': 46, 'Results_raw': {'train_loss': 8.740696, 'val_loss': 8.361058, 'test_loss': 8.338205}}
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2024-11-17 20:07:38,326 (client:354) INFO: {'Role': 'Client #1', 'Round': 46, 'Results_raw': {'train_loss': 8.849701, 'val_loss': 8.745594, 'test_loss': 8.461398}}
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2024-11-17 20:13:54,723 (client:354) INFO: {'Role': 'Client #9', 'Round': 46, 'Results_raw': {'train_loss': 8.772922, 'val_loss': 8.826985, 'test_loss': 8.482616}}
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2024-11-17 20:13:54,732 (server:615) INFO: {'Role': 'Server #', 'Round': 45, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70375.119499), 'test_avg_loss': np.float64(12.495582), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72249.184018), 'val_avg_loss': np.float64(12.828335)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70375.119499), 'test_avg_loss': np.float64(12.495582), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72249.184018), 'val_avg_loss': np.float64(12.828335)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6993.071347), 'test_loss_bottom_decile': np.float64(63414.796463), 'test_loss_top_decile': np.float64(83503.78302), 'test_loss_min': np.float64(62445.718864), 'test_loss_max': np.float64(83503.78302), 'test_loss_bottom10%': np.float64(62445.718864), 'test_loss_top10%': np.float64(83503.78302), 'test_loss_cos1': np.float64(0.995099), 'test_loss_entropy': np.float64(2.297742), 'test_avg_loss_std': np.float64(1.241667), 'test_avg_loss_bottom_decile': np.float64(11.259729), 'test_avg_loss_top_decile': np.float64(14.826666), 'test_avg_loss_min': np.float64(11.087663), 'test_avg_loss_max': np.float64(14.826666), 'test_avg_loss_bottom10%': np.float64(11.087663), 'test_avg_loss_top10%': np.float64(14.826666), 'test_avg_loss_cos1': np.float64(0.995099), 'test_avg_loss_entropy': np.float64(2.297742), 'val_loss_std': np.float64(7607.023423), 'val_loss_bottom_decile': np.float64(64936.786575), 'val_loss_top_decile': np.float64(86568.898865), 'val_loss_min': np.float64(62298.433861), 'val_loss_max': np.float64(86568.898865), 'val_loss_bottom10%': np.float64(62298.433861), 'val_loss_top10%': np.float64(86568.898865), 'val_loss_cos1': np.float64(0.994503), 'val_loss_entropy': np.float64(2.297139), 'val_avg_loss_std': np.float64(1.350679), 'val_avg_loss_bottom_decile': np.float64(11.529969), 'val_avg_loss_top_decile': np.float64(15.370898), 'val_avg_loss_min': np.float64(11.061512), 'val_avg_loss_max': np.float64(15.370898), 'val_avg_loss_bottom10%': np.float64(11.061512), 'val_avg_loss_top10%': np.float64(15.370898), 'val_avg_loss_cos1': np.float64(0.994503), 'val_avg_loss_entropy': np.float64(2.297139)}}
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2024-11-17 20:13:54,827 (server:353) INFO: Server: Starting evaluation at the end of round 46.
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2024-11-17 20:13:54,829 (server:359) INFO: ----------- Starting a new training round (Round #47) -------------
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2024-11-17 20:30:21,866 (client:354) INFO: {'Role': 'Client #7', 'Round': 47, 'Results_raw': {'train_loss': 8.74793, 'val_loss': 8.315456, 'test_loss': 8.33632}}
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2024-11-17 20:36:57,267 (client:354) INFO: {'Role': 'Client #5', 'Round': 47, 'Results_raw': {'train_loss': 8.536018, 'val_loss': 8.501664, 'test_loss': 8.412134}}
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2024-11-17 20:44:04,770 (client:354) INFO: {'Role': 'Client #3', 'Round': 47, 'Results_raw': {'train_loss': 8.572852, 'val_loss': 8.45887, 'test_loss': 8.801852}}
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2024-11-17 20:50:47,022 (client:354) INFO: {'Role': 'Client #8', 'Round': 47, 'Results_raw': {'train_loss': 8.53872, 'val_loss': 8.623577, 'test_loss': 8.556541}}
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2024-11-17 20:57:39,786 (client:354) INFO: {'Role': 'Client #2', 'Round': 47, 'Results_raw': {'train_loss': 7.746571, 'val_loss': 7.659407, 'test_loss': 7.618974}}
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2024-11-17 21:04:00,731 (client:354) INFO: {'Role': 'Client #4', 'Round': 47, 'Results_raw': {'train_loss': 8.675079, 'val_loss': 8.988107, 'test_loss': 8.422093}}
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2024-11-17 21:11:07,122 (client:354) INFO: {'Role': 'Client #1', 'Round': 47, 'Results_raw': {'train_loss': 8.83172, 'val_loss': 8.65489, 'test_loss': 8.384681}}
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2024-11-17 21:17:58,513 (client:354) INFO: {'Role': 'Client #10', 'Round': 47, 'Results_raw': {'train_loss': 8.360603, 'val_loss': 8.225646, 'test_loss': 8.052155}}
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2024-11-17 21:24:31,095 (client:354) INFO: {'Role': 'Client #9', 'Round': 47, 'Results_raw': {'train_loss': 8.744081, 'val_loss': 8.829055, 'test_loss': 8.496068}}
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2024-11-17 21:31:23,201 (client:354) INFO: {'Role': 'Client #6', 'Round': 47, 'Results_raw': {'train_loss': 8.979191, 'val_loss': 8.918002, 'test_loss': 8.820635}}
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2024-11-17 21:31:23,230 (server:615) INFO: {'Role': 'Server #', 'Round': 46, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70071.380286), 'test_avg_loss': np.float64(12.441651), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71943.258096), 'val_avg_loss': np.float64(12.774016)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70071.380286), 'test_avg_loss': np.float64(12.441651), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71943.258096), 'val_avg_loss': np.float64(12.774016)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6647.359339), 'test_loss_bottom_decile': np.float64(63479.353958), 'test_loss_top_decile': np.float64(82364.463913), 'test_loss_min': np.float64(62298.041733), 'test_loss_max': np.float64(82364.463913), 'test_loss_bottom10%': np.float64(62298.041733), 'test_loss_top10%': np.float64(82364.463913), 'test_loss_cos1': np.float64(0.99553), 'test_loss_entropy': np.float64(2.298163), 'test_avg_loss_std': np.float64(1.180284), 'test_avg_loss_bottom_decile': np.float64(11.271192), 'test_avg_loss_top_decile': np.float64(14.624372), 'test_avg_loss_min': np.float64(11.061442), 'test_avg_loss_max': np.float64(14.624372), 'test_avg_loss_bottom10%': np.float64(11.061442), 'test_avg_loss_top10%': np.float64(14.624372), 'test_avg_loss_cos1': np.float64(0.99553), 'test_avg_loss_entropy': np.float64(2.298163), 'val_loss_std': np.float64(7238.964024), 'val_loss_bottom_decile': np.float64(64969.954308), 'val_loss_top_decile': np.float64(85399.419632), 'val_loss_min': np.float64(62162.232262), 'val_loss_max': np.float64(85399.419632), 'val_loss_bottom10%': np.float64(62162.232262), 'val_loss_top10%': np.float64(85399.419632), 'val_loss_cos1': np.float64(0.994976), 'val_loss_entropy': np.float64(2.297603), 'val_avg_loss_std': np.float64(1.285327), 'val_avg_loss_bottom_decile': np.float64(11.535858), 'val_avg_loss_top_decile': np.float64(15.163249), 'val_avg_loss_min': np.float64(11.037328), 'val_avg_loss_max': np.float64(15.163249), 'val_avg_loss_bottom10%': np.float64(11.037328), 'val_avg_loss_top10%': np.float64(15.163249), 'val_avg_loss_cos1': np.float64(0.994976), 'val_avg_loss_entropy': np.float64(2.297603)}}
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2024-11-17 21:31:23,347 (server:353) INFO: Server: Starting evaluation at the end of round 47.
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2024-11-17 21:31:23,348 (server:359) INFO: ----------- Starting a new training round (Round #48) -------------
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2024-11-17 21:47:30,304 (client:354) INFO: {'Role': 'Client #7', 'Round': 48, 'Results_raw': {'train_loss': 8.7049, 'val_loss': 8.344753, 'test_loss': 8.381578}}
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2024-11-17 21:54:36,198 (client:354) INFO: {'Role': 'Client #4', 'Round': 48, 'Results_raw': {'train_loss': 8.659048, 'val_loss': 8.924915, 'test_loss': 8.359998}}
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2024-11-17 22:01:51,382 (client:354) INFO: {'Role': 'Client #8', 'Round': 48, 'Results_raw': {'train_loss': 8.531142, 'val_loss': 8.680542, 'test_loss': 8.625453}}
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2024-11-17 22:09:09,469 (client:354) INFO: {'Role': 'Client #5', 'Round': 48, 'Results_raw': {'train_loss': 8.515235, 'val_loss': 8.551393, 'test_loss': 8.44251}}
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2024-11-17 22:16:31,020 (client:354) INFO: {'Role': 'Client #6', 'Round': 48, 'Results_raw': {'train_loss': 8.97016, 'val_loss': 8.879475, 'test_loss': 8.725452}}
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2024-11-17 22:25:21,163 (client:354) INFO: {'Role': 'Client #9', 'Round': 48, 'Results_raw': {'train_loss': 8.737877, 'val_loss': 8.970916, 'test_loss': 8.66718}}
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2024-11-17 22:35:07,049 (client:354) INFO: {'Role': 'Client #2', 'Round': 48, 'Results_raw': {'train_loss': 7.74302, 'val_loss': 7.613028, 'test_loss': 7.556886}}
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2024-11-17 22:44:31,630 (client:354) INFO: {'Role': 'Client #10', 'Round': 48, 'Results_raw': {'train_loss': 8.366375, 'val_loss': 8.280179, 'test_loss': 8.134172}}
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2024-11-17 22:51:22,732 (client:354) INFO: {'Role': 'Client #3', 'Round': 48, 'Results_raw': {'train_loss': 8.546574, 'val_loss': 8.464233, 'test_loss': 8.82113}}
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2024-11-17 22:58:50,771 (client:354) INFO: {'Role': 'Client #1', 'Round': 48, 'Results_raw': {'train_loss': 8.819507, 'val_loss': 8.760283, 'test_loss': 8.494045}}
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2024-11-17 22:58:50,787 (server:615) INFO: {'Role': 'Server #', 'Round': 47, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71118.561788), 'test_avg_loss': np.float64(12.627586), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73120.60243), 'val_avg_loss': np.float64(12.983062)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(71118.561788), 'test_avg_loss': np.float64(12.627586), 'val_total': np.float64(5632.0), 'val_loss': np.float64(73120.60243), 'val_avg_loss': np.float64(12.983062)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7139.735016), 'test_loss_bottom_decile': np.float64(63953.557716), 'test_loss_top_decile': np.float64(84795.893669), 'test_loss_min': np.float64(62966.599747), 'test_loss_max': np.float64(84795.893669), 'test_loss_bottom10%': np.float64(62966.599747), 'test_loss_top10%': np.float64(84795.893669), 'test_loss_cos1': np.float64(0.994998), 'test_loss_entropy': np.float64(2.297646), 'test_avg_loss_std': np.float64(1.267709), 'test_avg_loss_bottom_decile': np.float64(11.35539), 'test_avg_loss_top_decile': np.float64(15.056089), 'test_avg_loss_min': np.float64(11.180149), 'test_avg_loss_max': np.float64(15.056089), 'test_avg_loss_bottom10%': np.float64(11.180149), 'test_avg_loss_top10%': np.float64(15.056089), 'test_avg_loss_cos1': np.float64(0.994998), 'test_avg_loss_entropy': np.float64(2.297646), 'val_loss_std': np.float64(7792.403364), 'val_loss_bottom_decile': np.float64(65539.415726), 'val_loss_top_decile': np.float64(88157.484543), 'val_loss_min': np.float64(62908.93261), 'val_loss_max': np.float64(88157.484543), 'val_loss_bottom10%': np.float64(62908.93261), 'val_loss_top10%': np.float64(88157.484543), 'val_loss_cos1': np.float64(0.994369), 'val_loss_entropy': np.float64(2.297013), 'val_avg_loss_std': np.float64(1.383594), 'val_avg_loss_bottom_decile': np.float64(11.63697), 'val_avg_loss_top_decile': np.float64(15.652962), 'val_avg_loss_min': np.float64(11.16991), 'val_avg_loss_max': np.float64(15.652962), 'val_avg_loss_bottom10%': np.float64(11.16991), 'val_avg_loss_top10%': np.float64(15.652962), 'val_avg_loss_cos1': np.float64(0.994369), 'val_avg_loss_entropy': np.float64(2.297013)}}
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2024-11-17 22:58:50,939 (server:353) INFO: Server: Starting evaluation at the end of round 48.
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2024-11-17 22:58:50,940 (server:359) INFO: ----------- Starting a new training round (Round #49) -------------
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2024-11-17 23:14:57,745 (client:354) INFO: {'Role': 'Client #10', 'Round': 49, 'Results_raw': {'train_loss': 8.33476, 'val_loss': 8.212843, 'test_loss': 8.091196}}
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2024-11-17 23:22:48,280 (client:354) INFO: {'Role': 'Client #3', 'Round': 49, 'Results_raw': {'train_loss': 8.499997, 'val_loss': 8.291669, 'test_loss': 8.63489}}
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2024-11-17 23:29:51,201 (client:354) INFO: {'Role': 'Client #2', 'Round': 49, 'Results_raw': {'train_loss': 7.744826, 'val_loss': 7.723406, 'test_loss': 7.629713}}
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2024-11-17 23:36:53,405 (client:354) INFO: {'Role': 'Client #6', 'Round': 49, 'Results_raw': {'train_loss': 8.94565, 'val_loss': 8.877181, 'test_loss': 8.763071}}
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2024-11-17 23:43:22,721 (client:354) INFO: {'Role': 'Client #7', 'Round': 49, 'Results_raw': {'train_loss': 8.750866, 'val_loss': 8.404775, 'test_loss': 8.422071}}
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2024-11-17 23:50:27,120 (client:354) INFO: {'Role': 'Client #4', 'Round': 49, 'Results_raw': {'train_loss': 8.670759, 'val_loss': 8.921825, 'test_loss': 8.343766}}
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2024-11-17 23:57:32,803 (client:354) INFO: {'Role': 'Client #5', 'Round': 49, 'Results_raw': {'train_loss': 8.49386, 'val_loss': 8.469991, 'test_loss': 8.373528}}
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2024-11-18 00:04:58,596 (client:354) INFO: {'Role': 'Client #9', 'Round': 49, 'Results_raw': {'train_loss': 8.732754, 'val_loss': 8.8099, 'test_loss': 8.453015}}
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2024-11-18 00:12:04,339 (client:354) INFO: {'Role': 'Client #8', 'Round': 49, 'Results_raw': {'train_loss': 8.533233, 'val_loss': 8.699921, 'test_loss': 8.675178}}
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2024-11-18 00:19:07,886 (client:354) INFO: {'Role': 'Client #1', 'Round': 49, 'Results_raw': {'train_loss': 8.81434, 'val_loss': 8.685783, 'test_loss': 8.435006}}
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2024-11-18 00:19:07,904 (server:615) INFO: {'Role': 'Server #', 'Round': 48, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69354.080281), 'test_avg_loss': np.float64(12.31429), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71167.117729), 'val_avg_loss': np.float64(12.636207)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69354.080281), 'test_avg_loss': np.float64(12.31429), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71167.117729), 'val_avg_loss': np.float64(12.636207)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6405.309285), 'test_loss_bottom_decile': np.float64(63038.927261), 'test_loss_top_decile': np.float64(81742.425156), 'test_loss_min': np.float64(61950.600159), 'test_loss_max': np.float64(81742.425156), 'test_loss_bottom10%': np.float64(61950.600159), 'test_loss_top10%': np.float64(81742.425156), 'test_loss_cos1': np.float64(0.995762), 'test_loss_entropy': np.float64(2.298398), 'test_avg_loss_std': np.float64(1.137306), 'test_avg_loss_bottom_decile': np.float64(11.192991), 'test_avg_loss_top_decile': np.float64(14.513925), 'test_avg_loss_min': np.float64(10.999751), 'test_avg_loss_max': np.float64(14.513925), 'test_avg_loss_bottom10%': np.float64(10.999751), 'test_avg_loss_top10%': np.float64(14.513925), 'test_avg_loss_cos1': np.float64(0.995762), 'test_avg_loss_entropy': np.float64(2.298398), 'val_loss_std': np.float64(7011.143991), 'val_loss_bottom_decile': np.float64(64463.703354), 'val_loss_top_decile': np.float64(84767.135376), 'val_loss_min': np.float64(61814.903542), 'val_loss_max': np.float64(84767.135376), 'val_loss_bottom10%': np.float64(61814.903542), 'val_loss_top10%': np.float64(84767.135376), 'val_loss_cos1': np.float64(0.995182), 'val_loss_entropy': np.float64(2.297815), 'val_avg_loss_std': np.float64(1.244876), 'val_avg_loss_bottom_decile': np.float64(11.44597), 'val_avg_loss_top_decile': np.float64(15.050983), 'val_avg_loss_min': np.float64(10.975658), 'val_avg_loss_max': np.float64(15.050983), 'val_avg_loss_bottom10%': np.float64(10.975658), 'val_avg_loss_top10%': np.float64(15.050983), 'val_avg_loss_cos1': np.float64(0.995182), 'val_avg_loss_entropy': np.float64(2.297815)}}
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2024-11-18 00:19:08,019 (server:353) INFO: Server: Starting evaluation at the end of round 49.
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2024-11-18 00:19:08,021 (server:359) INFO: ----------- Starting a new training round (Round #50) -------------
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2024-11-18 00:35:49,720 (client:354) INFO: {'Role': 'Client #8', 'Round': 50, 'Results_raw': {'train_loss': 8.506036, 'val_loss': 8.69698, 'test_loss': 8.654647}}
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2024-11-18 00:43:07,917 (client:354) INFO: {'Role': 'Client #1', 'Round': 50, 'Results_raw': {'train_loss': 8.814127, 'val_loss': 8.69095, 'test_loss': 8.419623}}
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2024-11-18 00:49:57,087 (client:354) INFO: {'Role': 'Client #9', 'Round': 50, 'Results_raw': {'train_loss': 8.716339, 'val_loss': 8.913929, 'test_loss': 8.565186}}
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2024-11-18 00:57:15,047 (client:354) INFO: {'Role': 'Client #10', 'Round': 50, 'Results_raw': {'train_loss': 8.330756, 'val_loss': 8.180022, 'test_loss': 8.002452}}
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2024-11-18 01:04:30,550 (client:354) INFO: {'Role': 'Client #3', 'Round': 50, 'Results_raw': {'train_loss': 8.512984, 'val_loss': 8.691584, 'test_loss': 8.968699}}
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2024-11-18 01:11:18,389 (client:354) INFO: {'Role': 'Client #5', 'Round': 50, 'Results_raw': {'train_loss': 8.505295, 'val_loss': 8.699339, 'test_loss': 8.609549}}
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2024-11-18 01:17:57,900 (client:354) INFO: {'Role': 'Client #7', 'Round': 50, 'Results_raw': {'train_loss': 8.683823, 'val_loss': 8.426591, 'test_loss': 8.460862}}
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2024-11-18 01:25:04,697 (client:354) INFO: {'Role': 'Client #2', 'Round': 50, 'Results_raw': {'train_loss': 7.711898, 'val_loss': 7.63148, 'test_loss': 7.564208}}
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2024-11-18 01:32:06,784 (client:354) INFO: {'Role': 'Client #6', 'Round': 50, 'Results_raw': {'train_loss': 8.930875, 'val_loss': 9.101618, 'test_loss': 8.937728}}
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2024-11-18 01:39:14,397 (client:354) INFO: {'Role': 'Client #4', 'Round': 50, 'Results_raw': {'train_loss': 8.653331, 'val_loss': 8.873691, 'test_loss': 8.308095}}
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2024-11-18 01:39:14,408 (server:615) INFO: {'Role': 'Server #', 'Round': 49, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70571.474793), 'test_avg_loss': np.float64(12.530447), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72500.571741), 'val_avg_loss': np.float64(12.872971)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70571.474793), 'test_avg_loss': np.float64(12.530447), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72500.571741), 'val_avg_loss': np.float64(12.872971)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7019.763812), 'test_loss_bottom_decile': np.float64(63693.187454), 'test_loss_top_decile': np.float64(84355.192459), 'test_loss_min': np.float64(62641.812546), 'test_loss_max': np.float64(84355.192459), 'test_loss_bottom10%': np.float64(62641.812546), 'test_loss_top10%': np.float64(84355.192459), 'test_loss_cos1': np.float64(0.995089), 'test_loss_entropy': np.float64(2.29774), 'test_avg_loss_std': np.float64(1.246407), 'test_avg_loss_bottom_decile': np.float64(11.30916), 'test_avg_loss_top_decile': np.float64(14.97784), 'test_avg_loss_min': np.float64(11.122481), 'test_avg_loss_max': np.float64(14.97784), 'test_avg_loss_bottom10%': np.float64(11.122481), 'test_avg_loss_top10%': np.float64(14.97784), 'test_avg_loss_cos1': np.float64(0.995089), 'test_avg_loss_entropy': np.float64(2.29774), 'val_loss_std': np.float64(7682.292643), 'val_loss_bottom_decile': np.float64(65167.028763), 'val_loss_top_decile': np.float64(87723.991859), 'val_loss_min': np.float64(62520.280067), 'val_loss_max': np.float64(87723.991859), 'val_loss_bottom10%': np.float64(62520.280067), 'val_loss_top10%': np.float64(87723.991859), 'val_loss_cos1': np.float64(0.994433), 'val_loss_entropy': np.float64(2.297081), 'val_avg_loss_std': np.float64(1.364043), 'val_avg_loss_bottom_decile': np.float64(11.57085), 'val_avg_loss_top_decile': np.float64(15.575993), 'val_avg_loss_min': np.float64(11.100902), 'val_avg_loss_max': np.float64(15.575993), 'val_avg_loss_bottom10%': np.float64(11.100902), 'val_avg_loss_top10%': np.float64(15.575993), 'val_avg_loss_cos1': np.float64(0.994433), 'val_avg_loss_entropy': np.float64(2.297081)}}
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2024-11-18 01:39:14,461 (server:353) INFO: Server: Starting evaluation at the end of round 50.
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2024-11-18 01:39:14,462 (server:359) INFO: ----------- Starting a new training round (Round #51) -------------
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2024-11-18 01:56:13,364 (client:354) INFO: {'Role': 'Client #8', 'Round': 51, 'Results_raw': {'train_loss': 8.499781, 'val_loss': 8.658025, 'test_loss': 8.648437}}
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2024-11-18 02:03:14,423 (client:354) INFO: {'Role': 'Client #1', 'Round': 51, 'Results_raw': {'train_loss': 8.803126, 'val_loss': 8.812698, 'test_loss': 8.516048}}
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2024-11-18 02:09:55,247 (client:354) INFO: {'Role': 'Client #4', 'Round': 51, 'Results_raw': {'train_loss': 8.639126, 'val_loss': 9.06299, 'test_loss': 8.462686}}
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2024-11-18 02:16:42,649 (client:354) INFO: {'Role': 'Client #10', 'Round': 51, 'Results_raw': {'train_loss': 8.324936, 'val_loss': 8.390282, 'test_loss': 8.161257}}
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2024-11-18 02:23:18,825 (client:354) INFO: {'Role': 'Client #3', 'Round': 51, 'Results_raw': {'train_loss': 8.502138, 'val_loss': 8.304692, 'test_loss': 8.641719}}
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2024-11-18 02:30:32,316 (client:354) INFO: {'Role': 'Client #2', 'Round': 51, 'Results_raw': {'train_loss': 7.706526, 'val_loss': 7.612682, 'test_loss': 7.543054}}
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2024-11-18 02:37:22,369 (client:354) INFO: {'Role': 'Client #7', 'Round': 51, 'Results_raw': {'train_loss': 8.69977, 'val_loss': 8.377291, 'test_loss': 8.397352}}
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2024-11-18 02:44:26,480 (client:354) INFO: {'Role': 'Client #5', 'Round': 51, 'Results_raw': {'train_loss': 8.477787, 'val_loss': 8.504549, 'test_loss': 8.393674}}
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2024-11-18 02:51:35,306 (client:354) INFO: {'Role': 'Client #9', 'Round': 51, 'Results_raw': {'train_loss': 8.694384, 'val_loss': 8.800691, 'test_loss': 8.476785}}
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2024-11-18 02:57:41,768 (client:354) INFO: {'Role': 'Client #6', 'Round': 51, 'Results_raw': {'train_loss': 8.957931, 'val_loss': 8.884559, 'test_loss': 8.757815}}
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2024-11-18 02:57:41,773 (server:615) INFO: {'Role': 'Server #', 'Round': 50, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70053.204861), 'test_avg_loss': np.float64(12.438424), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71982.384337), 'val_avg_loss': np.float64(12.780963)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70053.204861), 'test_avg_loss': np.float64(12.438424), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71982.384337), 'val_avg_loss': np.float64(12.780963)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6852.279217), 'test_loss_bottom_decile': np.float64(63301.473907), 'test_loss_top_decile': np.float64(83307.151253), 'test_loss_min': np.float64(62148.683311), 'test_loss_max': np.float64(83307.151253), 'test_loss_bottom10%': np.float64(62148.683311), 'test_loss_top10%': np.float64(83307.151253), 'test_loss_cos1': np.float64(0.99525), 'test_loss_entropy': np.float64(2.297895), 'test_avg_loss_std': np.float64(1.216669), 'test_avg_loss_bottom_decile': np.float64(11.239608), 'test_avg_loss_top_decile': np.float64(14.791753), 'test_avg_loss_min': np.float64(11.034922), 'test_avg_loss_max': np.float64(14.791753), 'test_avg_loss_bottom10%': np.float64(11.034922), 'test_avg_loss_top10%': np.float64(14.791753), 'test_avg_loss_cos1': np.float64(0.99525), 'test_avg_loss_entropy': np.float64(2.297895), 'val_loss_std': np.float64(7495.598958), 'val_loss_bottom_decile': np.float64(64820.570755), 'val_loss_top_decile': np.float64(86622.917564), 'val_loss_min': np.float64(62071.811935), 'val_loss_max': np.float64(86622.917564), 'val_loss_bottom10%': np.float64(62071.811935), 'val_loss_top10%': np.float64(86622.917564), 'val_loss_cos1': np.float64(0.994622), 'val_loss_entropy': np.float64(2.297265), 'val_avg_loss_std': np.float64(1.330895), 'val_avg_loss_bottom_decile': np.float64(11.509334), 'val_avg_loss_top_decile': np.float64(15.38049), 'val_avg_loss_min': np.float64(11.021273), 'val_avg_loss_max': np.float64(15.38049), 'val_avg_loss_bottom10%': np.float64(11.021273), 'val_avg_loss_top10%': np.float64(15.38049), 'val_avg_loss_cos1': np.float64(0.994622), 'val_avg_loss_entropy': np.float64(2.297265)}}
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2024-11-18 02:57:41,822 (server:353) INFO: Server: Starting evaluation at the end of round 51.
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2024-11-18 02:57:41,823 (server:359) INFO: ----------- Starting a new training round (Round #52) -------------
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2024-11-18 03:14:14,519 (client:354) INFO: {'Role': 'Client #10', 'Round': 52, 'Results_raw': {'train_loss': 8.30287, 'val_loss': 8.214514, 'test_loss': 8.071561}}
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2024-11-18 03:20:30,224 (client:354) INFO: {'Role': 'Client #1', 'Round': 52, 'Results_raw': {'train_loss': 8.790885, 'val_loss': 8.713514, 'test_loss': 8.415566}}
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2024-11-18 03:27:39,276 (client:354) INFO: {'Role': 'Client #5', 'Round': 52, 'Results_raw': {'train_loss': 8.474565, 'val_loss': 8.556668, 'test_loss': 8.443252}}
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2024-11-18 03:34:53,807 (client:354) INFO: {'Role': 'Client #4', 'Round': 52, 'Results_raw': {'train_loss': 8.624085, 'val_loss': 9.047944, 'test_loss': 8.452948}}
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2024-11-18 03:41:24,406 (client:354) INFO: {'Role': 'Client #7', 'Round': 52, 'Results_raw': {'train_loss': 8.674274, 'val_loss': 8.222192, 'test_loss': 8.239398}}
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2024-11-18 03:48:10,570 (client:354) INFO: {'Role': 'Client #9', 'Round': 52, 'Results_raw': {'train_loss': 8.694955, 'val_loss': 8.783245, 'test_loss': 8.457866}}
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2024-11-18 03:55:16,019 (client:354) INFO: {'Role': 'Client #3', 'Round': 52, 'Results_raw': {'train_loss': 8.542322, 'val_loss': 8.295168, 'test_loss': 8.644893}}
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2024-11-18 04:02:41,032 (client:354) INFO: {'Role': 'Client #6', 'Round': 52, 'Results_raw': {'train_loss': 8.920187, 'val_loss': 8.880764, 'test_loss': 8.775956}}
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2024-11-18 04:09:12,345 (client:354) INFO: {'Role': 'Client #2', 'Round': 52, 'Results_raw': {'train_loss': 7.707279, 'val_loss': 7.532401, 'test_loss': 7.45996}}
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2024-11-18 04:15:40,578 (client:354) INFO: {'Role': 'Client #8', 'Round': 52, 'Results_raw': {'train_loss': 8.491766, 'val_loss': 8.614435, 'test_loss': 8.577948}}
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2024-11-18 04:15:40,619 (server:615) INFO: {'Role': 'Server #', 'Round': 51, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70571.96241), 'test_avg_loss': np.float64(12.530533), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72573.056911), 'val_avg_loss': np.float64(12.885841)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70571.96241), 'test_avg_loss': np.float64(12.530533), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72573.056911), 'val_avg_loss': np.float64(12.885841)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(7080.899202), 'test_loss_bottom_decile': np.float64(63510.397873), 'test_loss_top_decile': np.float64(84421.129692), 'test_loss_min': np.float64(62543.623466), 'test_loss_max': np.float64(84421.129692), 'test_loss_bottom10%': np.float64(62543.623466), 'test_loss_top10%': np.float64(84421.129692), 'test_loss_cos1': np.float64(0.995004), 'test_loss_entropy': np.float64(2.297656), 'test_avg_loss_std': np.float64(1.257262), 'test_avg_loss_bottom_decile': np.float64(11.276704), 'test_avg_loss_top_decile': np.float64(14.989547), 'test_avg_loss_min': np.float64(11.105047), 'test_avg_loss_max': np.float64(14.989547), 'test_avg_loss_bottom10%': np.float64(11.105047), 'test_avg_loss_top10%': np.float64(14.989547), 'test_avg_loss_cos1': np.float64(0.995004), 'test_avg_loss_entropy': np.float64(2.297656), 'val_loss_std': np.float64(7753.756202), 'val_loss_bottom_decile': np.float64(65094.683495), 'val_loss_top_decile': np.float64(87860.368759), 'val_loss_min': np.float64(62452.20829), 'val_loss_max': np.float64(87860.368759), 'val_loss_bottom10%': np.float64(62452.20829), 'val_loss_top10%': np.float64(87860.368759), 'val_loss_cos1': np.float64(0.994341), 'val_loss_entropy': np.float64(2.296992), 'val_avg_loss_std': np.float64(1.376732), 'val_avg_loss_bottom_decile': np.float64(11.558005), 'val_avg_loss_top_decile': np.float64(15.600208), 'val_avg_loss_min': np.float64(11.088815), 'val_avg_loss_max': np.float64(15.600208), 'val_avg_loss_bottom10%': np.float64(11.088815), 'val_avg_loss_top10%': np.float64(15.600208), 'val_avg_loss_cos1': np.float64(0.994341), 'val_avg_loss_entropy': np.float64(2.296992)}}
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2024-11-18 04:15:40,797 (server:353) INFO: Server: Starting evaluation at the end of round 52.
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2024-11-18 04:15:40,799 (server:359) INFO: ----------- Starting a new training round (Round #53) -------------
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2024-11-18 04:31:24,192 (client:354) INFO: {'Role': 'Client #9', 'Round': 53, 'Results_raw': {'train_loss': 8.701419, 'val_loss': 8.93707, 'test_loss': 8.629209}}
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2024-11-18 04:37:58,072 (client:354) INFO: {'Role': 'Client #1', 'Round': 53, 'Results_raw': {'train_loss': 8.773228, 'val_loss': 8.852506, 'test_loss': 8.54282}}
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2024-11-18 04:44:14,097 (client:354) INFO: {'Role': 'Client #2', 'Round': 53, 'Results_raw': {'train_loss': 7.678062, 'val_loss': 7.591753, 'test_loss': 7.534536}}
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2024-11-18 04:51:44,116 (client:354) INFO: {'Role': 'Client #6', 'Round': 53, 'Results_raw': {'train_loss': 8.908326, 'val_loss': 8.860949, 'test_loss': 8.751073}}
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2024-11-18 04:58:31,541 (client:354) INFO: {'Role': 'Client #3', 'Round': 53, 'Results_raw': {'train_loss': 8.467876, 'val_loss': 8.503918, 'test_loss': 8.794594}}
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2024-11-18 05:04:46,267 (client:354) INFO: {'Role': 'Client #4', 'Round': 53, 'Results_raw': {'train_loss': 8.632948, 'val_loss': 8.957811, 'test_loss': 8.393636}}
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2024-11-18 05:11:10,865 (client:354) INFO: {'Role': 'Client #10', 'Round': 53, 'Results_raw': {'train_loss': 8.288218, 'val_loss': 8.234594, 'test_loss': 8.095706}}
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2024-11-18 05:18:02,482 (client:354) INFO: {'Role': 'Client #8', 'Round': 53, 'Results_raw': {'train_loss': 8.494095, 'val_loss': 8.656942, 'test_loss': 8.606683}}
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2024-11-18 05:24:41,787 (client:354) INFO: {'Role': 'Client #7', 'Round': 53, 'Results_raw': {'train_loss': 8.65771, 'val_loss': 8.336889, 'test_loss': 8.345187}}
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2024-11-18 05:30:56,946 (client:354) INFO: {'Role': 'Client #5', 'Round': 53, 'Results_raw': {'train_loss': 8.445718, 'val_loss': 8.471143, 'test_loss': 8.342022}}
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2024-11-18 05:30:56,960 (server:615) INFO: {'Role': 'Server #', 'Round': 52, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69913.743211), 'test_avg_loss': np.float64(12.413662), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71807.938364), 'val_avg_loss': np.float64(12.749989)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69913.743211), 'test_avg_loss': np.float64(12.413662), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71807.938364), 'val_avg_loss': np.float64(12.749989)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6770.037424), 'test_loss_bottom_decile': np.float64(63146.584785), 'test_loss_top_decile': np.float64(82851.343086), 'test_loss_min': np.float64(62368.254059), 'test_loss_max': np.float64(82851.343086), 'test_loss_bottom10%': np.float64(62368.254059), 'test_loss_top10%': np.float64(82851.343086), 'test_loss_cos1': np.float64(0.995344), 'test_loss_entropy': np.float64(2.297988), 'test_avg_loss_std': np.float64(1.202066), 'test_avg_loss_bottom_decile': np.float64(11.212107), 'test_avg_loss_top_decile': np.float64(14.710821), 'test_avg_loss_min': np.float64(11.073909), 'test_avg_loss_max': np.float64(14.710821), 'test_avg_loss_bottom10%': np.float64(11.073909), 'test_avg_loss_top10%': np.float64(14.710821), 'test_avg_loss_cos1': np.float64(0.995344), 'test_avg_loss_entropy': np.float64(2.297988), 'val_loss_std': np.float64(7429.100297), 'val_loss_bottom_decile': np.float64(64650.165878), 'val_loss_top_decile': np.float64(86117.598473), 'val_loss_min': np.float64(62221.554108), 'val_loss_max': np.float64(86117.598473), 'val_loss_bottom10%': np.float64(62221.554108), 'val_loss_top10%': np.float64(86117.598473), 'val_loss_cos1': np.float64(0.994691), 'val_loss_entropy': np.float64(2.297333), 'val_avg_loss_std': np.float64(1.319087), 'val_avg_loss_bottom_decile': np.float64(11.479078), 'val_avg_loss_top_decile': np.float64(15.290767), 'val_avg_loss_min': np.float64(11.047861), 'val_avg_loss_max': np.float64(15.290767), 'val_avg_loss_bottom10%': np.float64(11.047861), 'val_avg_loss_top10%': np.float64(15.290767), 'val_avg_loss_cos1': np.float64(0.994691), 'val_avg_loss_entropy': np.float64(2.297333)}}
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2024-11-18 05:30:57,010 (server:353) INFO: Server: Starting evaluation at the end of round 53.
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2024-11-18 05:30:57,011 (server:359) INFO: ----------- Starting a new training round (Round #54) -------------
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2024-11-18 05:47:01,479 (client:354) INFO: {'Role': 'Client #5', 'Round': 54, 'Results_raw': {'train_loss': 8.457724, 'val_loss': 8.503627, 'test_loss': 8.399998}}
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2024-11-18 05:54:01,862 (client:354) INFO: {'Role': 'Client #4', 'Round': 54, 'Results_raw': {'train_loss': 8.598365, 'val_loss': 8.833397, 'test_loss': 8.278455}}
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2024-11-18 06:00:32,978 (client:354) INFO: {'Role': 'Client #2', 'Round': 54, 'Results_raw': {'train_loss': 7.663365, 'val_loss': 7.698164, 'test_loss': 7.612442}}
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2024-11-18 06:07:18,290 (client:354) INFO: {'Role': 'Client #9', 'Round': 54, 'Results_raw': {'train_loss': 8.672818, 'val_loss': 8.804018, 'test_loss': 8.508825}}
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2024-11-18 06:13:54,550 (client:354) INFO: {'Role': 'Client #7', 'Round': 54, 'Results_raw': {'train_loss': 8.668652, 'val_loss': 8.197486, 'test_loss': 8.211286}}
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2024-11-18 06:20:35,805 (client:354) INFO: {'Role': 'Client #10', 'Round': 54, 'Results_raw': {'train_loss': 8.291919, 'val_loss': 8.440486, 'test_loss': 8.273873}}
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2024-11-18 06:27:23,604 (client:354) INFO: {'Role': 'Client #3', 'Round': 54, 'Results_raw': {'train_loss': 8.487591, 'val_loss': 8.342803, 'test_loss': 8.709842}}
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2024-11-18 06:34:39,230 (client:354) INFO: {'Role': 'Client #8', 'Round': 54, 'Results_raw': {'train_loss': 8.487536, 'val_loss': 8.6271, 'test_loss': 8.578653}}
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2024-11-18 06:41:32,860 (client:354) INFO: {'Role': 'Client #1', 'Round': 54, 'Results_raw': {'train_loss': 8.773732, 'val_loss': 8.650198, 'test_loss': 8.361548}}
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2024-11-18 06:48:16,162 (client:354) INFO: {'Role': 'Client #6', 'Round': 54, 'Results_raw': {'train_loss': 8.899573, 'val_loss': 9.030697, 'test_loss': 8.906428}}
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2024-11-18 06:48:16,166 (server:615) INFO: {'Role': 'Server #', 'Round': 53, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70215.022107), 'test_avg_loss': np.float64(12.467156), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72123.271774), 'val_avg_loss': np.float64(12.805979)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(70215.022107), 'test_avg_loss': np.float64(12.467156), 'val_total': np.float64(5632.0), 'val_loss': np.float64(72123.271774), 'val_avg_loss': np.float64(12.805979)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6872.955805), 'test_loss_bottom_decile': np.float64(63217.092529), 'test_loss_top_decile': np.float64(83442.996529), 'test_loss_min': np.float64(62583.44857), 'test_loss_max': np.float64(83442.996529), 'test_loss_bottom10%': np.float64(62583.44857), 'test_loss_top10%': np.float64(83442.996529), 'test_loss_cos1': np.float64(0.995243), 'test_loss_entropy': np.float64(2.297887), 'test_avg_loss_std': np.float64(1.22034), 'test_avg_loss_bottom_decile': np.float64(11.224626), 'test_avg_loss_top_decile': np.float64(14.815873), 'test_avg_loss_min': np.float64(11.112118), 'test_avg_loss_max': np.float64(14.815873), 'test_avg_loss_bottom10%': np.float64(11.112118), 'test_avg_loss_top10%': np.float64(14.815873), 'test_avg_loss_cos1': np.float64(0.995243), 'test_avg_loss_entropy': np.float64(2.297887), 'val_loss_std': np.float64(7504.293936), 'val_loss_bottom_decile': np.float64(64731.717384), 'val_loss_top_decile': np.float64(86622.435081), 'val_loss_min': np.float64(62474.386185), 'val_loss_max': np.float64(86622.435081), 'val_loss_bottom10%': np.float64(62474.386185), 'val_loss_top10%': np.float64(86622.435081), 'val_loss_cos1': np.float64(0.994631), 'val_loss_entropy': np.float64(2.297271), 'val_avg_loss_std': np.float64(1.332439), 'val_avg_loss_bottom_decile': np.float64(11.493558), 'val_avg_loss_top_decile': np.float64(15.380404), 'val_avg_loss_min': np.float64(11.092753), 'val_avg_loss_max': np.float64(15.380404), 'val_avg_loss_bottom10%': np.float64(11.092753), 'val_avg_loss_top10%': np.float64(15.380404), 'val_avg_loss_cos1': np.float64(0.994631), 'val_avg_loss_entropy': np.float64(2.297271)}}
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2024-11-18 06:48:16,209 (server:353) INFO: Server: Starting evaluation at the end of round 54.
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2024-11-18 06:48:16,209 (server:359) INFO: ----------- Starting a new training round (Round #55) -------------
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2024-11-18 07:04:31,954 (client:354) INFO: {'Role': 'Client #5', 'Round': 55, 'Results_raw': {'train_loss': 8.440406, 'val_loss': 8.479115, 'test_loss': 8.369579}}
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2024-11-18 07:11:30,151 (client:354) INFO: {'Role': 'Client #2', 'Round': 55, 'Results_raw': {'train_loss': 7.674501, 'val_loss': 7.667812, 'test_loss': 7.59761}}
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2024-11-18 07:18:10,186 (client:354) INFO: {'Role': 'Client #3', 'Round': 55, 'Results_raw': {'train_loss': 8.445739, 'val_loss': 8.336725, 'test_loss': 8.662475}}
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2024-11-18 07:24:53,418 (client:354) INFO: {'Role': 'Client #7', 'Round': 55, 'Results_raw': {'train_loss': 8.638897, 'val_loss': 8.345562, 'test_loss': 8.378968}}
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2024-11-18 07:31:22,851 (client:354) INFO: {'Role': 'Client #6', 'Round': 55, 'Results_raw': {'train_loss': 8.89779, 'val_loss': 8.973445, 'test_loss': 8.894931}}
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2024-11-18 07:37:50,120 (client:354) INFO: {'Role': 'Client #9', 'Round': 55, 'Results_raw': {'train_loss': 8.668304, 'val_loss': 8.817024, 'test_loss': 8.521173}}
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2024-11-18 07:43:45,213 (client:354) INFO: {'Role': 'Client #1', 'Round': 55, 'Results_raw': {'train_loss': 8.765248, 'val_loss': 8.628143, 'test_loss': 8.348669}}
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2024-11-18 07:50:22,332 (client:354) INFO: {'Role': 'Client #10', 'Round': 55, 'Results_raw': {'train_loss': 8.291114, 'val_loss': 8.160852, 'test_loss': 8.005863}}
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2024-11-18 07:57:13,733 (client:354) INFO: {'Role': 'Client #4', 'Round': 55, 'Results_raw': {'train_loss': 8.589957, 'val_loss': 8.977266, 'test_loss': 8.407407}}
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2024-11-18 08:03:31,585 (client:354) INFO: {'Role': 'Client #8', 'Round': 55, 'Results_raw': {'train_loss': 8.475123, 'val_loss': 8.633155, 'test_loss': 8.646111}}
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2024-11-18 08:03:31,595 (server:615) INFO: {'Role': 'Server #', 'Round': 54, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69616.490526), 'test_avg_loss': np.float64(12.360883), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71451.624734), 'val_avg_loss': np.float64(12.686723)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69616.490526), 'test_avg_loss': np.float64(12.360883), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71451.624734), 'val_avg_loss': np.float64(12.686723)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6474.319215), 'test_loss_bottom_decile': np.float64(62886.69426), 'test_loss_top_decile': np.float64(81705.785179), 'test_loss_min': np.float64(62297.869843), 'test_loss_max': np.float64(81705.785179), 'test_loss_bottom10%': np.float64(62297.869843), 'test_loss_top10%': np.float64(81705.785179), 'test_loss_cos1': np.float64(0.995703), 'test_loss_entropy': np.float64(2.298336), 'test_avg_loss_std': np.float64(1.14956), 'test_avg_loss_bottom_decile': np.float64(11.165961), 'test_avg_loss_top_decile': np.float64(14.507419), 'test_avg_loss_min': np.float64(11.061412), 'test_avg_loss_max': np.float64(14.507419), 'test_avg_loss_bottom10%': np.float64(11.061412), 'test_avg_loss_top10%': np.float64(14.507419), 'test_avg_loss_cos1': np.float64(0.995703), 'test_avg_loss_entropy': np.float64(2.298336), 'val_loss_std': np.float64(7112.629992), 'val_loss_bottom_decile': np.float64(64339.420677), 'val_loss_top_decile': np.float64(84914.704025), 'val_loss_min': np.float64(62162.028297), 'val_loss_max': np.float64(84914.704025), 'val_loss_bottom10%': np.float64(62162.028297), 'val_loss_top10%': np.float64(84914.704025), 'val_loss_cos1': np.float64(0.995082), 'val_loss_entropy': np.float64(2.297713), 'val_avg_loss_std': np.float64(1.262896), 'val_avg_loss_bottom_decile': np.float64(11.423903), 'val_avg_loss_top_decile': np.float64(15.077185), 'val_avg_loss_min': np.float64(11.037292), 'val_avg_loss_max': np.float64(15.077185), 'val_avg_loss_bottom10%': np.float64(11.037292), 'val_avg_loss_top10%': np.float64(15.077185), 'val_avg_loss_cos1': np.float64(0.995082), 'val_avg_loss_entropy': np.float64(2.297713)}}
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2024-11-18 08:03:31,684 (server:353) INFO: Server: Starting evaluation at the end of round 55.
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2024-11-18 08:03:31,684 (server:359) INFO: ----------- Starting a new training round (Round #56) -------------
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2024-11-18 08:18:59,419 (client:354) INFO: {'Role': 'Client #5', 'Round': 56, 'Results_raw': {'train_loss': 8.444103, 'val_loss': 8.491502, 'test_loss': 8.43557}}
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2024-11-18 08:25:45,107 (client:354) INFO: {'Role': 'Client #10', 'Round': 56, 'Results_raw': {'train_loss': 8.268892, 'val_loss': 8.099882, 'test_loss': 7.940496}}
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2024-11-18 08:32:35,836 (client:354) INFO: {'Role': 'Client #7', 'Round': 56, 'Results_raw': {'train_loss': 8.632576, 'val_loss': 8.229672, 'test_loss': 8.278149}}
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2024-11-18 08:39:30,902 (client:354) INFO: {'Role': 'Client #8', 'Round': 56, 'Results_raw': {'train_loss': 8.466553, 'val_loss': 8.655548, 'test_loss': 8.637727}}
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2024-11-18 08:45:59,294 (client:354) INFO: {'Role': 'Client #9', 'Round': 56, 'Results_raw': {'train_loss': 8.667498, 'val_loss': 8.758933, 'test_loss': 8.463789}}
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2024-11-18 08:52:44,825 (client:354) INFO: {'Role': 'Client #4', 'Round': 56, 'Results_raw': {'train_loss': 8.588984, 'val_loss': 8.939022, 'test_loss': 8.37346}}
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2024-11-18 08:59:58,970 (client:354) INFO: {'Role': 'Client #3', 'Round': 56, 'Results_raw': {'train_loss': 8.457983, 'val_loss': 8.285723, 'test_loss': 8.642864}}
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2024-11-18 09:07:06,465 (client:354) INFO: {'Role': 'Client #1', 'Round': 56, 'Results_raw': {'train_loss': 8.740767, 'val_loss': 8.708065, 'test_loss': 8.438352}}
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2024-11-18 09:13:40,715 (client:354) INFO: {'Role': 'Client #2', 'Round': 56, 'Results_raw': {'train_loss': 7.645496, 'val_loss': 7.556493, 'test_loss': 7.494143}}
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2024-11-18 09:20:31,378 (client:354) INFO: {'Role': 'Client #6', 'Round': 56, 'Results_raw': {'train_loss': 8.901869, 'val_loss': 8.959913, 'test_loss': 8.865098}}
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2024-11-18 09:20:31,401 (server:615) INFO: {'Role': 'Server #', 'Round': 55, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69854.285119), 'test_avg_loss': np.float64(12.403105), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71746.693086), 'val_avg_loss': np.float64(12.739115)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69854.285119), 'test_avg_loss': np.float64(12.403105), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71746.693086), 'val_avg_loss': np.float64(12.739115)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6917.11879), 'test_loss_bottom_decile': np.float64(62994.293373), 'test_loss_top_decile': np.float64(83668.40667), 'test_loss_min': np.float64(62001.979874), 'test_loss_max': np.float64(83668.40667), 'test_loss_bottom10%': np.float64(62001.979874), 'test_loss_top10%': np.float64(83668.40667), 'test_loss_cos1': np.float64(0.995133), 'test_loss_entropy': np.float64(2.297785), 'test_avg_loss_std': np.float64(1.228182), 'test_avg_loss_bottom_decile': np.float64(11.185066), 'test_avg_loss_top_decile': np.float64(14.855896), 'test_avg_loss_min': np.float64(11.008874), 'test_avg_loss_max': np.float64(14.855896), 'test_avg_loss_bottom10%': np.float64(11.008874), 'test_avg_loss_top10%': np.float64(14.855896), 'test_avg_loss_cos1': np.float64(0.995133), 'test_avg_loss_entropy': np.float64(2.297785), 'val_loss_std': np.float64(7589.827372), 'val_loss_bottom_decile': np.float64(64418.215324), 'val_loss_top_decile': np.float64(87062.051743), 'val_loss_min': np.float64(61947.310318), 'val_loss_max': np.float64(87062.051743), 'val_loss_bottom10%': np.float64(61947.310318), 'val_loss_top10%': np.float64(87062.051743), 'val_loss_cos1': np.float64(0.994451), 'val_loss_entropy': np.float64(2.297105), 'val_avg_loss_std': np.float64(1.347626), 'val_avg_loss_bottom_decile': np.float64(11.437893), 'val_avg_loss_top_decile': np.float64(15.458461), 'val_avg_loss_min': np.float64(10.999167), 'val_avg_loss_max': np.float64(15.458461), 'val_avg_loss_bottom10%': np.float64(10.999167), 'val_avg_loss_top10%': np.float64(15.458461), 'val_avg_loss_cos1': np.float64(0.994451), 'val_avg_loss_entropy': np.float64(2.297105)}}
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2024-11-18 09:20:31,518 (server:353) INFO: Server: Starting evaluation at the end of round 56.
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2024-11-18 09:20:31,520 (server:359) INFO: ----------- Starting a new training round (Round #57) -------------
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2024-11-18 09:36:57,502 (client:354) INFO: {'Role': 'Client #2', 'Round': 57, 'Results_raw': {'train_loss': 7.634276, 'val_loss': 7.621317, 'test_loss': 7.572445}}
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2024-11-18 09:44:14,463 (client:354) INFO: {'Role': 'Client #10', 'Round': 57, 'Results_raw': {'train_loss': 8.274961, 'val_loss': 8.16563, 'test_loss': 7.966079}}
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2024-11-18 09:51:16,111 (client:354) INFO: {'Role': 'Client #5', 'Round': 57, 'Results_raw': {'train_loss': 8.424299, 'val_loss': 8.485028, 'test_loss': 8.413509}}
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2024-11-18 09:58:14,411 (client:354) INFO: {'Role': 'Client #7', 'Round': 57, 'Results_raw': {'train_loss': 8.631424, 'val_loss': 8.376614, 'test_loss': 8.412272}}
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2024-11-18 10:05:23,326 (client:354) INFO: {'Role': 'Client #4', 'Round': 57, 'Results_raw': {'train_loss': 8.599343, 'val_loss': 9.014066, 'test_loss': 8.418192}}
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2024-11-18 10:12:01,883 (client:354) INFO: {'Role': 'Client #6', 'Round': 57, 'Results_raw': {'train_loss': 8.864906, 'val_loss': 8.953882, 'test_loss': 8.893987}}
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2024-11-18 10:18:53,080 (client:354) INFO: {'Role': 'Client #8', 'Round': 57, 'Results_raw': {'train_loss': 8.470387, 'val_loss': 8.599474, 'test_loss': 8.561367}}
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2024-11-18 10:25:48,396 (client:354) INFO: {'Role': 'Client #1', 'Round': 57, 'Results_raw': {'train_loss': 8.747193, 'val_loss': 8.603728, 'test_loss': 8.345162}}
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2024-11-18 10:32:58,487 (client:354) INFO: {'Role': 'Client #9', 'Round': 57, 'Results_raw': {'train_loss': 8.654097, 'val_loss': 8.799862, 'test_loss': 8.447799}}
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2024-11-18 10:39:54,888 (client:354) INFO: {'Role': 'Client #3', 'Round': 57, 'Results_raw': {'train_loss': 8.431092, 'val_loss': 8.371179, 'test_loss': 8.688229}}
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2024-11-18 10:39:54,919 (server:615) INFO: {'Role': 'Server #', 'Round': 56, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69554.665482), 'test_avg_loss': np.float64(12.349905), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71393.390211), 'val_avg_loss': np.float64(12.676383)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69554.665482), 'test_avg_loss': np.float64(12.349905), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71393.390211), 'val_avg_loss': np.float64(12.676383)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6702.145954), 'test_loss_bottom_decile': np.float64(62886.141609), 'test_loss_top_decile': np.float64(82860.840202), 'test_loss_min': np.float64(61910.514603), 'test_loss_max': np.float64(82860.840202), 'test_loss_bottom10%': np.float64(61910.514603), 'test_loss_top10%': np.float64(82860.840202), 'test_loss_cos1': np.float64(0.99539), 'test_loss_entropy': np.float64(2.298037), 'test_avg_loss_std': np.float64(1.190012), 'test_avg_loss_bottom_decile': np.float64(11.165863), 'test_avg_loss_top_decile': np.float64(14.712507), 'test_avg_loss_min': np.float64(10.992634), 'test_avg_loss_max': np.float64(14.712507), 'test_avg_loss_bottom10%': np.float64(10.992634), 'test_avg_loss_top10%': np.float64(14.712507), 'test_avg_loss_cos1': np.float64(0.99539), 'test_avg_loss_entropy': np.float64(2.298037), 'val_loss_std': np.float64(7347.75588), 'val_loss_bottom_decile': np.float64(64347.664978), 'val_loss_top_decile': np.float64(86116.660599), 'val_loss_min': np.float64(61821.690102), 'val_loss_max': np.float64(86116.660599), 'val_loss_bottom10%': np.float64(61821.690102), 'val_loss_top10%': np.float64(86116.660599), 'val_loss_cos1': np.float64(0.994746), 'val_loss_entropy': np.float64(2.297394), 'val_avg_loss_std': np.float64(1.304644), 'val_avg_loss_bottom_decile': np.float64(11.425367), 'val_avg_loss_top_decile': np.float64(15.2906), 'val_avg_loss_min': np.float64(10.976863), 'val_avg_loss_max': np.float64(15.2906), 'val_avg_loss_bottom10%': np.float64(10.976863), 'val_avg_loss_top10%': np.float64(15.2906), 'val_avg_loss_cos1': np.float64(0.994746), 'val_avg_loss_entropy': np.float64(2.297394)}}
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2024-11-18 10:39:54,986 (server:353) INFO: Server: Starting evaluation at the end of round 57.
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2024-11-18 10:39:54,988 (server:359) INFO: ----------- Starting a new training round (Round #58) -------------
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2024-11-18 10:56:59,467 (client:354) INFO: {'Role': 'Client #6', 'Round': 58, 'Results_raw': {'train_loss': 8.867748, 'val_loss': 8.862833, 'test_loss': 8.819219}}
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2024-11-18 11:04:04,564 (client:354) INFO: {'Role': 'Client #8', 'Round': 58, 'Results_raw': {'train_loss': 8.443378, 'val_loss': 8.648081, 'test_loss': 8.636775}}
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2024-11-18 11:10:49,272 (client:354) INFO: {'Role': 'Client #10', 'Round': 58, 'Results_raw': {'train_loss': 8.274925, 'val_loss': 8.190053, 'test_loss': 8.026337}}
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2024-11-18 11:17:37,661 (client:354) INFO: {'Role': 'Client #9', 'Round': 58, 'Results_raw': {'train_loss': 8.6394, 'val_loss': 8.785815, 'test_loss': 8.436511}}
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2024-11-18 11:24:32,728 (client:354) INFO: {'Role': 'Client #4', 'Round': 58, 'Results_raw': {'train_loss': 8.564415, 'val_loss': 8.941843, 'test_loss': 8.386632}}
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2024-11-18 11:31:07,240 (client:354) INFO: {'Role': 'Client #2', 'Round': 58, 'Results_raw': {'train_loss': 7.620931, 'val_loss': 7.624809, 'test_loss': 7.558575}}
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2024-11-18 11:37:15,674 (client:354) INFO: {'Role': 'Client #3', 'Round': 58, 'Results_raw': {'train_loss': 8.401435, 'val_loss': 8.222648, 'test_loss': 8.599324}}
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2024-11-18 11:43:24,215 (client:354) INFO: {'Role': 'Client #5', 'Round': 58, 'Results_raw': {'train_loss': 8.408851, 'val_loss': 8.423283, 'test_loss': 8.367967}}
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2024-11-18 11:50:07,601 (client:354) INFO: {'Role': 'Client #7', 'Round': 58, 'Results_raw': {'train_loss': 8.625153, 'val_loss': 8.179282, 'test_loss': 8.216784}}
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2024-11-18 11:57:20,922 (client:354) INFO: {'Role': 'Client #1', 'Round': 58, 'Results_raw': {'train_loss': 8.733846, 'val_loss': 8.595077, 'test_loss': 8.310536}}
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2024-11-18 11:57:20,941 (server:615) INFO: {'Role': 'Server #', 'Round': 57, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69740.847453), 'test_avg_loss': np.float64(12.382963), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71670.493654), 'val_avg_loss': np.float64(12.725585)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69740.847453), 'test_avg_loss': np.float64(12.382963), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71670.493654), 'val_avg_loss': np.float64(12.725585)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6828.440814), 'test_loss_bottom_decile': np.float64(62973.54525), 'test_loss_top_decile': np.float64(83392.25396), 'test_loss_min': np.float64(62250.498497), 'test_loss_max': np.float64(83392.25396), 'test_loss_bottom10%': np.float64(62250.498497), 'test_loss_top10%': np.float64(83392.25396), 'test_loss_cos1': np.float64(0.995241), 'test_loss_entropy': np.float64(2.297893), 'test_avg_loss_std': np.float64(1.212436), 'test_avg_loss_bottom_decile': np.float64(11.181382), 'test_avg_loss_top_decile': np.float64(14.806863), 'test_avg_loss_min': np.float64(11.053), 'test_avg_loss_max': np.float64(14.806863), 'test_avg_loss_bottom10%': np.float64(11.053), 'test_avg_loss_top10%': np.float64(14.806863), 'test_avg_loss_cos1': np.float64(0.995241), 'test_avg_loss_entropy': np.float64(2.297893), 'val_loss_std': np.float64(7492.703916), 'val_loss_bottom_decile': np.float64(64497.439339), 'val_loss_top_decile': np.float64(86816.177658), 'val_loss_min': np.float64(62203.301964), 'val_loss_max': np.float64(86816.177658), 'val_loss_bottom10%': np.float64(62203.301964), 'val_loss_top10%': np.float64(86816.177658), 'val_loss_cos1': np.float64(0.99458), 'val_loss_entropy': np.float64(2.297237), 'val_avg_loss_std': np.float64(1.330381), 'val_avg_loss_bottom_decile': np.float64(11.45196), 'val_avg_loss_top_decile': np.float64(15.414804), 'val_avg_loss_min': np.float64(11.04462), 'val_avg_loss_max': np.float64(15.414804), 'val_avg_loss_bottom10%': np.float64(11.04462), 'val_avg_loss_top10%': np.float64(15.414804), 'val_avg_loss_cos1': np.float64(0.99458), 'val_avg_loss_entropy': np.float64(2.297237)}}
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2024-11-18 11:57:21,060 (server:353) INFO: Server: Starting evaluation at the end of round 58.
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2024-11-18 11:57:21,060 (server:359) INFO: ----------- Starting a new training round (Round #59) -------------
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2024-11-18 12:14:17,189 (client:354) INFO: {'Role': 'Client #1', 'Round': 59, 'Results_raw': {'train_loss': 8.711675, 'val_loss': 8.579286, 'test_loss': 8.330987}}
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2024-11-18 12:21:32,440 (client:354) INFO: {'Role': 'Client #10', 'Round': 59, 'Results_raw': {'train_loss': 8.242775, 'val_loss': 8.144821, 'test_loss': 8.013873}}
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2024-11-18 12:28:15,527 (client:354) INFO: {'Role': 'Client #5', 'Round': 59, 'Results_raw': {'train_loss': 8.414743, 'val_loss': 8.469117, 'test_loss': 8.410873}}
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2024-11-18 12:35:16,828 (client:354) INFO: {'Role': 'Client #3', 'Round': 59, 'Results_raw': {'train_loss': 8.437473, 'val_loss': 8.467249, 'test_loss': 8.7922}}
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2024-11-18 12:42:02,771 (client:354) INFO: {'Role': 'Client #8', 'Round': 59, 'Results_raw': {'train_loss': 8.44358, 'val_loss': 8.581418, 'test_loss': 8.548712}}
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2024-11-18 12:48:36,614 (client:354) INFO: {'Role': 'Client #2', 'Round': 59, 'Results_raw': {'train_loss': 7.633016, 'val_loss': 7.494231, 'test_loss': 7.439166}}
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2024-11-18 12:55:21,399 (client:354) INFO: {'Role': 'Client #6', 'Round': 59, 'Results_raw': {'train_loss': 8.852985, 'val_loss': 8.848566, 'test_loss': 8.762479}}
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2024-11-18 13:02:17,156 (client:354) INFO: {'Role': 'Client #7', 'Round': 59, 'Results_raw': {'train_loss': 8.592712, 'val_loss': 8.200174, 'test_loss': 8.255188}}
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2024-11-18 13:09:17,291 (client:354) INFO: {'Role': 'Client #9', 'Round': 59, 'Results_raw': {'train_loss': 8.626804, 'val_loss': 8.7272, 'test_loss': 8.42517}}
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2024-11-18 13:16:02,804 (client:354) INFO: {'Role': 'Client #4', 'Round': 59, 'Results_raw': {'train_loss': 8.572181, 'val_loss': 8.927152, 'test_loss': 8.367822}}
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2024-11-18 13:16:02,822 (server:615) INFO: {'Role': 'Server #', 'Round': 58, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69015.183264), 'test_avg_loss': np.float64(12.254116), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70796.795753), 'val_avg_loss': np.float64(12.570454)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69015.183264), 'test_avg_loss': np.float64(12.254116), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70796.795753), 'val_avg_loss': np.float64(12.570454)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6411.579751), 'test_loss_bottom_decile': np.float64(62823.810219), 'test_loss_top_decile': np.float64(81965.895004), 'test_loss_min': np.float64(61634.781296), 'test_loss_max': np.float64(81965.895004), 'test_loss_bottom10%': np.float64(61634.781296), 'test_loss_top10%': np.float64(81965.895004), 'test_loss_cos1': np.float64(0.995712), 'test_loss_entropy': np.float64(2.298358), 'test_avg_loss_std': np.float64(1.13842), 'test_avg_loss_bottom_decile': np.float64(11.154796), 'test_avg_loss_top_decile': np.float64(14.553604), 'test_avg_loss_min': np.float64(10.943676), 'test_avg_loss_max': np.float64(14.553604), 'test_avg_loss_bottom10%': np.float64(10.943676), 'test_avg_loss_top10%': np.float64(14.553604), 'test_avg_loss_cos1': np.float64(0.995712), 'test_avg_loss_entropy': np.float64(2.298358), 'val_loss_std': np.float64(7052.931461), 'val_loss_bottom_decile': np.float64(64233.974976), 'val_loss_top_decile': np.float64(85124.15937), 'val_loss_min': np.float64(61558.594528), 'val_loss_max': np.float64(85124.15937), 'val_loss_bottom10%': np.float64(61558.594528), 'val_loss_top10%': np.float64(85124.15937), 'val_loss_cos1': np.float64(0.995074), 'val_loss_entropy': np.float64(2.297723), 'val_avg_loss_std': np.float64(1.252296), 'val_avg_loss_bottom_decile': np.float64(11.40518), 'val_avg_loss_top_decile': np.float64(15.114375), 'val_avg_loss_min': np.float64(10.930148), 'val_avg_loss_max': np.float64(15.114375), 'val_avg_loss_bottom10%': np.float64(10.930148), 'val_avg_loss_top10%': np.float64(15.114375), 'val_avg_loss_cos1': np.float64(0.995074), 'val_avg_loss_entropy': np.float64(2.297723)}}
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2024-11-18 13:16:02,988 (server:353) INFO: Server: Starting evaluation at the end of round 59.
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2024-11-18 13:16:02,992 (server:359) INFO: ----------- Starting a new training round (Round #60) -------------
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2024-11-18 13:33:25,810 (client:354) INFO: {'Role': 'Client #4', 'Round': 60, 'Results_raw': {'train_loss': 8.553224, 'val_loss': 8.910058, 'test_loss': 8.358441}}
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2024-11-18 13:40:06,816 (client:354) INFO: {'Role': 'Client #10', 'Round': 60, 'Results_raw': {'train_loss': 8.228415, 'val_loss': 8.154167, 'test_loss': 7.988067}}
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2024-11-18 13:47:28,581 (client:354) INFO: {'Role': 'Client #1', 'Round': 60, 'Results_raw': {'train_loss': 8.707329, 'val_loss': 8.577295, 'test_loss': 8.314858}}
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2024-11-18 13:54:45,220 (client:354) INFO: {'Role': 'Client #9', 'Round': 60, 'Results_raw': {'train_loss': 8.634455, 'val_loss': 8.826377, 'test_loss': 8.46582}}
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2024-11-18 14:01:33,751 (client:354) INFO: {'Role': 'Client #5', 'Round': 60, 'Results_raw': {'train_loss': 8.39586, 'val_loss': 8.516412, 'test_loss': 8.44229}}
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2024-11-18 14:08:40,888 (client:354) INFO: {'Role': 'Client #3', 'Round': 60, 'Results_raw': {'train_loss': 8.414435, 'val_loss': 8.365914, 'test_loss': 8.696266}}
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2024-11-18 14:15:27,253 (client:354) INFO: {'Role': 'Client #7', 'Round': 60, 'Results_raw': {'train_loss': 8.597075, 'val_loss': 8.232964, 'test_loss': 8.231141}}
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2024-11-18 14:22:12,721 (client:354) INFO: {'Role': 'Client #2', 'Round': 60, 'Results_raw': {'train_loss': 7.621206, 'val_loss': 7.623195, 'test_loss': 7.524623}}
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2024-11-18 14:28:56,178 (client:354) INFO: {'Role': 'Client #6', 'Round': 60, 'Results_raw': {'train_loss': 8.829263, 'val_loss': 8.837451, 'test_loss': 8.728425}}
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2024-11-18 14:36:03,407 (client:354) INFO: {'Role': 'Client #8', 'Round': 60, 'Results_raw': {'train_loss': 8.434724, 'val_loss': 8.538781, 'test_loss': 8.484013}}
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2024-11-18 14:36:03,414 (server:615) INFO: {'Role': 'Server #', 'Round': 59, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69550.141209), 'test_avg_loss': np.float64(12.349102), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71428.737743), 'val_avg_loss': np.float64(12.682659)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69550.141209), 'test_avg_loss': np.float64(12.349102), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71428.737743), 'val_avg_loss': np.float64(12.682659)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6964.437785), 'test_loss_bottom_decile': np.float64(62858.262604), 'test_loss_top_decile': np.float64(83740.799973), 'test_loss_min': np.float64(61872.389023), 'test_loss_max': np.float64(83740.799973), 'test_loss_bottom10%': np.float64(61872.389023), 'test_loss_top10%': np.float64(83740.799973), 'test_loss_cos1': np.float64(0.995024), 'test_loss_entropy': np.float64(2.297686), 'test_avg_loss_std': np.float64(1.236583), 'test_avg_loss_bottom_decile': np.float64(11.160913), 'test_avg_loss_top_decile': np.float64(14.86875), 'test_avg_loss_min': np.float64(10.985865), 'test_avg_loss_max': np.float64(14.86875), 'test_avg_loss_bottom10%': np.float64(10.985865), 'test_avg_loss_top10%': np.float64(14.86875), 'test_avg_loss_cos1': np.float64(0.995024), 'test_avg_loss_entropy': np.float64(2.297686), 'val_loss_std': np.float64(7628.413498), 'val_loss_bottom_decile': np.float64(64343.043549), 'val_loss_top_decile': np.float64(87114.790459), 'val_loss_min': np.float64(61823.995193), 'val_loss_max': np.float64(87114.790459), 'val_loss_bottom10%': np.float64(61823.995193), 'val_loss_top10%': np.float64(87114.790459), 'val_loss_cos1': np.float64(0.994345), 'val_loss_entropy': np.float64(2.297012), 'val_avg_loss_std': np.float64(1.354477), 'val_avg_loss_bottom_decile': np.float64(11.424546), 'val_avg_loss_top_decile': np.float64(15.467825), 'val_avg_loss_min': np.float64(10.977272), 'val_avg_loss_max': np.float64(15.467825), 'val_avg_loss_bottom10%': np.float64(10.977272), 'val_avg_loss_top10%': np.float64(15.467825), 'val_avg_loss_cos1': np.float64(0.994345), 'val_avg_loss_entropy': np.float64(2.297012)}}
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2024-11-18 14:36:03,476 (server:353) INFO: Server: Starting evaluation at the end of round 60.
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2024-11-18 14:36:03,477 (server:359) INFO: ----------- Starting a new training round (Round #61) -------------
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2024-11-18 14:53:15,211 (client:354) INFO: {'Role': 'Client #6', 'Round': 61, 'Results_raw': {'train_loss': 8.838194, 'val_loss': 8.808517, 'test_loss': 8.726299}}
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2024-11-18 15:00:22,845 (client:354) INFO: {'Role': 'Client #2', 'Round': 61, 'Results_raw': {'train_loss': 7.609785, 'val_loss': 7.452672, 'test_loss': 7.42654}}
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2024-11-18 15:06:51,072 (client:354) INFO: {'Role': 'Client #8', 'Round': 61, 'Results_raw': {'train_loss': 8.424482, 'val_loss': 8.567463, 'test_loss': 8.519607}}
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2024-11-18 15:13:32,809 (client:354) INFO: {'Role': 'Client #4', 'Round': 61, 'Results_raw': {'train_loss': 8.55023, 'val_loss': 8.951944, 'test_loss': 8.393252}}
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2024-11-18 15:20:17,039 (client:354) INFO: {'Role': 'Client #5', 'Round': 61, 'Results_raw': {'train_loss': 8.395222, 'val_loss': 8.614769, 'test_loss': 8.500582}}
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2024-11-18 15:26:55,270 (client:354) INFO: {'Role': 'Client #7', 'Round': 61, 'Results_raw': {'train_loss': 8.583282, 'val_loss': 8.390259, 'test_loss': 8.475369}}
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2024-11-18 15:34:13,808 (client:354) INFO: {'Role': 'Client #9', 'Round': 61, 'Results_raw': {'train_loss': 8.609487, 'val_loss': 8.729925, 'test_loss': 8.417442}}
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2024-11-18 15:41:18,455 (client:354) INFO: {'Role': 'Client #10', 'Round': 61, 'Results_raw': {'train_loss': 8.226224, 'val_loss': 8.096031, 'test_loss': 7.953103}}
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2024-11-18 15:48:32,464 (client:354) INFO: {'Role': 'Client #1', 'Round': 61, 'Results_raw': {'train_loss': 8.691589, 'val_loss': 8.723446, 'test_loss': 8.443894}}
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2024-11-18 15:56:25,449 (client:354) INFO: {'Role': 'Client #3', 'Round': 61, 'Results_raw': {'train_loss': 8.40616, 'val_loss': 8.466145, 'test_loss': 8.814548}}
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2024-11-18 15:56:25,465 (server:615) INFO: {'Role': 'Server #', 'Round': 60, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69324.209448), 'test_avg_loss': np.float64(12.308986), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71233.826519), 'val_avg_loss': np.float64(12.648052)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69324.209448), 'test_avg_loss': np.float64(12.308986), 'val_total': np.float64(5632.0), 'val_loss': np.float64(71233.826519), 'val_avg_loss': np.float64(12.648052)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6697.064737), 'test_loss_bottom_decile': np.float64(62824.53743), 'test_loss_top_decile': np.float64(82518.395622), 'test_loss_min': np.float64(61693.345291), 'test_loss_max': np.float64(82518.395622), 'test_loss_bottom10%': np.float64(61693.345291), 'test_loss_top10%': np.float64(82518.395622), 'test_loss_cos1': np.float64(0.995366), 'test_loss_entropy': np.float64(2.298015), 'test_avg_loss_std': np.float64(1.18911), 'test_avg_loss_bottom_decile': np.float64(11.154925), 'test_avg_loss_top_decile': np.float64(14.651704), 'test_avg_loss_min': np.float64(10.954074), 'test_avg_loss_max': np.float64(14.651704), 'test_avg_loss_bottom10%': np.float64(10.954074), 'test_avg_loss_top10%': np.float64(14.651704), 'test_avg_loss_cos1': np.float64(0.995366), 'test_avg_loss_entropy': np.float64(2.298015), 'val_loss_std': np.float64(7371.028655), 'val_loss_bottom_decile': np.float64(64337.165726), 'val_loss_top_decile': np.float64(85968.858368), 'val_loss_min': np.float64(61680.606499), 'val_loss_max': np.float64(85968.858368), 'val_loss_bottom10%': np.float64(61680.606499), 'val_loss_top10%': np.float64(85968.858368), 'val_loss_cos1': np.float64(0.994689), 'val_loss_entropy': np.float64(2.297341), 'val_avg_loss_std': np.float64(1.308776), 'val_avg_loss_bottom_decile': np.float64(11.423502), 'val_avg_loss_top_decile': np.float64(15.264357), 'val_avg_loss_min': np.float64(10.951812), 'val_avg_loss_max': np.float64(15.264357), 'val_avg_loss_bottom10%': np.float64(10.951812), 'val_avg_loss_top10%': np.float64(15.264357), 'val_avg_loss_cos1': np.float64(0.994689), 'val_avg_loss_entropy': np.float64(2.297341)}}
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2024-11-18 15:56:25,528 (server:353) INFO: Server: Starting evaluation at the end of round 61.
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2024-11-18 15:56:25,529 (server:359) INFO: ----------- Starting a new training round (Round #62) -------------
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2024-11-18 16:11:34,401 (client:354) INFO: {'Role': 'Client #4', 'Round': 62, 'Results_raw': {'train_loss': 8.549929, 'val_loss': 9.080687, 'test_loss': 8.530322}}
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2024-11-18 16:15:06,398 (client:354) INFO: {'Role': 'Client #8', 'Round': 62, 'Results_raw': {'train_loss': 8.407751, 'val_loss': 8.566083, 'test_loss': 8.541254}}
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2024-11-18 16:18:33,356 (client:354) INFO: {'Role': 'Client #3', 'Round': 62, 'Results_raw': {'train_loss': 8.377486, 'val_loss': 8.472516, 'test_loss': 8.781433}}
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2024-11-18 16:22:07,373 (client:354) INFO: {'Role': 'Client #10', 'Round': 62, 'Results_raw': {'train_loss': 8.226649, 'val_loss': 8.1647, 'test_loss': 8.049717}}
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2024-11-18 16:25:46,210 (client:354) INFO: {'Role': 'Client #2', 'Round': 62, 'Results_raw': {'train_loss': 7.606155, 'val_loss': 7.490989, 'test_loss': 7.408584}}
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2024-11-18 16:29:34,248 (client:354) INFO: {'Role': 'Client #6', 'Round': 62, 'Results_raw': {'train_loss': 8.810837, 'val_loss': 8.825695, 'test_loss': 8.821199}}
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2024-11-18 16:33:09,630 (client:354) INFO: {'Role': 'Client #9', 'Round': 62, 'Results_raw': {'train_loss': 8.608369, 'val_loss': 8.776021, 'test_loss': 8.479177}}
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2024-11-18 16:36:40,656 (client:354) INFO: {'Role': 'Client #5', 'Round': 62, 'Results_raw': {'train_loss': 8.394462, 'val_loss': 8.470723, 'test_loss': 8.397853}}
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2024-11-18 16:40:14,609 (client:354) INFO: {'Role': 'Client #7', 'Round': 62, 'Results_raw': {'train_loss': 8.581829, 'val_loss': 8.182682, 'test_loss': 8.210507}}
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2024-11-18 16:43:48,608 (client:354) INFO: {'Role': 'Client #1', 'Round': 62, 'Results_raw': {'train_loss': 8.692507, 'val_loss': 8.581872, 'test_loss': 8.312899}}
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2024-11-18 16:43:48,615 (server:615) INFO: {'Role': 'Server #', 'Round': 61, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69026.933838), 'test_avg_loss': np.float64(12.256203), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70824.050768), 'val_avg_loss': np.float64(12.575293)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(69026.933838), 'test_avg_loss': np.float64(12.256203), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70824.050768), 'val_avg_loss': np.float64(12.575293)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6480.206126), 'test_loss_bottom_decile': np.float64(62618.978844), 'test_loss_top_decile': np.float64(82132.607002), 'test_loss_min': np.float64(61782.48658), 'test_loss_max': np.float64(82132.607002), 'test_loss_bottom10%': np.float64(61782.48658), 'test_loss_top10%': np.float64(82132.607002), 'test_loss_cos1': np.float64(0.995622), 'test_loss_entropy': np.float64(2.298271), 'test_avg_loss_std': np.float64(1.150605), 'test_avg_loss_bottom_decile': np.float64(11.118427), 'test_avg_loss_top_decile': np.float64(14.583204), 'test_avg_loss_min': np.float64(10.969902), 'test_avg_loss_max': np.float64(14.583204), 'test_avg_loss_bottom10%': np.float64(10.969902), 'test_avg_loss_top10%': np.float64(14.583204), 'test_avg_loss_cos1': np.float64(0.995622), 'test_avg_loss_entropy': np.float64(2.298271), 'val_loss_std': np.float64(7097.750184), 'val_loss_bottom_decile': np.float64(64071.275986), 'val_loss_top_decile': np.float64(85254.449287), 'val_loss_min': np.float64(61668.102104), 'val_loss_max': np.float64(85254.449287), 'val_loss_bottom10%': np.float64(61668.102104), 'val_loss_top10%': np.float64(85254.449287), 'val_loss_cos1': np.float64(0.995016), 'val_loss_entropy': np.float64(2.297666), 'val_avg_loss_std': np.float64(1.260254), 'val_avg_loss_bottom_decile': np.float64(11.376292), 'val_avg_loss_top_decile': np.float64(15.137509), 'val_avg_loss_min': np.float64(10.949592), 'val_avg_loss_max': np.float64(15.137509), 'val_avg_loss_bottom10%': np.float64(10.949592), 'val_avg_loss_top10%': np.float64(15.137509), 'val_avg_loss_cos1': np.float64(0.995016), 'val_avg_loss_entropy': np.float64(2.297666)}}
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2024-11-18 16:43:48,653 (server:353) INFO: Server: Starting evaluation at the end of round 62.
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2024-11-18 16:43:48,654 (server:359) INFO: ----------- Starting a new training round (Round #63) -------------
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2024-11-18 16:52:58,296 (client:354) INFO: {'Role': 'Client #8', 'Round': 63, 'Results_raw': {'train_loss': 8.411048, 'val_loss': 8.589912, 'test_loss': 8.578142}}
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2024-11-18 16:59:17,744 (client:354) INFO: {'Role': 'Client #3', 'Round': 63, 'Results_raw': {'train_loss': 8.388439, 'val_loss': 8.196672, 'test_loss': 8.59276}}
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2024-11-18 17:06:40,641 (client:354) INFO: {'Role': 'Client #10', 'Round': 63, 'Results_raw': {'train_loss': 8.217566, 'val_loss': 8.165654, 'test_loss': 8.042014}}
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2024-11-18 17:13:56,591 (client:354) INFO: {'Role': 'Client #1', 'Round': 63, 'Results_raw': {'train_loss': 8.710469, 'val_loss': 8.624833, 'test_loss': 8.345698}}
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2024-11-18 17:21:07,226 (client:354) INFO: {'Role': 'Client #9', 'Round': 63, 'Results_raw': {'train_loss': 8.59973, 'val_loss': 8.845457, 'test_loss': 8.486116}}
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2024-11-18 17:28:32,004 (client:354) INFO: {'Role': 'Client #7', 'Round': 63, 'Results_raw': {'train_loss': 8.570524, 'val_loss': 8.187627, 'test_loss': 8.211056}}
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2024-11-18 17:36:52,247 (client:354) INFO: {'Role': 'Client #4', 'Round': 63, 'Results_raw': {'train_loss': 8.530336, 'val_loss': 8.860671, 'test_loss': 8.301649}}
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2024-11-18 17:46:50,120 (client:354) INFO: {'Role': 'Client #5', 'Round': 63, 'Results_raw': {'train_loss': 8.371745, 'val_loss': 8.508073, 'test_loss': 8.417948}}
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2024-11-18 17:57:44,469 (client:354) INFO: {'Role': 'Client #2', 'Round': 63, 'Results_raw': {'train_loss': 7.604384, 'val_loss': 7.443972, 'test_loss': 7.400748}}
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2024-11-18 18:08:52,475 (client:354) INFO: {'Role': 'Client #6', 'Round': 63, 'Results_raw': {'train_loss': 8.814244, 'val_loss': 8.774815, 'test_loss': 8.71593}}
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2024-11-18 18:08:52,561 (server:615) INFO: {'Role': 'Server #', 'Round': 62, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68143.711384), 'test_avg_loss': np.float64(12.099381), 'val_total': np.float64(5632.0), 'val_loss': np.float64(69877.654121), 'val_avg_loss': np.float64(12.407254)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68143.711384), 'test_avg_loss': np.float64(12.099381), 'val_total': np.float64(5632.0), 'val_loss': np.float64(69877.654121), 'val_avg_loss': np.float64(12.407254)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6190.593634), 'test_loss_bottom_decile': np.float64(61960.383522), 'test_loss_top_decile': np.float64(80667.182404), 'test_loss_min': np.float64(61261.19915), 'test_loss_max': np.float64(80667.182404), 'test_loss_bottom10%': np.float64(61261.19915), 'test_loss_top10%': np.float64(80667.182404), 'test_loss_cos1': np.float64(0.995899), 'test_loss_entropy': np.float64(2.298539), 'test_avg_loss_std': np.float64(1.099182), 'test_avg_loss_bottom_decile': np.float64(11.001489), 'test_avg_loss_top_decile': np.float64(14.323008), 'test_avg_loss_min': np.float64(10.877344), 'test_avg_loss_max': np.float64(14.323008), 'test_avg_loss_bottom10%': np.float64(10.877344), 'test_avg_loss_top10%': np.float64(14.323008), 'test_avg_loss_cos1': np.float64(0.995899), 'test_avg_loss_entropy': np.float64(2.298539), 'val_loss_std': np.float64(6793.670231), 'val_loss_bottom_decile': np.float64(63368.189903), 'val_loss_top_decile': np.float64(83723.760139), 'val_loss_min': np.float64(61220.59861), 'val_loss_max': np.float64(83723.760139), 'val_loss_bottom10%': np.float64(61220.59861), 'val_loss_top10%': np.float64(83723.760139), 'val_loss_cos1': np.float64(0.995307), 'val_loss_entropy': np.float64(2.297951), 'val_avg_loss_std': np.float64(1.206262), 'val_avg_loss_bottom_decile': np.float64(11.251454), 'val_avg_loss_top_decile': np.float64(14.865724), 'val_avg_loss_min': np.float64(10.870135), 'val_avg_loss_max': np.float64(14.865724), 'val_avg_loss_bottom10%': np.float64(10.870135), 'val_avg_loss_top10%': np.float64(14.865724), 'val_avg_loss_cos1': np.float64(0.995307), 'val_avg_loss_entropy': np.float64(2.297951)}}
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2024-11-18 18:08:52,934 (server:353) INFO: Server: Starting evaluation at the end of round 63.
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2024-11-18 18:08:52,935 (server:359) INFO: ----------- Starting a new training round (Round #64) -------------
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2024-11-18 18:42:09,711 (client:354) INFO: {'Role': 'Client #10', 'Round': 64, 'Results_raw': {'train_loss': 8.2212, 'val_loss': 8.167189, 'test_loss': 8.027151}}
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2024-11-18 18:53:37,058 (client:354) INFO: {'Role': 'Client #9', 'Round': 64, 'Results_raw': {'train_loss': 8.592702, 'val_loss': 8.777537, 'test_loss': 8.469091}}
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2024-11-18 19:04:50,195 (client:354) INFO: {'Role': 'Client #1', 'Round': 64, 'Results_raw': {'train_loss': 8.670693, 'val_loss': 8.614883, 'test_loss': 8.372751}}
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2024-11-18 19:16:18,289 (client:354) INFO: {'Role': 'Client #5', 'Round': 64, 'Results_raw': {'train_loss': 8.369789, 'val_loss': 8.42758, 'test_loss': 8.337589}}
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2024-11-18 19:27:56,310 (client:354) INFO: {'Role': 'Client #2', 'Round': 64, 'Results_raw': {'train_loss': 7.607807, 'val_loss': 7.596617, 'test_loss': 7.52735}}
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2024-11-18 19:39:31,145 (client:354) INFO: {'Role': 'Client #7', 'Round': 64, 'Results_raw': {'train_loss': 8.550613, 'val_loss': 8.242251, 'test_loss': 8.270252}}
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2024-11-18 19:50:57,464 (client:354) INFO: {'Role': 'Client #6', 'Round': 64, 'Results_raw': {'train_loss': 8.815148, 'val_loss': 8.82571, 'test_loss': 8.787447}}
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2024-11-18 20:02:00,414 (client:354) INFO: {'Role': 'Client #8', 'Round': 64, 'Results_raw': {'train_loss': 8.400932, 'val_loss': 8.542388, 'test_loss': 8.508223}}
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2024-11-18 20:13:51,376 (client:354) INFO: {'Role': 'Client #3', 'Round': 64, 'Results_raw': {'train_loss': 8.373529, 'val_loss': 8.213851, 'test_loss': 8.611422}}
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2024-11-18 20:25:26,445 (client:354) INFO: {'Role': 'Client #4', 'Round': 64, 'Results_raw': {'train_loss': 8.538847, 'val_loss': 8.840326, 'test_loss': 8.280477}}
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2024-11-18 20:25:26,482 (server:615) INFO: {'Role': 'Server #', 'Round': 63, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68466.378248), 'test_avg_loss': np.float64(12.156672), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70244.043892), 'val_avg_loss': np.float64(12.472309)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68466.378248), 'test_avg_loss': np.float64(12.156672), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70244.043892), 'val_avg_loss': np.float64(12.472309)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6355.873494), 'test_loss_bottom_decile': np.float64(62273.720078), 'test_loss_top_decile': np.float64(81214.030968), 'test_loss_min': np.float64(61117.862656), 'test_loss_max': np.float64(81214.030968), 'test_loss_bottom10%': np.float64(61117.862656), 'test_loss_top10%': np.float64(81214.030968), 'test_loss_cos1': np.float64(0.995719), 'test_loss_entropy': np.float64(2.298362), 'test_avg_loss_std': np.float64(1.128529), 'test_avg_loss_bottom_decile': np.float64(11.057124), 'test_avg_loss_top_decile': np.float64(14.420105), 'test_avg_loss_min': np.float64(10.851893), 'test_avg_loss_max': np.float64(14.420105), 'test_avg_loss_bottom10%': np.float64(10.851893), 'test_avg_loss_top10%': np.float64(14.420105), 'test_avg_loss_cos1': np.float64(0.995719), 'test_avg_loss_entropy': np.float64(2.298362), 'val_loss_std': np.float64(6963.366859), 'val_loss_bottom_decile': np.float64(63711.372337), 'val_loss_top_decile': np.float64(84339.602592), 'val_loss_min': np.float64(61125.078415), 'val_loss_max': np.float64(84339.602592), 'val_loss_bottom10%': np.float64(61125.078415), 'val_loss_top10%': np.float64(84339.602592), 'val_loss_cos1': np.float64(0.995122), 'val_loss_entropy': np.float64(2.297769), 'val_avg_loss_std': np.float64(1.236393), 'val_avg_loss_bottom_decile': np.float64(11.312389), 'val_avg_loss_top_decile': np.float64(14.975071), 'val_avg_loss_min': np.float64(10.853174), 'val_avg_loss_max': np.float64(14.975071), 'val_avg_loss_bottom10%': np.float64(10.853174), 'val_avg_loss_top10%': np.float64(14.975071), 'val_avg_loss_cos1': np.float64(0.995122), 'val_avg_loss_entropy': np.float64(2.297769)}}
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2024-11-18 20:25:26,822 (server:353) INFO: Server: Starting evaluation at the end of round 64.
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2024-11-18 20:25:26,827 (server:359) INFO: ----------- Starting a new training round (Round #65) -------------
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2024-11-18 20:51:10,864 (client:354) INFO: {'Role': 'Client #2', 'Round': 65, 'Results_raw': {'train_loss': 7.585865, 'val_loss': 7.541265, 'test_loss': 7.487903}}
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2024-11-18 20:57:33,971 (client:354) INFO: {'Role': 'Client #5', 'Round': 65, 'Results_raw': {'train_loss': 8.367965, 'val_loss': 8.417992, 'test_loss': 8.339336}}
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2024-11-18 21:04:48,893 (client:354) INFO: {'Role': 'Client #10', 'Round': 65, 'Results_raw': {'train_loss': 8.205799, 'val_loss': 8.162751, 'test_loss': 8.007277}}
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2024-11-18 21:11:07,140 (client:354) INFO: {'Role': 'Client #1', 'Round': 65, 'Results_raw': {'train_loss': 8.673947, 'val_loss': 8.647113, 'test_loss': 8.367993}}
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2024-11-18 21:14:55,879 (client:354) INFO: {'Role': 'Client #7', 'Round': 65, 'Results_raw': {'train_loss': 8.547581, 'val_loss': 8.405942, 'test_loss': 8.460932}}
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2024-11-18 21:18:46,259 (client:354) INFO: {'Role': 'Client #3', 'Round': 65, 'Results_raw': {'train_loss': 8.38964, 'val_loss': 8.274249, 'test_loss': 8.659895}}
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2024-11-18 21:22:20,280 (client:354) INFO: {'Role': 'Client #9', 'Round': 65, 'Results_raw': {'train_loss': 8.593865, 'val_loss': 8.725824, 'test_loss': 8.415447}}
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2024-11-18 21:25:55,910 (client:354) INFO: {'Role': 'Client #8', 'Round': 65, 'Results_raw': {'train_loss': 8.409904, 'val_loss': 8.624726, 'test_loss': 8.62172}}
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2024-11-18 21:29:26,987 (client:354) INFO: {'Role': 'Client #4', 'Round': 65, 'Results_raw': {'train_loss': 8.526053, 'val_loss': 8.850071, 'test_loss': 8.276904}}
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2024-11-18 21:33:26,805 (client:354) INFO: {'Role': 'Client #6', 'Round': 65, 'Results_raw': {'train_loss': 8.797761, 'val_loss': 8.826581, 'test_loss': 8.778149}}
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2024-11-18 21:33:26,829 (server:615) INFO: {'Role': 'Server #', 'Round': 64, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(67613.675295), 'test_avg_loss': np.float64(12.005269), 'val_total': np.float64(5632.0), 'val_loss': np.float64(69329.008586), 'val_avg_loss': np.float64(12.309838)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(67613.675295), 'test_avg_loss': np.float64(12.005269), 'val_total': np.float64(5632.0), 'val_loss': np.float64(69329.008586), 'val_avg_loss': np.float64(12.309838)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(5879.056856), 'test_loss_bottom_decile': np.float64(61739.806541), 'test_loss_top_decile': np.float64(78971.399239), 'test_loss_min': np.float64(60821.440659), 'test_loss_max': np.float64(78971.399239), 'test_loss_bottom10%': np.float64(60821.440659), 'test_loss_top10%': np.float64(78971.399239), 'test_loss_cos1': np.float64(0.996241), 'test_loss_entropy': np.float64(2.298868), 'test_avg_loss_std': np.float64(1.043867), 'test_avg_loss_bottom_decile': np.float64(10.962324), 'test_avg_loss_top_decile': np.float64(14.02191), 'test_avg_loss_min': np.float64(10.799261), 'test_avg_loss_max': np.float64(14.02191), 'test_avg_loss_bottom10%': np.float64(10.799261), 'test_avg_loss_top10%': np.float64(14.02191), 'test_avg_loss_cos1': np.float64(0.996241), 'test_avg_loss_entropy': np.float64(2.298868), 'val_loss_std': np.float64(6436.601733), 'val_loss_bottom_decile': np.float64(63139.337265), 'val_loss_top_decile': np.float64(81820.677704), 'val_loss_min': np.float64(60820.533272), 'val_loss_max': np.float64(81820.677704), 'val_loss_bottom10%': np.float64(60820.533272), 'val_loss_top10%': np.float64(81820.677704), 'val_loss_cos1': np.float64(0.995718), 'val_loss_entropy': np.float64(2.298343), 'val_avg_loss_std': np.float64(1.142863), 'val_avg_loss_bottom_decile': np.float64(11.21082), 'val_avg_loss_top_decile': np.float64(14.527819), 'val_avg_loss_min': np.float64(10.7991), 'val_avg_loss_max': np.float64(14.527819), 'val_avg_loss_bottom10%': np.float64(10.7991), 'val_avg_loss_top10%': np.float64(14.527819), 'val_avg_loss_cos1': np.float64(0.995718), 'val_avg_loss_entropy': np.float64(2.298343)}}
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2024-11-18 21:33:26,876 (server:353) INFO: Server: Starting evaluation at the end of round 65.
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2024-11-18 21:33:26,877 (server:359) INFO: ----------- Starting a new training round (Round #66) -------------
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2024-11-18 21:43:39,981 (client:354) INFO: {'Role': 'Client #4', 'Round': 66, 'Results_raw': {'train_loss': 8.505608, 'val_loss': 8.816101, 'test_loss': 8.2567}}
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2024-11-18 21:47:21,602 (client:354) INFO: {'Role': 'Client #6', 'Round': 66, 'Results_raw': {'train_loss': 8.787312, 'val_loss': 8.79967, 'test_loss': 8.700546}}
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2024-11-18 21:51:07,772 (client:354) INFO: {'Role': 'Client #9', 'Round': 66, 'Results_raw': {'train_loss': 8.578869, 'val_loss': 8.727642, 'test_loss': 8.405051}}
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2024-11-18 21:55:03,626 (client:354) INFO: {'Role': 'Client #5', 'Round': 66, 'Results_raw': {'train_loss': 8.363124, 'val_loss': 8.466463, 'test_loss': 8.391182}}
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2024-11-18 21:58:53,911 (client:354) INFO: {'Role': 'Client #2', 'Round': 66, 'Results_raw': {'train_loss': 7.558879, 'val_loss': 7.576264, 'test_loss': 7.517599}}
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2024-11-18 22:02:50,162 (client:354) INFO: {'Role': 'Client #10', 'Round': 66, 'Results_raw': {'train_loss': 8.185652, 'val_loss': 8.066304, 'test_loss': 7.934705}}
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2024-11-18 22:06:13,421 (client:354) INFO: {'Role': 'Client #8', 'Round': 66, 'Results_raw': {'train_loss': 8.404622, 'val_loss': 8.583913, 'test_loss': 8.582629}}
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2024-11-18 22:09:44,130 (client:354) INFO: {'Role': 'Client #1', 'Round': 66, 'Results_raw': {'train_loss': 8.653785, 'val_loss': 8.627774, 'test_loss': 8.391642}}
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2024-11-18 22:13:10,719 (client:354) INFO: {'Role': 'Client #7', 'Round': 66, 'Results_raw': {'train_loss': 8.537344, 'val_loss': 8.261005, 'test_loss': 8.280516}}
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2024-11-18 22:16:33,348 (client:354) INFO: {'Role': 'Client #3', 'Round': 66, 'Results_raw': {'train_loss': 8.346846, 'val_loss': 8.175275, 'test_loss': 8.557155}}
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2024-11-18 22:16:33,351 (server:615) INFO: {'Role': 'Server #', 'Round': 65, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68637.520465), 'test_avg_loss': np.float64(12.18706), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70390.775239), 'val_avg_loss': np.float64(12.498362)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68637.520465), 'test_avg_loss': np.float64(12.18706), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70390.775239), 'val_avg_loss': np.float64(12.498362)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6276.626314), 'test_loss_bottom_decile': np.float64(62294.484344), 'test_loss_top_decile': np.float64(80950.931717), 'test_loss_min': np.float64(61525.795128), 'test_loss_max': np.float64(80950.931717), 'test_loss_bottom10%': np.float64(61525.795128), 'test_loss_top10%': np.float64(80950.931717), 'test_loss_cos1': np.float64(0.995845), 'test_loss_entropy': np.float64(2.298481), 'test_avg_loss_std': np.float64(1.114458), 'test_avg_loss_bottom_decile': np.float64(11.06081), 'test_avg_loss_top_decile': np.float64(14.37339), 'test_avg_loss_min': np.float64(10.924324), 'test_avg_loss_max': np.float64(14.37339), 'test_avg_loss_bottom10%': np.float64(10.924324), 'test_avg_loss_top10%': np.float64(14.37339), 'test_avg_loss_cos1': np.float64(0.995845), 'test_avg_loss_entropy': np.float64(2.298481), 'val_loss_std': np.float64(6896.751192), 'val_loss_bottom_decile': np.float64(63755.529541), 'val_loss_top_decile': np.float64(83998.64119), 'val_loss_min': np.float64(61449.19976), 'val_loss_max': np.float64(83998.64119), 'val_loss_bottom10%': np.float64(61449.19976), 'val_loss_top10%': np.float64(83998.64119), 'val_loss_cos1': np.float64(0.995234), 'val_loss_entropy': np.float64(2.297872), 'val_avg_loss_std': np.float64(1.224565), 'val_avg_loss_bottom_decile': np.float64(11.320229), 'val_avg_loss_top_decile': np.float64(14.914531), 'val_avg_loss_min': np.float64(10.910724), 'val_avg_loss_max': np.float64(14.914531), 'val_avg_loss_bottom10%': np.float64(10.910724), 'val_avg_loss_top10%': np.float64(14.914531), 'val_avg_loss_cos1': np.float64(0.995234), 'val_avg_loss_entropy': np.float64(2.297872)}}
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2024-11-18 22:16:33,385 (server:353) INFO: Server: Starting evaluation at the end of round 66.
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2024-11-18 22:16:33,385 (server:359) INFO: ----------- Starting a new training round (Round #67) -------------
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2024-11-18 22:26:20,202 (client:354) INFO: {'Role': 'Client #3', 'Round': 67, 'Results_raw': {'train_loss': 8.362916, 'val_loss': 8.191359, 'test_loss': 8.577903}}
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2024-11-18 22:30:58,404 (client:354) INFO: {'Role': 'Client #9', 'Round': 67, 'Results_raw': {'train_loss': 8.577136, 'val_loss': 8.766657, 'test_loss': 8.461821}}
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2024-11-18 22:35:11,125 (client:354) INFO: {'Role': 'Client #5', 'Round': 67, 'Results_raw': {'train_loss': 8.351411, 'val_loss': 8.452486, 'test_loss': 8.358422}}
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2024-11-18 22:39:27,900 (client:354) INFO: {'Role': 'Client #7', 'Round': 67, 'Results_raw': {'train_loss': 8.536449, 'val_loss': 8.143579, 'test_loss': 8.174294}}
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2024-11-18 22:43:24,332 (client:354) INFO: {'Role': 'Client #4', 'Round': 67, 'Results_raw': {'train_loss': 8.507921, 'val_loss': 8.888889, 'test_loss': 8.334048}}
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2024-11-18 22:47:57,989 (client:354) INFO: {'Role': 'Client #1', 'Round': 67, 'Results_raw': {'train_loss': 8.652628, 'val_loss': 8.586165, 'test_loss': 8.308716}}
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2024-11-18 22:52:34,202 (client:354) INFO: {'Role': 'Client #6', 'Round': 67, 'Results_raw': {'train_loss': 8.776753, 'val_loss': 8.77131, 'test_loss': 8.7107}}
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2024-11-18 22:57:26,109 (client:354) INFO: {'Role': 'Client #10', 'Round': 67, 'Results_raw': {'train_loss': 8.185909, 'val_loss': 8.171622, 'test_loss': 8.056099}}
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2024-11-18 23:01:54,432 (client:354) INFO: {'Role': 'Client #2', 'Round': 67, 'Results_raw': {'train_loss': 7.562097, 'val_loss': 7.465729, 'test_loss': 7.401564}}
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2024-11-18 23:06:23,381 (client:354) INFO: {'Role': 'Client #8', 'Round': 67, 'Results_raw': {'train_loss': 8.391591, 'val_loss': 8.606192, 'test_loss': 8.629259}}
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2024-11-18 23:06:23,390 (server:615) INFO: {'Role': 'Server #', 'Round': 66, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68193.757575), 'test_avg_loss': np.float64(12.108267), 'val_total': np.float64(5632.0), 'val_loss': np.float64(69925.001106), 'val_avg_loss': np.float64(12.415661)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68193.757575), 'test_avg_loss': np.float64(12.108267), 'val_total': np.float64(5632.0), 'val_loss': np.float64(69925.001106), 'val_avg_loss': np.float64(12.415661)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6338.967416), 'test_loss_bottom_decile': np.float64(62031.797806), 'test_loss_top_decile': np.float64(81131.929054), 'test_loss_min': np.float64(61209.783524), 'test_loss_max': np.float64(81131.929054), 'test_loss_bottom10%': np.float64(61209.783524), 'test_loss_top10%': np.float64(81131.929054), 'test_loss_cos1': np.float64(0.995707), 'test_loss_entropy': np.float64(2.298356), 'test_avg_loss_std': np.float64(1.125527), 'test_avg_loss_bottom_decile': np.float64(11.014169), 'test_avg_loss_top_decile': np.float64(14.405527), 'test_avg_loss_min': np.float64(10.868214), 'test_avg_loss_max': np.float64(14.405527), 'test_avg_loss_bottom10%': np.float64(10.868214), 'test_avg_loss_top10%': np.float64(14.405527), 'test_avg_loss_cos1': np.float64(0.995707), 'test_avg_loss_entropy': np.float64(2.298356), 'val_loss_std': np.float64(6946.434611), 'val_loss_bottom_decile': np.float64(63441.019783), 'val_loss_top_decile': np.float64(84216.400665), 'val_loss_min': np.float64(61140.592865), 'val_loss_max': np.float64(84216.400665), 'val_loss_bottom10%': np.float64(61140.592865), 'val_loss_top10%': np.float64(84216.400665), 'val_loss_cos1': np.float64(0.995102), 'val_loss_entropy': np.float64(2.297753), 'val_avg_loss_std': np.float64(1.233387), 'val_avg_loss_bottom_decile': np.float64(11.264386), 'val_avg_loss_top_decile': np.float64(14.953196), 'val_avg_loss_min': np.float64(10.855929), 'val_avg_loss_max': np.float64(14.953196), 'val_avg_loss_bottom10%': np.float64(10.855929), 'val_avg_loss_top10%': np.float64(14.953196), 'val_avg_loss_cos1': np.float64(0.995102), 'val_avg_loss_entropy': np.float64(2.297753)}}
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2024-11-18 23:06:23,429 (server:353) INFO: Server: Starting evaluation at the end of round 67.
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2024-11-18 23:06:23,429 (server:359) INFO: ----------- Starting a new training round (Round #68) -------------
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2024-11-18 23:17:33,184 (client:354) INFO: {'Role': 'Client #9', 'Round': 68, 'Results_raw': {'train_loss': 8.588883, 'val_loss': 8.74206, 'test_loss': 8.443862}}
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2024-11-18 23:21:56,133 (client:354) INFO: {'Role': 'Client #2', 'Round': 68, 'Results_raw': {'train_loss': 7.583093, 'val_loss': 7.565811, 'test_loss': 7.476187}}
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2024-11-18 23:26:43,453 (client:354) INFO: {'Role': 'Client #5', 'Round': 68, 'Results_raw': {'train_loss': 8.34995, 'val_loss': 8.406079, 'test_loss': 8.348061}}
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2024-11-18 23:31:19,264 (client:354) INFO: {'Role': 'Client #1', 'Round': 68, 'Results_raw': {'train_loss': 8.639089, 'val_loss': 8.572835, 'test_loss': 8.307017}}
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2024-11-18 23:35:58,646 (client:354) INFO: {'Role': 'Client #10', 'Round': 68, 'Results_raw': {'train_loss': 8.179902, 'val_loss': 8.207155, 'test_loss': 8.03776}}
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2024-11-18 23:40:35,646 (client:354) INFO: {'Role': 'Client #4', 'Round': 68, 'Results_raw': {'train_loss': 8.496134, 'val_loss': 8.859725, 'test_loss': 8.282999}}
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2024-11-18 23:45:18,892 (client:354) INFO: {'Role': 'Client #3', 'Round': 68, 'Results_raw': {'train_loss': 8.370399, 'val_loss': 8.333522, 'test_loss': 8.711423}}
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2024-11-18 23:49:48,143 (client:354) INFO: {'Role': 'Client #7', 'Round': 68, 'Results_raw': {'train_loss': 8.537142, 'val_loss': 8.176458, 'test_loss': 8.225742}}
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2024-11-18 23:54:31,934 (client:354) INFO: {'Role': 'Client #8', 'Round': 68, 'Results_raw': {'train_loss': 8.379386, 'val_loss': 8.655479, 'test_loss': 8.653786}}
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2024-11-18 23:59:45,770 (client:354) INFO: {'Role': 'Client #6', 'Round': 68, 'Results_raw': {'train_loss': 8.780873, 'val_loss': 9.007842, 'test_loss': 8.965663}}
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2024-11-18 23:59:45,790 (server:615) INFO: {'Role': 'Server #', 'Round': 67, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68543.018475), 'test_avg_loss': np.float64(12.17028), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70318.203899), 'val_avg_loss': np.float64(12.485477)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68543.018475), 'test_avg_loss': np.float64(12.17028), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70318.203899), 'val_avg_loss': np.float64(12.485477)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6290.264606), 'test_loss_bottom_decile': np.float64(62144.433075), 'test_loss_top_decile': np.float64(80719.561279), 'test_loss_min': np.float64(61530.337784), 'test_loss_max': np.float64(80719.561279), 'test_loss_bottom10%': np.float64(61530.337784), 'test_loss_top10%': np.float64(80719.561279), 'test_loss_cos1': np.float64(0.995815), 'test_loss_entropy': np.float64(2.298452), 'test_avg_loss_std': np.float64(1.116879), 'test_avg_loss_bottom_decile': np.float64(11.034168), 'test_avg_loss_top_decile': np.float64(14.332308), 'test_avg_loss_min': np.float64(10.925131), 'test_avg_loss_max': np.float64(14.332308), 'test_avg_loss_bottom10%': np.float64(10.925131), 'test_avg_loss_top10%': np.float64(14.332308), 'test_avg_loss_cos1': np.float64(0.995815), 'test_avg_loss_entropy': np.float64(2.298452), 'val_loss_std': np.float64(6894.897836), 'val_loss_bottom_decile': np.float64(63607.336418), 'val_loss_top_decile': np.float64(83741.089149), 'val_loss_min': np.float64(61460.013519), 'val_loss_max': np.float64(83741.089149), 'val_loss_bottom10%': np.float64(61460.013519), 'val_loss_top10%': np.float64(83741.089149), 'val_loss_cos1': np.float64(0.995227), 'val_loss_entropy': np.float64(2.297863), 'val_avg_loss_std': np.float64(1.224236), 'val_avg_loss_bottom_decile': np.float64(11.293916), 'val_avg_loss_top_decile': np.float64(14.868801), 'val_avg_loss_min': np.float64(10.912644), 'val_avg_loss_max': np.float64(14.868801), 'val_avg_loss_bottom10%': np.float64(10.912644), 'val_avg_loss_top10%': np.float64(14.868801), 'val_avg_loss_cos1': np.float64(0.995227), 'val_avg_loss_entropy': np.float64(2.297863)}}
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2024-11-18 23:59:45,934 (server:353) INFO: Server: Starting evaluation at the end of round 68.
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2024-11-18 23:59:45,937 (server:359) INFO: ----------- Starting a new training round (Round #69) -------------
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2024-11-19 00:15:02,636 (client:354) INFO: {'Role': 'Client #10', 'Round': 69, 'Results_raw': {'train_loss': 8.166127, 'val_loss': 8.151317, 'test_loss': 7.996091}}
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2024-11-19 00:21:15,628 (client:354) INFO: {'Role': 'Client #8', 'Round': 69, 'Results_raw': {'train_loss': 8.372805, 'val_loss': 8.580581, 'test_loss': 8.534327}}
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2024-11-19 00:27:05,683 (client:354) INFO: {'Role': 'Client #6', 'Round': 69, 'Results_raw': {'train_loss': 8.772628, 'val_loss': 8.759298, 'test_loss': 8.715979}}
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2024-11-19 00:35:06,315 (client:354) INFO: {'Role': 'Client #5', 'Round': 69, 'Results_raw': {'train_loss': 8.333564, 'val_loss': 8.416637, 'test_loss': 8.352893}}
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2024-11-19 00:42:54,558 (client:354) INFO: {'Role': 'Client #9', 'Round': 69, 'Results_raw': {'train_loss': 8.572695, 'val_loss': 8.730955, 'test_loss': 8.418448}}
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2024-11-19 00:51:27,001 (client:354) INFO: {'Role': 'Client #7', 'Round': 69, 'Results_raw': {'train_loss': 8.498993, 'val_loss': 8.230458, 'test_loss': 8.2368}}
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2024-11-19 00:59:01,088 (client:354) INFO: {'Role': 'Client #2', 'Round': 69, 'Results_raw': {'train_loss': 7.531249, 'val_loss': 7.522577, 'test_loss': 7.455067}}
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2024-11-19 01:06:26,563 (client:354) INFO: {'Role': 'Client #4', 'Round': 69, 'Results_raw': {'train_loss': 8.503168, 'val_loss': 8.895948, 'test_loss': 8.321752}}
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2024-11-19 01:14:31,258 (client:354) INFO: {'Role': 'Client #1', 'Round': 69, 'Results_raw': {'train_loss': 8.625572, 'val_loss': 8.554356, 'test_loss': 8.278241}}
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2024-11-19 01:22:05,696 (client:354) INFO: {'Role': 'Client #3', 'Round': 69, 'Results_raw': {'train_loss': 8.324262, 'val_loss': 8.283725, 'test_loss': 8.645691}}
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2024-11-19 01:22:05,745 (server:615) INFO: {'Role': 'Server #', 'Round': 68, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68863.249192), 'test_avg_loss': np.float64(12.227139), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70656.329291), 'val_avg_loss': np.float64(12.545513)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68863.249192), 'test_avg_loss': np.float64(12.227139), 'val_total': np.float64(5632.0), 'val_loss': np.float64(70656.329291), 'val_avg_loss': np.float64(12.545513)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6295.453714), 'test_loss_bottom_decile': np.float64(62498.175301), 'test_loss_top_decile': np.float64(81147.587379), 'test_loss_min': np.float64(61894.991753), 'test_loss_max': np.float64(81147.587379), 'test_loss_bottom10%': np.float64(61894.991753), 'test_loss_top10%': np.float64(81147.587379), 'test_loss_cos1': np.float64(0.995847), 'test_loss_entropy': np.float64(2.298486), 'test_avg_loss_std': np.float64(1.117801), 'test_avg_loss_bottom_decile': np.float64(11.096977), 'test_avg_loss_top_decile': np.float64(14.408307), 'test_avg_loss_min': np.float64(10.989878), 'test_avg_loss_max': np.float64(14.408307), 'test_avg_loss_bottom10%': np.float64(10.989878), 'test_avg_loss_top10%': np.float64(14.408307), 'test_avg_loss_cos1': np.float64(0.995847), 'test_avg_loss_entropy': np.float64(2.298486), 'val_loss_std': np.float64(6900.433867), 'val_loss_bottom_decile': np.float64(63980.390594), 'val_loss_top_decile': np.float64(84354.828346), 'val_loss_min': np.float64(61822.484772), 'val_loss_max': np.float64(84354.828346), 'val_loss_bottom10%': np.float64(61822.484772), 'val_loss_top10%': np.float64(84354.828346), 'val_loss_cos1': np.float64(0.995265), 'val_loss_entropy': np.float64(2.297906), 'val_avg_loss_std': np.float64(1.225219), 'val_avg_loss_bottom_decile': np.float64(11.360155), 'val_avg_loss_top_decile': np.float64(14.977775), 'val_avg_loss_min': np.float64(10.977004), 'val_avg_loss_max': np.float64(14.977775), 'val_avg_loss_bottom10%': np.float64(10.977004), 'val_avg_loss_top10%': np.float64(14.977775), 'val_avg_loss_cos1': np.float64(0.995265), 'val_avg_loss_entropy': np.float64(2.297906)}}
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2024-11-19 01:22:06,001 (server:370) INFO: Server: Training is finished! Starting evaluation.
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2024-11-19 01:33:20,735 (server:615) INFO: {'Role': 'Server #', 'Round': 69, 'Results_weighted_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68026.043088), 'test_avg_loss': np.float64(12.078488), 'val_total': np.float64(5632.0), 'val_loss': np.float64(69778.406473), 'val_avg_loss': np.float64(12.389632)}, 'Results_avg': {'test_total': np.float64(5632.0), 'test_loss': np.float64(68026.043088), 'test_avg_loss': np.float64(12.078488), 'val_total': np.float64(5632.0), 'val_loss': np.float64(69778.406473), 'val_avg_loss': np.float64(12.389632)}, 'Results_fairness': {'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(6334.652176), 'test_loss_bottom_decile': np.float64(61968.968613), 'test_loss_top_decile': np.float64(80916.847527), 'test_loss_min': np.float64(61084.973824), 'test_loss_max': np.float64(80916.847527), 'test_loss_bottom10%': np.float64(61084.973824), 'test_loss_top10%': np.float64(80916.847527), 'test_loss_cos1': np.float64(0.995692), 'test_loss_entropy': np.float64(2.298341), 'test_avg_loss_std': np.float64(1.124761), 'test_avg_loss_bottom_decile': np.float64(11.003013), 'test_avg_loss_top_decile': np.float64(14.367338), 'test_avg_loss_min': np.float64(10.846054), 'test_avg_loss_max': np.float64(14.367338), 'test_avg_loss_bottom10%': np.float64(10.846054), 'test_avg_loss_top10%': np.float64(14.367338), 'test_avg_loss_cos1': np.float64(0.995692), 'test_avg_loss_entropy': np.float64(2.298341), 'val_loss_std': np.float64(6949.698039), 'val_loss_bottom_decile': np.float64(63432.693161), 'val_loss_top_decile': np.float64(84096.907455), 'val_loss_min': np.float64(61028.216858), 'val_loss_max': np.float64(84096.907455), 'val_loss_bottom10%': np.float64(61028.216858), 'val_loss_top10%': np.float64(84096.907455), 'val_loss_cos1': np.float64(0.995077), 'val_loss_entropy': np.float64(2.297731), 'val_avg_loss_std': np.float64(1.233966), 'val_avg_loss_bottom_decile': np.float64(11.262907), 'val_avg_loss_top_decile': np.float64(14.931979), 'val_avg_loss_min': np.float64(10.835976), 'val_avg_loss_max': np.float64(14.931979), 'val_avg_loss_bottom10%': np.float64(10.835976), 'val_avg_loss_top10%': np.float64(14.931979), 'val_avg_loss_cos1': np.float64(0.995077), 'val_avg_loss_entropy': np.float64(2.297731)}}
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2024-11-19 01:33:20,740 (server:420) INFO: Server: Final evaluation is finished! Starting merging results.
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2024-11-19 01:33:20,741 (server:546) INFO: {'Role': 'Server #', 'Round': 'Final', 'Results_raw': {'client_best_individual': {'val_loss': 60820.533272, 'test_total': 5632.0, 'test_loss': 60821.440659, 'test_avg_loss': 10.799261, 'val_total': 5632.0, 'val_avg_loss': 10.7991}, 'client_summarized_weighted_avg': {'val_loss': np.float64(69329.008586), 'test_total': np.float64(5632.0), 'test_loss': np.float64(67613.675295), 'test_avg_loss': np.float64(12.005269), 'val_total': np.float64(5632.0), 'val_avg_loss': np.float64(12.309838)}, 'client_summarized_avg': {'val_loss': np.float64(69329.008586), 'test_total': np.float64(5632.0), 'test_loss': np.float64(67613.675295), 'test_avg_loss': np.float64(12.005269), 'val_total': np.float64(5632.0), 'val_avg_loss': np.float64(12.309838)}, 'client_summarized_fairness': {'val_loss_entropy': np.float64(2.273437), 'val_loss_cos1': np.float64(0.970573), 'val_loss_top10%': np.float64(988673.398621), 'val_loss_bottom10%': np.float64(463003.740387), 'val_loss_max': np.float64(988673.398621), 'val_loss_min': np.float64(463003.740387), 'val_loss_top_decile': np.float64(988673.398621), 'val_loss_bottom_decile': np.float64(498269.112946), 'val_loss_std': np.float64(158744.543141), 'test_total': np.float64(5632.0), 'val_total': np.float64(5632.0), 'test_loss_std': np.float64(135382.499927), 'test_loss_bottom_decile': np.float64(470824.836731), 'test_loss_top_decile': np.float64(896531.748108), 'test_loss_min': np.float64(439096.04715), 'test_loss_max': np.float64(896531.748108), 'test_loss_bottom10%': np.float64(439096.04715), 'test_loss_top10%': np.float64(896531.748108), 'test_loss_cos1': np.float64(0.975212), 'test_loss_entropy': np.float64(2.278097), 'test_avg_loss_std': np.float64(24.038086), 'test_avg_loss_bottom_decile': np.float64(83.59816), 'test_avg_loss_top_decile': np.float64(159.185325), 'test_avg_loss_min': np.float64(77.964497), 'test_avg_loss_max': np.float64(159.185325), 'test_avg_loss_bottom10%': np.float64(77.964497), 'test_avg_loss_top10%': np.float64(159.185325), 'test_avg_loss_cos1': np.float64(0.975212), 'test_avg_loss_entropy': np.float64(2.278097), 'val_avg_loss_std': np.float64(28.186176), 'val_avg_loss_bottom_decile': np.float64(88.471078), 'val_avg_loss_top_decile': np.float64(175.545703), 'val_avg_loss_min': np.float64(82.209471), 'val_avg_loss_max': np.float64(175.545703), 'val_avg_loss_bottom10%': np.float64(82.209471), 'val_avg_loss_top10%': np.float64(175.545703), 'val_avg_loss_cos1': np.float64(0.970573), 'val_avg_loss_entropy': np.float64(2.273437)}}}
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2024-11-19 01:33:20,744 (server:565) INFO: {'Role': 'Client #1', 'Round': 70, 'Results_raw': {'test_total': 5632, 'test_loss': 68789.594826, 'test_avg_loss': 12.214062, 'val_total': 5632, 'val_loss': 71172.754257, 'val_avg_loss': 12.637208}}
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2024-11-19 01:33:20,744 (server:565) INFO: {'Role': 'Client #2', 'Round': 70, 'Results_raw': {'test_total': 5632, 'test_loss': 62687.377441, 'test_avg_loss': 11.130571, 'val_total': 5632, 'val_loss': 63847.559959, 'val_avg_loss': 11.33657}}
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2024-11-19 01:33:20,745 (server:565) INFO: {'Role': 'Client #3', 'Round': 70, 'Results_raw': {'test_total': 5632, 'test_loss': 74184.313629, 'test_avg_loss': 13.171931, 'val_total': 5632, 'val_loss': 73851.283691, 'val_avg_loss': 13.112799}}
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2024-11-19 01:33:20,745 (server:565) INFO: {'Role': 'Client #4', 'Round': 70, 'Results_raw': {'test_total': 5632, 'test_loss': 65537.005096, 'test_avg_loss': 11.636542, 'val_total': 5632, 'val_loss': 68872.964172, 'val_avg_loss': 12.228864}}
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2024-11-19 01:33:20,746 (server:565) INFO: {'Role': 'Client #5', 'Round': 70, 'Results_raw': {'test_total': 5632, 'test_loss': 61968.968613, 'test_avg_loss': 11.003013, 'val_total': 5632, 'val_loss': 63432.693161, 'val_avg_loss': 11.262907}}
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2024-11-19 01:33:20,746 (server:565) INFO: {'Role': 'Client #6', 'Round': 70, 'Results_raw': {'test_total': 5632, 'test_loss': 75468.129761, 'test_avg_loss': 13.399881, 'val_total': 5632, 'val_loss': 78863.095329, 'val_avg_loss': 14.00268}}
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2024-11-19 01:33:20,747 (server:565) INFO: {'Role': 'Client #7', 'Round': 70, 'Results_raw': {'test_total': 5632, 'test_loss': 80916.847527, 'test_avg_loss': 14.367338, 'val_total': 5632, 'val_loss': 84096.907455, 'val_avg_loss': 14.931979}}
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2024-11-19 01:33:20,747 (server:565) INFO: {'Role': 'Client #8', 'Round': 70, 'Results_raw': {'test_total': 5632, 'test_loss': 65513.381989, 'test_avg_loss': 11.632348, 'val_total': 5632, 'val_loss': 66283.605309, 'val_avg_loss': 11.769106}}
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2024-11-19 01:33:20,748 (server:565) INFO: {'Role': 'Client #9', 'Round': 70, 'Results_raw': {'test_total': 5632, 'test_loss': 64109.838173, 'test_avg_loss': 11.383139, 'val_total': 5632, 'val_loss': 66334.984535, 'val_avg_loss': 11.778229}}
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2024-11-19 01:33:20,748 (server:565) INFO: {'Role': 'Client #10', 'Round': 70, 'Results_raw': {'test_total': 5632, 'test_loss': 61084.973824, 'test_avg_loss': 10.846054, 'val_total': 5632, 'val_loss': 61028.216858, 'val_avg_loss': 10.835976}}
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2024-11-19 01:33:20,751 (monitor:173) INFO: In worker #0, the system-related metrics are: {'id': 0, 'fl_end_time_minutes': 4709.058612, '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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2024-11-19 01:33:20,758 (client:582) INFO: ================= client 1 received finish message =================
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2024-11-19 01:33:20,777 (monitor:173) INFO: In worker #1, the system-related metrics are: {'id': 1, 'fl_end_time_minutes': 4709.058526, 'total_model_size': 564874, 'total_flops': 246478130330880.0, '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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2024-11-19 01:33:20,778 (client:582) INFO: ================= client 2 received finish message =================
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2024-11-19 01:33:20,791 (monitor:173) INFO: In worker #2, the system-related metrics are: {'id': 2, 'fl_end_time_minutes': 4709.058102, 'total_model_size': 564874, 'total_flops': 246478130330880.0, '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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2024-11-19 01:33:20,792 (client:582) INFO: ================= client 3 received finish message =================
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2024-11-19 01:33:20,806 (monitor:173) INFO: In worker #3, the system-related metrics are: {'id': 3, 'fl_end_time_minutes': 4709.058032, 'total_model_size': 564874, 'total_flops': 246478130330880.0, '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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2024-11-19 01:33:20,806 (client:582) INFO: ================= client 4 received finish message =================
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2024-11-19 01:33:20,819 (monitor:173) INFO: In worker #4, the system-related metrics are: {'id': 4, 'fl_end_time_minutes': 4709.057986, 'total_model_size': 564874, 'total_flops': 246478130330880.0, '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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2024-11-19 01:33:20,819 (client:582) INFO: ================= client 5 received finish message =================
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2024-11-19 01:33:20,827 (monitor:173) INFO: In worker #5, the system-related metrics are: {'id': 5, 'fl_end_time_minutes': 4709.057871, 'total_model_size': 564874, 'total_flops': 246478130330880.0, '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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2024-11-19 01:33:20,828 (client:582) INFO: ================= client 6 received finish message =================
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2024-11-19 01:33:20,835 (monitor:173) INFO: In worker #6, the system-related metrics are: {'id': 6, 'fl_end_time_minutes': 4709.057733, 'total_model_size': 564874, 'total_flops': 246478130330880.0, '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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2024-11-19 01:33:20,836 (client:582) INFO: ================= client 7 received finish message =================
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2024-11-19 01:33:20,842 (monitor:173) INFO: In worker #7, the system-related metrics are: {'id': 7, 'fl_end_time_minutes': 4709.057559, 'total_model_size': 564874, 'total_flops': 246478130330880.0, '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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2024-11-19 01:33:20,843 (client:582) INFO: ================= client 8 received finish message =================
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2024-11-19 01:33:20,850 (monitor:173) INFO: In worker #8, the system-related metrics are: {'id': 8, 'fl_end_time_minutes': 4709.057347, 'total_model_size': 564874, 'total_flops': 246478130330880.0, '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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2024-11-19 01:33:20,851 (client:582) INFO: ================= client 9 received finish message =================
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2024-11-19 01:33:20,858 (monitor:173) INFO: In worker #9, the system-related metrics are: {'id': 9, 'fl_end_time_minutes': 4709.057194, 'total_model_size': 564874, 'total_flops': 246478130330880.0, '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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2024-11-19 01:33:20,858 (client:582) INFO: ================= client 10 received finish message =================
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2024-11-19 01:33:20,863 (monitor:173) INFO: In worker #10, the system-related metrics are: {'id': 10, 'fl_end_time_minutes': 4709.057026, 'total_model_size': 564874, 'total_flops': 246478130330880.0, '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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2024-11-19 01:33:20,864 (monitor:338) INFO: We will compress the file eval_results.raw into a .gz file, and delete the old one
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2024-11-19 01:33:20,934 (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(4709.057817), 'sys_avg/total_model_size': '501.49K', 'sys_avg/total_flops': '203.79T', '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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2024-11-19 01:33:20,935 (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(0.000486), 'sys_std/total_model_size': '158.58K', 'sys_std/total_flops': '64.44T', '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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