2024-11-14 09:45:05,259 (logging:124) INFO: the current machine is at 127.0.1.1 2024-11-14 09:45:05,260 (logging:126) INFO: the current dir is /home/czzhangheng/code/FederatedScope 2024-11-14 09:45:05,260 (logging:127) INFO: the output dir is exp/FedAvg_FedDGCN_on_trafficflow_lr0.003_lstep1/sub_exp_20241114094505 2024-11-14 09:45:49,140 (config:243) INFO: the used configs are: aggregator: BFT_args: byzantine_node_num: 0 inside_weight: 1.0 num_agg_groups: 1 num_agg_topk: [] outside_weight: 0.0 robust_rule: fedavg asyn: use: False attack: alpha_TV: 0.001 alpha_prop_loss: 0 attack_method: attacker_id: -1 classifier_PIA: randomforest edge_num: 100 edge_path: edge_data/ freq: 10 info_diff_type: l2 inject_round: 0 insert_round: 100000 label_type: dirty max_ite: 400 mean: [0.9637] mia_is_simulate_in: False mia_simulate_in_round: 20 pgd_eps: 2 pgd_lr: 0.1 pgd_poisoning: False poison_ratio: 0.5 reconstruct_lr: 0.01 reconstruct_optim: Adam scale_para: 1.0 scale_poisoning: False self_epoch: 6 self_lr: 0.05 self_opt: False setting: fix std: [0.1592] target_label_ind: -1 trigger_path: trigger/ trigger_type: edge backend: torch cfg_file: check_completeness: False criterion: type: RMSE data: add_day_in_week: True add_time_in_day: True args: [] batch_size: 64 cSBM_phi: [0.5, 0.5, 0.5] cache_dir: column_wise: False consistent_label_distribution: True days_per_week: 7 default_graph: True drop_last: False file_path: hetero_data_name: [] hetero_synth_batch_size: 32 hetero_synth_feat_dim: 128 hetero_synth_prim_weight: 0.5 horizon: 12 is_debug: False lag: 12 loader: max_query_len: 128 max_seq_len: 384 max_tgt_len: 128 normalizer: std num_contrast: 0 num_nodes: 358 num_of_client_for_data: [] num_steps: 30 num_workers: 0 pre_transform: [] quadratic: dim: 1 max_curv: 12.5 min_curv: 0.02 root: data/trafficflow/PeMS03 save_data: False scaler: [181.375268, 144.408363] server_holds_all: False shuffle: True sizes: [10, 5] splits: [0.8, 0.1, 0.1] splitter: trafficflowprediction splitter_args: [] steps_per_day: 288 subsample: 1.0 target_transform: [] test_pre_transform: [] test_ratio: 0.2 test_target_transform: [] test_transform: [] tod: False transform: [] trunc_stride: 128 type: trafficflow val_pre_transform: [] val_ratio: 0.2 val_target_transform: [] val_transform: [] walk_length: 2 dataloader: batch_size: 64 drop_last: True num_steps: 30 num_workers: 0 pin_memory: False shuffle: True sizes: [10, 5] theta: -1 type: trafficflow walk_length: 2 device: 1 distribute: use: False early_stop: delta: 0.0 improve_indicator_mode: best patience: 60 eval: best_res_update_round_wise_key: val_loss count_flops: True freq: 1 metrics: ['avg_loss'] monitoring: [] report: ['weighted_avg', 'avg', 'fairness', 'raw'] split: ['test', 'val'] expname: FedAvg_FedDGCN_on_trafficflow_lr0.003_lstep1 expname_tag: feat_engr: num_bins: 5 scenario: hfl secure: dp: encrypt: type: dummy key_size: 3072 type: encrypt selec_threshold: 0.05 selec_woe_binning: quantile type: federate: atc_load_from: atc_vanilla: False client_num: 10 data_weighted_aggr: False ignore_weight: False join_in_info: [] make_global_eval: False master_addr: 127.0.0.1 master_port: 29500 merge_test_data: False merge_val_data: False method: FedAvg mode: standalone online_aggr: False process_num: 1 resource_info_file: restore_from: sample_client_num: 10 sample_client_rate: -1.0 sampler: uniform save_to: share_local_model: False total_round_num: 70 unseen_clients_rate: 0.0 use_diff: False use_ss: False fedopt: use: False fedprox: use: False fedsageplus: a: 1.0 b: 1.0 c: 1.0 fedgen_epoch: 200 gen_hidden: 128 hide_portion: 0.5 loc_epoch: 1 num_pred: 5 fedswa: use: False finetune: batch_or_epoch: epoch before_eval: False epoch_linear: 10 freeze_param: local_param: [] local_update_steps: 1 lr_linear: 0.005 optimizer: lr: 0.1 type: SGD scheduler: type: warmup_ratio: 0.0 simple_tuning: False weight_decay: 0.0 flitplus: factor_ema: 0.8 lambdavat: 0.5 tmpFed: 0.5 weightReg: 1.0 gcflplus: EPS_1: 0.05 EPS_2: 0.1 seq_length: 5 standardize: False grad: grad_accum_count: 1 grad_clip: 5.0 hpo: fedex: cutoff: 0.0 diff: False eta0: -1.0 flatten_ss: True gamma: 0.0 pi_lr: 0.01 psn: False sched: auto ss: use: False fts: M: 100 M_target: 200 allow_load_existing_info: True diff: False fed_bo_max_iter: 50 g_var: 1e-06 gp_opt_schedule: 1 local_bo_epochs: 50 local_bo_max_iter: 50 ls: 1.0 obs_noise: 1e-06 ss: target_clients: [] use: False v_kernel: 1.0 var: 0.1 init_cand_num: 16 larger_better: False metric: client_summarized_weighted_avg.val_loss num_workers: 0 pbt: max_stage: 5 perf_threshold: 0.1 pfedhpo: discrete: False ss: target_fl_total_round: 1000 train_anchor: False train_fl: False use: False scheduler: rs sha: budgets: [] elim_rate: 3 iter: 0 ss: table: eps: 0.1 idx: 0 num: 27 trial_index: 0 working_folder: hpo model: cheb_order: 2 contrast_temp: 1.0 contrast_topk: 100 downstream_tasks: [] dropout: 0.1 embed_dim: 10 embed_size: 8 gamma: 0 graph_pooling: mean hidden: 256 horizon: 12 in_channels: 0 input_dim: 1 input_shape: () label_smoothing: 0.1 lambda_: 0.1 layer: 2 length_penalty: 2.0 max_answer_len: 30 max_length: 200 max_tree_depth: 3 min_length: 1 model_num_per_trainer: 1 model_type: google/bert_uncased_L-2_H-128_A-2 n_best_size: 20 no_repeat_ngram_size: 3 null_score_diff_threshold: 0.0 num_beams: 5 num_item: 0 num_labels: 1 num_layers: 1 num_nodes: 35 num_of_trees: 10 num_user: 0 out_channels: 1 output_dim: 1 pretrain_tasks: [] rnn_units: 64 stage: task: TrafficFlowPrediction type: FedDGCN use_bias: True use_contrastive_loss: False use_day: True use_week: True nbafl: use: False outdir: exp/FedAvg_FedDGCN_on_trafficflow_lr0.003_lstep1/sub_exp_20241114094505 personalization: K: 5 beta: 1.0 epoch_feature: 1 epoch_linear: 2 local_param: [] local_update_steps: 1 lr: 0.003 lr_feature: 0.1 lr_linear: 0.1 regular_weight: 0.1 share_non_trainable_para: False weight_decay: 0.0 print_decimal_digits: 6 quantization: method: none nbits: 8 regularizer: mu: 0.0 type: seed: 10 sgdmf: use: False train: batch_or_epoch: epoch batch_size: 64 data_para_dids: [] early_stop: False early_stop_patience: 15 epochs: 300 grad_norm: True local_update_steps: 1 loss_func: mae lr_decay: False lr_decay_rate: 0.3 lr_decay_step: [5, 20, 40, 70] lr_init: 0.003 max_grad_norm: 5 optimizer: lr: 0.003 type: Adam weight_decay: 0.0 real_value: True scheduler: type: warmup_ratio: 0.0 seed: 10 weight_decay: 0 trainer: disp_freq: 50 local_entropy: alpha: 0.75 eps: 0.0001 gamma: 0.03 inc_factor: 1.0 log_dir: ./ sam: adaptive: False eta: 0.0 rho: 1.0 type: trafficflowtrainer val_freq: 100000000 use_gpu: True verbose: 1 vertical: use: False wandb: use: False 2024-11-14 09:45:49,269 (utils:147) INFO: The device information file is not provided 2024-11-14 09:45:49,324 (fed_runner:173) INFO: Server has been set up ... 2024-11-14 09:45:49,350 (fed_runner:225) INFO: Client 1 has been set up ... 2024-11-14 09:45:49,378 (fed_runner:225) INFO: Client 2 has been set up ... 2024-11-14 09:45:49,396 (fed_runner:225) INFO: Client 3 has been set up ... 2024-11-14 09:45:49,415 (fed_runner:225) INFO: Client 4 has been set up ... 2024-11-14 09:45:49,432 (fed_runner:225) INFO: Client 5 has been set up ... 2024-11-14 09:45:49,454 (fed_runner:225) INFO: Client 6 has been set up ... 2024-11-14 09:45:49,474 (fed_runner:225) INFO: Client 7 has been set up ... 2024-11-14 09:45:49,493 (fed_runner:225) INFO: Client 8 has been set up ... 2024-11-14 09:45:49,511 (fed_runner:225) INFO: Client 9 has been set up ... 2024-11-14 09:45:49,530 (fed_runner:225) INFO: Client 10 has been set up ... 2024-11-14 09:45:49,531 (trainer:345) INFO: Model meta-info: . 2024-11-14 09:45:49,533 (trainer:353) INFO: Num of original para names: 50. 2024-11-14 09:45:49,533 (trainer:354) INFO: Num of original trainable para names: 50. 2024-11-14 09:45:49,533 (trainer:356) INFO: Num of preserved para names in local update: 50. Preserved para names in local update: {'encoder1.DGCRM_cells.0.update.fc.fc2.bias', 'encoder1.DGCRM_cells.0.update.fc.fc1.weight', 'D_i_W_emb', 'encoder2.DGCRM_cells.0.gate.bias_pool', 'encoder2.DGCRM_cells.0.gate.fc.fc3.weight', 'encoder1.DGCRM_cells.0.update.weights', 'encoder1.DGCRM_cells.0.gate.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc2.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc2.weight', 'end_conv1.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc2.weight', 'end_conv2.bias', 'encoder1.DGCRM_cells.0.gate.weights', 'encoder1.DGCRM_cells.0.update.fc.fc3.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc3.bias', 'end_conv1.bias', 'encoder1.DGCRM_cells.0.update.fc.fc3.weight', 'encoder1.DGCRM_cells.0.update.weights_pool', 'encoder2.DGCRM_cells.0.update.fc.fc2.bias', 'encoder2.DGCRM_cells.0.update.fc.fc2.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder1.DGCRM_cells.0.gate.weights_pool', 'encoder2.DGCRM_cells.0.update.weights_pool', 'encoder1.DGCRM_cells.0.gate.bias_pool', 'end_conv3.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder2.DGCRM_cells.0.update.fc.fc1.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder2.DGCRM_cells.0.update.fc.fc3.bias', 'encoder2.DGCRM_cells.0.update.fc.fc3.weight', 'encoder2.DGCRM_cells.0.update.weights', 'encoder2.DGCRM_cells.0.gate.bias', 'encoder2.DGCRM_cells.0.update.fc.fc1.weight', 'encoder1.DGCRM_cells.0.update.fc.fc2.weight', 'encoder2.DGCRM_cells.0.update.bias_pool', 'end_conv2.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc2.bias', 'node_embeddings1', 'encoder2.DGCRM_cells.0.update.bias', 'encoder1.DGCRM_cells.0.update.bias_pool', 'node_embeddings2', 'encoder1.DGCRM_cells.0.update.fc.fc1.bias', 'T_i_D_emb', 'encoder2.DGCRM_cells.0.gate.weights_pool', 'encoder1.DGCRM_cells.0.gate.fc.fc3.weight', 'encoder2.DGCRM_cells.0.gate.weights', 'encoder1.DGCRM_cells.0.gate.fc.fc3.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder1.DGCRM_cells.0.update.bias', 'end_conv3.bias'}. 2024-11-14 09:45:49,533 (trainer:360) INFO: Num of filtered para names in local update: 0. Filtered para names in local update: set(). 2024-11-14 09:45:49,534 (trainer:365) INFO: After register default hooks, the hooks_in_train is: { "on_fit_start": [ "_hook_on_data_parallel_init", "_hook_on_fit_start_init", "_hook_on_fit_start_calculate_model_size" ], "on_epoch_start": [ "_hook_on_epoch_start" ], "on_batch_start": [ "_hook_on_batch_start_init" ], "on_batch_forward": [ "_hook_on_batch_forward", "_hook_on_batch_forward_regularizer", "_hook_on_batch_forward_flop_count" ], "on_batch_backward": [ "_hook_on_batch_backward" ], "on_batch_end": [ "_hook_on_batch_end" ], "on_fit_end": [ "_hook_on_fit_end" ] }; the hooks_in_eval is: t{ "on_fit_start": [ "_hook_on_data_parallel_init", "_hook_on_fit_start_init" ], "on_epoch_start": [ "_hook_on_epoch_start" ], "on_batch_start": [ "_hook_on_batch_start_init" ], "on_batch_forward": [ "_hook_on_batch_forward" ], "on_batch_end": [ "_hook_on_batch_end" ], "on_fit_end": [ "_hook_on_fit_end" ] } 2024-11-14 09:45:49,547 (server:843) INFO: ----------- Starting training (Round #0) ------------- 2024-11-14 09:46:42,655 (client:354) INFO: {'Role': 'Client #9', 'Round': 0, 'Results_raw': {'train_loss': 47.048076, 'val_loss': 30.776891, 'test_loss': 29.725464}} 2024-11-14 09:47:33,384 (client:354) INFO: {'Role': 'Client #3', 'Round': 0, 'Results_raw': {'train_loss': 29.81839, 'val_loss': 19.133921, 'test_loss': 20.868182}} 2024-11-14 09:48:23,511 (client:354) INFO: {'Role': 'Client #6', 'Round': 0, 'Results_raw': {'train_loss': 41.850742, 'val_loss': 26.13304, 'test_loss': 26.270759}} 2024-11-14 09:49:14,280 (client:354) INFO: {'Role': 'Client #7', 'Round': 0, 'Results_raw': {'train_loss': 41.925825, 'val_loss': 27.561376, 'test_loss': 27.166113}} 2024-11-14 09:50:04,813 (client:354) INFO: {'Role': 'Client #4', 'Round': 0, 'Results_raw': {'train_loss': 38.554958, 'val_loss': 25.054342, 'test_loss': 25.409314}} 2024-11-14 09:50:58,503 (client:354) INFO: {'Role': 'Client #2', 'Round': 0, 'Results_raw': {'train_loss': 26.315925, 'val_loss': 14.992464, 'test_loss': 15.508496}} 2024-11-14 09:51:50,597 (client:354) INFO: {'Role': 'Client #1', 'Round': 0, 'Results_raw': {'train_loss': 33.268993, 'val_loss': 19.083141, 'test_loss': 20.284235}} 2024-11-14 09:52:42,475 (client:354) INFO: {'Role': 'Client #8', 'Round': 0, 'Results_raw': {'train_loss': 36.65536, 'val_loss': 23.019589, 'test_loss': 23.370984}} 2024-11-14 09:53:34,097 (client:354) INFO: {'Role': 'Client #5', 'Round': 0, 'Results_raw': {'train_loss': 42.512421, 'val_loss': 28.811068, 'test_loss': 34.83749}} 2024-11-14 09:54:26,450 (client:354) INFO: {'Role': 'Client #10', 'Round': 0, 'Results_raw': {'train_loss': 41.114738, 'val_loss': 26.142369, 'test_loss': 26.878353}} 2024-11-14 09:54:26,490 (server:353) INFO: Server: Starting evaluation at the end of round 0. 2024-11-14 09:54:26,491 (server:359) INFO: ----------- Starting a new training round (Round #1) ------------- 2024-11-14 09:56:48,231 (client:354) INFO: {'Role': 'Client #6', 'Round': 1, 'Results_raw': {'train_loss': 29.771579, 'val_loss': 25.280736, 'test_loss': 25.923534}} 2024-11-14 09:57:40,274 (client:354) INFO: {'Role': 'Client #4', 'Round': 1, 'Results_raw': {'train_loss': 28.814212, 'val_loss': 24.34533, 'test_loss': 24.545857}} 2024-11-14 09:58:32,073 (client:354) INFO: {'Role': 'Client #5', 'Round': 1, 'Results_raw': {'train_loss': 30.404432, 'val_loss': 28.349826, 'test_loss': 33.820134}} 2024-11-14 09:59:23,865 (client:354) INFO: {'Role': 'Client #8', 'Round': 1, 'Results_raw': {'train_loss': 25.713386, 'val_loss': 22.563393, 'test_loss': 23.329809}} 2024-11-14 10:00:14,906 (client:354) INFO: {'Role': 'Client #7', 'Round': 1, 'Results_raw': {'train_loss': 29.967974, 'val_loss': 25.941509, 'test_loss': 25.983261}} 2024-11-14 10:01:06,171 (client:354) INFO: {'Role': 'Client #9', 'Round': 1, 'Results_raw': {'train_loss': 35.778252, 'val_loss': 30.457825, 'test_loss': 29.754844}} 2024-11-14 10:01:57,658 (client:354) INFO: {'Role': 'Client #10', 'Round': 1, 'Results_raw': {'train_loss': 29.091352, 'val_loss': 25.301776, 'test_loss': 26.136816}} 2024-11-14 10:02:48,919 (client:354) INFO: {'Role': 'Client #3', 'Round': 1, 'Results_raw': {'train_loss': 21.050349, 'val_loss': 18.6232, 'test_loss': 20.351812}} 2024-11-14 10:03:40,231 (client:354) INFO: {'Role': 'Client #2', 'Round': 1, 'Results_raw': {'train_loss': 17.591107, 'val_loss': 14.51501, 'test_loss': 14.862172}} 2024-11-14 10:04:31,577 (client:354) INFO: {'Role': 'Client #1', 'Round': 1, 'Results_raw': {'train_loss': 22.518266, 'val_loss': 18.638318, 'test_loss': 20.106169}} 2024-11-14 10:04:31,590 (server:615) INFO: {'Role': 'Server #', 'Round': 0, 'Results_weighted_avg': {'test_avg_loss': np.float64(44.909949), 'test_loss': np.float64(232813.176709), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(45.71027), 'val_loss': np.float64(236962.039844), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(44.909949), 'test_loss': np.float64(232813.176709), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(45.71027), 'val_loss': np.float64(236962.039844), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(6.89224), 'test_avg_loss_bottom_decile': np.float64(38.8195), 'test_avg_loss_top_decile': np.float64(56.793405), 'test_avg_loss_min': np.float64(30.10601), 'test_avg_loss_max': np.float64(56.793405), 'test_avg_loss_bottom10%': np.float64(30.10601), 'test_avg_loss_top10%': np.float64(56.793405), 'test_avg_loss_cos1': np.float64(0.988428), 'test_avg_loss_entropy': np.float64(2.290334), 'test_loss_std': np.float64(35729.370987), 'test_loss_bottom_decile': np.float64(201240.28833), 'test_loss_top_decile': np.float64(294417.012695), 'test_loss_min': np.float64(156069.553711), 'test_loss_max': np.float64(294417.012695), 'test_loss_bottom10%': np.float64(156069.553711), 'test_loss_top10%': np.float64(294417.012695), 'test_loss_cos1': np.float64(0.988428), 'test_loss_entropy': np.float64(2.290334), 'val_avg_loss_std': np.float64(6.972662), 'val_avg_loss_bottom_decile': np.float64(39.153918), 'val_avg_loss_top_decile': np.float64(54.36577), 'val_avg_loss_min': np.float64(30.512071), 'val_avg_loss_max': np.float64(54.36577), 'val_avg_loss_bottom10%': np.float64(30.512071), 'val_avg_loss_top10%': np.float64(54.36577), 'val_avg_loss_cos1': np.float64(0.988565), 'val_avg_loss_entropy': np.float64(2.290285), 'val_loss_std': np.float64(36146.281782), 'val_loss_bottom_decile': np.float64(202973.908569), 'val_loss_top_decile': np.float64(281832.150513), 'val_loss_min': np.float64(158174.575928), 'val_loss_max': np.float64(281832.150513), 'val_loss_bottom10%': np.float64(158174.575928), 'val_loss_top10%': np.float64(281832.150513), 'val_loss_cos1': np.float64(0.988565), 'val_loss_entropy': np.float64(2.290285)}} 2024-11-14 10:04:31,631 (server:353) INFO: Server: Starting evaluation at the end of round 1. 2024-11-14 10:04:31,631 (server:359) INFO: ----------- Starting a new training round (Round #2) ------------- 2024-11-14 10:06:52,947 (client:354) INFO: {'Role': 'Client #2', 'Round': 2, 'Results_raw': {'train_loss': 16.4626, 'val_loss': 14.200097, 'test_loss': 14.78757}} 2024-11-14 10:07:44,210 (client:354) INFO: {'Role': 'Client #7', 'Round': 2, 'Results_raw': {'train_loss': 27.928374, 'val_loss': 25.575669, 'test_loss': 25.852796}} 2024-11-14 10:08:35,616 (client:354) INFO: {'Role': 'Client #3', 'Round': 2, 'Results_raw': {'train_loss': 19.901395, 'val_loss': 18.290422, 'test_loss': 20.214856}} 2024-11-14 10:09:26,732 (client:354) INFO: {'Role': 'Client #6', 'Round': 2, 'Results_raw': {'train_loss': 27.993927, 'val_loss': 24.092971, 'test_loss': 24.383045}} 2024-11-14 10:10:17,958 (client:354) INFO: {'Role': 'Client #8', 'Round': 2, 'Results_raw': {'train_loss': 24.230397, 'val_loss': 22.106846, 'test_loss': 22.55689}} 2024-11-14 10:11:09,453 (client:354) INFO: {'Role': 'Client #9', 'Round': 2, 'Results_raw': {'train_loss': 34.070884, 'val_loss': 29.517356, 'test_loss': 28.632467}} 2024-11-14 10:12:01,877 (client:354) INFO: {'Role': 'Client #10', 'Round': 2, 'Results_raw': {'train_loss': 26.916797, 'val_loss': 24.690432, 'test_loss': 26.093474}} 2024-11-14 10:12:54,796 (client:354) INFO: {'Role': 'Client #1', 'Round': 2, 'Results_raw': {'train_loss': 20.868996, 'val_loss': 17.96104, 'test_loss': 19.311412}} 2024-11-14 10:13:47,151 (client:354) INFO: {'Role': 'Client #4', 'Round': 2, 'Results_raw': {'train_loss': 27.344057, 'val_loss': 23.599412, 'test_loss': 24.109979}} 2024-11-14 10:14:39,704 (client:354) INFO: {'Role': 'Client #5', 'Round': 2, 'Results_raw': {'train_loss': 28.662844, 'val_loss': 27.334489, 'test_loss': 33.256109}} 2024-11-14 10:14:39,707 (server:615) INFO: {'Role': 'Server #', 'Round': 1, 'Results_weighted_avg': {'test_avg_loss': np.float64(34.566525), 'test_loss': np.float64(179192.867249), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(35.10294), 'val_loss': np.float64(181973.641614), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(34.566525), 'test_loss': np.float64(179192.867249), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(35.10294), 'val_loss': np.float64(181973.641614), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.840588), 'test_avg_loss_bottom_decile': np.float64(30.049597), 'test_avg_loss_top_decile': np.float64(44.299374), 'test_avg_loss_min': np.float64(25.488141), 'test_avg_loss_max': np.float64(44.299374), 'test_avg_loss_bottom10%': np.float64(25.488141), 'test_avg_loss_top10%': np.float64(44.299374), 'test_avg_loss_cos1': np.float64(0.990337), 'test_avg_loss_entropy': np.float64(2.292727), 'test_loss_std': np.float64(25093.606671), 'test_loss_bottom_decile': np.float64(155777.113342), 'test_loss_top_decile': np.float64(229647.956543), 'test_loss_min': np.float64(132130.525452), 'test_loss_max': np.float64(229647.956543), 'test_loss_bottom10%': np.float64(132130.525452), 'test_loss_top10%': np.float64(229647.956543), 'test_loss_cos1': np.float64(0.990337), 'test_loss_entropy': np.float64(2.292727), 'val_avg_loss_std': np.float64(4.895108), 'val_avg_loss_bottom_decile': np.float64(30.011301), 'val_avg_loss_top_decile': np.float64(41.926769), 'val_avg_loss_min': np.float64(25.710445), 'val_avg_loss_max': np.float64(41.926769), 'val_avg_loss_bottom10%': np.float64(25.710445), 'val_avg_loss_top10%': np.float64(41.926769), 'val_avg_loss_cos1': np.float64(0.990416), 'val_avg_loss_entropy': np.float64(2.292573), 'val_loss_std': np.float64(25376.241642), 'val_loss_bottom_decile': np.float64(155578.582581), 'val_loss_top_decile': np.float64(217348.371582), 'val_loss_min': np.float64(133282.945801), 'val_loss_max': np.float64(217348.371582), 'val_loss_bottom10%': np.float64(133282.945801), 'val_loss_top10%': np.float64(217348.371582), 'val_loss_cos1': np.float64(0.990416), 'val_loss_entropy': np.float64(2.292573)}} 2024-11-14 10:14:39,746 (server:353) INFO: Server: Starting evaluation at the end of round 2. 2024-11-14 10:14:39,747 (server:359) INFO: ----------- Starting a new training round (Round #3) ------------- 2024-11-14 10:17:01,917 (client:354) INFO: {'Role': 'Client #1', 'Round': 3, 'Results_raw': {'train_loss': 20.396912, 'val_loss': 17.654421, 'test_loss': 19.110498}} 2024-11-14 10:17:53,946 (client:354) INFO: {'Role': 'Client #8', 'Round': 3, 'Results_raw': {'train_loss': 23.661684, 'val_loss': 21.756279, 'test_loss': 22.358063}} 2024-11-14 10:18:47,288 (client:354) INFO: {'Role': 'Client #6', 'Round': 3, 'Results_raw': {'train_loss': 27.092403, 'val_loss': 23.776661, 'test_loss': 24.587749}} 2024-11-14 10:19:37,657 (client:354) INFO: {'Role': 'Client #3', 'Round': 3, 'Results_raw': {'train_loss': 19.360903, 'val_loss': 18.029252, 'test_loss': 19.799435}} 2024-11-14 10:20:28,199 (client:354) INFO: {'Role': 'Client #4', 'Round': 3, 'Results_raw': {'train_loss': 26.597904, 'val_loss': 23.242315, 'test_loss': 23.939616}} 2024-11-14 10:21:19,271 (client:354) INFO: {'Role': 'Client #10', 'Round': 3, 'Results_raw': {'train_loss': 26.177959, 'val_loss': 24.359628, 'test_loss': 25.213662}} 2024-11-14 10:22:10,165 (client:354) INFO: {'Role': 'Client #7', 'Round': 3, 'Results_raw': {'train_loss': 26.969987, 'val_loss': 24.614788, 'test_loss': 24.52141}} 2024-11-14 10:23:01,577 (client:354) INFO: {'Role': 'Client #5', 'Round': 3, 'Results_raw': {'train_loss': 27.762354, 'val_loss': 26.875279, 'test_loss': 32.433389}} 2024-11-14 10:23:52,579 (client:354) INFO: {'Role': 'Client #9', 'Round': 3, 'Results_raw': {'train_loss': 33.368334, 'val_loss': 29.369764, 'test_loss': 28.103936}} 2024-11-14 10:24:43,682 (client:354) INFO: {'Role': 'Client #2', 'Round': 3, 'Results_raw': {'train_loss': 15.880156, 'val_loss': 13.76765, 'test_loss': 14.29548}} 2024-11-14 10:24:43,685 (server:615) INFO: {'Role': 'Server #', 'Round': 2, 'Results_weighted_avg': {'test_avg_loss': np.float64(32.703847), 'test_loss': np.float64(169536.742584), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(33.057607), 'val_loss': np.float64(171370.637274), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(32.703847), 'test_loss': np.float64(169536.742584), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(33.057607), 'val_loss': np.float64(171370.637274), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.77023), 'test_avg_loss_bottom_decile': np.float64(28.618763), 'test_avg_loss_top_decile': np.float64(42.803329), 'test_avg_loss_min': np.float64(23.697424), 'test_avg_loss_max': np.float64(42.803329), 'test_avg_loss_bottom10%': np.float64(23.697424), 'test_avg_loss_top10%': np.float64(42.803329), 'test_avg_loss_cos1': np.float64(0.989529), 'test_avg_loss_entropy': np.float64(2.291942), 'test_loss_std': np.float64(24728.871456), 'test_loss_bottom_decile': np.float64(148359.668762), 'test_loss_top_decile': np.float64(221892.456299), 'test_loss_min': np.float64(122847.447266), 'test_loss_max': np.float64(221892.456299), 'test_loss_bottom10%': np.float64(122847.447266), 'test_loss_top10%': np.float64(221892.456299), 'test_loss_cos1': np.float64(0.989529), 'test_loss_entropy': np.float64(2.291942), 'val_avg_loss_std': np.float64(4.666823), 'val_avg_loss_bottom_decile': np.float64(28.511198), 'val_avg_loss_top_decile': np.float64(39.996927), 'val_avg_loss_min': np.float64(23.941618), 'val_avg_loss_max': np.float64(39.996927), 'val_avg_loss_bottom10%': np.float64(23.941618), 'val_avg_loss_top10%': np.float64(39.996927), 'val_avg_loss_cos1': np.float64(0.990182), 'val_avg_loss_entropy': np.float64(2.292342), 'val_loss_std': np.float64(24192.808867), 'val_loss_bottom_decile': np.float64(147802.051331), 'val_loss_top_decile': np.float64(207344.07074), 'val_loss_min': np.float64(124113.345276), 'val_loss_max': np.float64(207344.07074), 'val_loss_bottom10%': np.float64(124113.345276), 'val_loss_top10%': np.float64(207344.07074), 'val_loss_cos1': np.float64(0.990182), 'val_loss_entropy': np.float64(2.292342)}} 2024-11-14 10:24:43,724 (server:353) INFO: Server: Starting evaluation at the end of round 3. 2024-11-14 10:24:43,725 (server:359) INFO: ----------- Starting a new training round (Round #4) ------------- 2024-11-14 10:27:05,211 (client:354) INFO: {'Role': 'Client #3', 'Round': 4, 'Results_raw': {'train_loss': 19.004204, 'val_loss': 18.150313, 'test_loss': 19.87292}} 2024-11-14 10:28:03,620 (client:354) INFO: {'Role': 'Client #1', 'Round': 4, 'Results_raw': {'train_loss': 19.997741, 'val_loss': 17.638652, 'test_loss': 19.43289}} 2024-11-14 10:29:09,474 (client:354) INFO: {'Role': 'Client #10', 'Round': 4, 'Results_raw': {'train_loss': 25.711437, 'val_loss': 23.874365, 'test_loss': 25.187117}} 2024-11-14 10:30:12,690 (client:354) INFO: {'Role': 'Client #6', 'Round': 4, 'Results_raw': {'train_loss': 26.56675, 'val_loss': 23.521416, 'test_loss': 24.028409}} 2024-11-14 10:31:16,043 (client:354) INFO: {'Role': 'Client #5', 'Round': 4, 'Results_raw': {'train_loss': 27.336638, 'val_loss': 26.581138, 'test_loss': 32.993878}} 2024-11-14 10:32:19,028 (client:354) INFO: {'Role': 'Client #2', 'Round': 4, 'Results_raw': {'train_loss': 15.494229, 'val_loss': 13.625166, 'test_loss': 14.312166}} 2024-11-14 10:33:22,723 (client:354) INFO: {'Role': 'Client #8', 'Round': 4, 'Results_raw': {'train_loss': 23.229225, 'val_loss': 21.335045, 'test_loss': 22.218691}} 2024-11-14 10:34:25,888 (client:354) INFO: {'Role': 'Client #9', 'Round': 4, 'Results_raw': {'train_loss': 32.948819, 'val_loss': 29.281855, 'test_loss': 28.685316}} 2024-11-14 10:35:29,119 (client:354) INFO: {'Role': 'Client #7', 'Round': 4, 'Results_raw': {'train_loss': 26.538374, 'val_loss': 24.532678, 'test_loss': 24.447729}} 2024-11-14 10:36:30,829 (client:354) INFO: {'Role': 'Client #4', 'Round': 4, 'Results_raw': {'train_loss': 26.002131, 'val_loss': 23.096023, 'test_loss': 23.679596}} 2024-11-14 10:36:30,833 (server:615) INFO: {'Role': 'Server #', 'Round': 3, 'Results_weighted_avg': {'test_avg_loss': np.float64(32.050417), 'test_loss': np.float64(166149.363092), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.330093), 'val_loss': np.float64(167599.200299), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(32.050417), 'test_loss': np.float64(166149.363092), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.330093), 'val_loss': np.float64(167599.200299), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.994945), 'test_avg_loss_bottom_decile': np.float64(28.13221), 'test_avg_loss_top_decile': np.float64(43.024097), 'test_avg_loss_min': np.float64(22.749701), 'test_avg_loss_max': np.float64(43.024097), 'test_avg_loss_bottom10%': np.float64(22.749701), 'test_avg_loss_top10%': np.float64(43.024097), 'test_avg_loss_cos1': np.float64(0.988073), 'test_avg_loss_entropy': np.float64(2.290509), 'test_loss_std': np.float64(25893.794573), 'test_loss_bottom_decile': np.float64(145837.37915), 'test_loss_top_decile': np.float64(223036.91864), 'test_loss_min': np.float64(117934.450806), 'test_loss_max': np.float64(223036.91864), 'test_loss_bottom10%': np.float64(117934.450806), 'test_loss_top10%': np.float64(223036.91864), 'test_loss_cos1': np.float64(0.988073), 'test_loss_entropy': np.float64(2.290509), 'val_avg_loss_std': np.float64(4.78782), 'val_avg_loss_bottom_decile': np.float64(27.899009), 'val_avg_loss_top_decile': np.float64(40.017765), 'val_avg_loss_min': np.float64(23.031451), 'val_avg_loss_max': np.float64(40.017765), 'val_avg_loss_bottom10%': np.float64(23.031451), 'val_avg_loss_top10%': np.float64(40.017765), 'val_avg_loss_cos1': np.float64(0.989212), 'val_avg_loss_entropy': np.float64(2.291345), 'val_loss_std': np.float64(24820.056462), 'val_loss_bottom_decile': np.float64(144628.463928), 'val_loss_top_decile': np.float64(207452.094788), 'val_loss_min': np.float64(119395.041565), 'val_loss_max': np.float64(207452.094788), 'val_loss_bottom10%': np.float64(119395.041565), 'val_loss_top10%': np.float64(207452.094788), 'val_loss_cos1': np.float64(0.989212), 'val_loss_entropy': np.float64(2.291345)}} 2024-11-14 10:36:30,885 (server:353) INFO: Server: Starting evaluation at the end of round 4. 2024-11-14 10:36:30,886 (server:359) INFO: ----------- Starting a new training round (Round #5) ------------- 2024-11-14 10:39:20,035 (client:354) INFO: {'Role': 'Client #2', 'Round': 5, 'Results_raw': {'train_loss': 15.26583, 'val_loss': 13.601448, 'test_loss': 14.078033}} 2024-11-14 10:40:23,872 (client:354) INFO: {'Role': 'Client #5', 'Round': 5, 'Results_raw': {'train_loss': 26.904588, 'val_loss': 26.642553, 'test_loss': 32.611594}} 2024-11-14 10:41:29,052 (client:354) INFO: {'Role': 'Client #10', 'Round': 5, 'Results_raw': {'train_loss': 25.326182, 'val_loss': 23.87153, 'test_loss': 25.18906}} 2024-11-14 10:42:31,700 (client:354) INFO: {'Role': 'Client #6', 'Round': 5, 'Results_raw': {'train_loss': 25.992041, 'val_loss': 23.300263, 'test_loss': 24.050849}} 2024-11-14 10:43:34,155 (client:354) INFO: {'Role': 'Client #4', 'Round': 5, 'Results_raw': {'train_loss': 25.778691, 'val_loss': 22.994719, 'test_loss': 23.678861}} 2024-11-14 10:44:35,956 (client:354) INFO: {'Role': 'Client #1', 'Round': 5, 'Results_raw': {'train_loss': 19.739834, 'val_loss': 17.549147, 'test_loss': 19.17783}} 2024-11-14 10:45:41,569 (client:354) INFO: {'Role': 'Client #9', 'Round': 5, 'Results_raw': {'train_loss': 32.621179, 'val_loss': 28.871698, 'test_loss': 28.202787}} 2024-11-14 10:46:46,382 (client:354) INFO: {'Role': 'Client #8', 'Round': 5, 'Results_raw': {'train_loss': 22.901381, 'val_loss': 21.259343, 'test_loss': 22.09944}} 2024-11-14 10:47:56,022 (client:354) INFO: {'Role': 'Client #7', 'Round': 5, 'Results_raw': {'train_loss': 26.093108, 'val_loss': 24.066292, 'test_loss': 24.173981}} 2024-11-14 10:49:00,399 (client:354) INFO: {'Role': 'Client #3', 'Round': 5, 'Results_raw': {'train_loss': 18.766907, 'val_loss': 17.747707, 'test_loss': 20.279205}} 2024-11-14 10:49:00,403 (server:615) INFO: {'Role': 'Server #', 'Round': 4, 'Results_weighted_avg': {'test_avg_loss': np.float64(32.432173), 'test_loss': np.float64(168128.383856), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.644241), 'val_loss': np.float64(169227.746393), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(32.432173), 'test_loss': np.float64(168128.383856), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.644241), 'val_loss': np.float64(169227.746393), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.072254), 'test_avg_loss_bottom_decile': np.float64(28.641943), 'test_avg_loss_top_decile': np.float64(43.941771), 'test_avg_loss_min': np.float64(23.118968), 'test_avg_loss_max': np.float64(43.941771), 'test_avg_loss_bottom10%': np.float64(23.118968), 'test_avg_loss_top10%': np.float64(43.941771), 'test_avg_loss_cos1': np.float64(0.98799), 'test_avg_loss_entropy': np.float64(2.290501), 'test_loss_std': np.float64(26294.562512), 'test_loss_bottom_decile': np.float64(148479.831543), 'test_loss_top_decile': np.float64(227794.139343), 'test_loss_min': np.float64(119848.730164), 'test_loss_max': np.float64(227794.139343), 'test_loss_bottom10%': np.float64(119848.730164), 'test_loss_top10%': np.float64(227794.139343), 'test_loss_cos1': np.float64(0.98799), 'test_loss_entropy': np.float64(2.290501), 'val_avg_loss_std': np.float64(4.776345), 'val_avg_loss_bottom_decile': np.float64(28.508847), 'val_avg_loss_top_decile': np.float64(40.760932), 'val_avg_loss_min': np.float64(23.359003), 'val_avg_loss_max': np.float64(40.760932), 'val_avg_loss_bottom10%': np.float64(23.359003), 'val_avg_loss_top10%': np.float64(40.760932), 'val_avg_loss_cos1': np.float64(0.989465), 'val_avg_loss_entropy': np.float64(2.291656), 'val_loss_std': np.float64(24760.571201), 'val_loss_bottom_decile': np.float64(147789.862), 'val_loss_top_decile': np.float64(211304.670532), 'val_loss_min': np.float64(121093.069519), 'val_loss_max': np.float64(211304.670532), 'val_loss_bottom10%': np.float64(121093.069519), 'val_loss_top10%': np.float64(211304.670532), 'val_loss_cos1': np.float64(0.989465), 'val_loss_entropy': np.float64(2.291656)}} 2024-11-14 10:49:00,442 (server:353) INFO: Server: Starting evaluation at the end of round 5. 2024-11-14 10:49:00,443 (server:359) INFO: ----------- Starting a new training round (Round #6) ------------- 2024-11-14 10:51:44,841 (client:354) INFO: {'Role': 'Client #6', 'Round': 6, 'Results_raw': {'train_loss': 25.752314, 'val_loss': 23.155177, 'test_loss': 24.76097}} 2024-11-14 10:52:50,414 (client:354) INFO: {'Role': 'Client #5', 'Round': 6, 'Results_raw': {'train_loss': 26.661998, 'val_loss': 26.405684, 'test_loss': 31.983776}} 2024-11-14 10:53:56,324 (client:354) INFO: {'Role': 'Client #9', 'Round': 6, 'Results_raw': {'train_loss': 32.401253, 'val_loss': 28.782616, 'test_loss': 27.757073}} 2024-11-14 10:55:05,277 (client:354) INFO: {'Role': 'Client #2', 'Round': 6, 'Results_raw': {'train_loss': 15.071992, 'val_loss': 13.432142, 'test_loss': 14.031835}} 2024-11-14 10:56:11,052 (client:354) INFO: {'Role': 'Client #4', 'Round': 6, 'Results_raw': {'train_loss': 25.501887, 'val_loss': 22.79076, 'test_loss': 23.948973}} 2024-11-14 10:57:16,601 (client:354) INFO: {'Role': 'Client #8', 'Round': 6, 'Results_raw': {'train_loss': 22.629502, 'val_loss': 20.920804, 'test_loss': 21.87807}} 2024-11-14 10:58:22,049 (client:354) INFO: {'Role': 'Client #7', 'Round': 6, 'Results_raw': {'train_loss': 25.722783, 'val_loss': 23.904961, 'test_loss': 23.794439}} 2024-11-14 10:59:28,773 (client:354) INFO: {'Role': 'Client #1', 'Round': 6, 'Results_raw': {'train_loss': 19.439498, 'val_loss': 17.487793, 'test_loss': 19.08819}} 2024-11-14 11:00:31,579 (client:354) INFO: {'Role': 'Client #3', 'Round': 6, 'Results_raw': {'train_loss': 18.622482, 'val_loss': 17.917246, 'test_loss': 20.087386}} 2024-11-14 11:01:36,387 (client:354) INFO: {'Role': 'Client #10', 'Round': 6, 'Results_raw': {'train_loss': 25.028908, 'val_loss': 23.461073, 'test_loss': 24.522151}} 2024-11-14 11:01:36,393 (server:615) INFO: {'Role': 'Server #', 'Round': 5, 'Results_weighted_avg': {'test_avg_loss': np.float64(32.304289), 'test_loss': np.float64(167465.432507), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.479688), 'val_loss': np.float64(168374.704822), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(32.304289), 'test_loss': np.float64(167465.432507), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.479688), 'val_loss': np.float64(168374.704822), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.093877), 'test_avg_loss_bottom_decile': np.float64(28.648534), 'test_avg_loss_top_decile': np.float64(43.805756), 'test_avg_loss_min': np.float64(22.788316), 'test_avg_loss_max': np.float64(43.805756), 'test_avg_loss_bottom10%': np.float64(22.788316), 'test_avg_loss_top10%': np.float64(43.805756), 'test_avg_loss_cos1': np.float64(0.987795), 'test_avg_loss_entropy': np.float64(2.290266), 'test_loss_std': np.float64(26406.660242), 'test_loss_bottom_decile': np.float64(148513.998901), 'test_loss_top_decile': np.float64(227089.040161), 'test_loss_min': np.float64(118134.6297), 'test_loss_max': np.float64(227089.040161), 'test_loss_bottom10%': np.float64(118134.6297), 'test_loss_top10%': np.float64(227089.040161), 'test_loss_cos1': np.float64(0.987795), 'test_loss_entropy': np.float64(2.290266), 'val_avg_loss_std': np.float64(4.752884), 'val_avg_loss_bottom_decile': np.float64(28.497755), 'val_avg_loss_top_decile': np.float64(40.494812), 'val_avg_loss_min': np.float64(23.068294), 'val_avg_loss_max': np.float64(40.494812), 'val_avg_loss_bottom10%': np.float64(23.068294), 'val_avg_loss_top10%': np.float64(40.494812), 'val_avg_loss_cos1': np.float64(0.989462), 'val_avg_loss_entropy': np.float64(2.291619), 'val_loss_std': np.float64(24638.9511), 'val_loss_bottom_decile': np.float64(147732.361633), 'val_loss_top_decile': np.float64(209925.104492), 'val_loss_min': np.float64(119586.037292), 'val_loss_max': np.float64(209925.104492), 'val_loss_bottom10%': np.float64(119586.037292), 'val_loss_top10%': np.float64(209925.104492), 'val_loss_cos1': np.float64(0.989462), 'val_loss_entropy': np.float64(2.291619)}} 2024-11-14 11:01:36,440 (server:353) INFO: Server: Starting evaluation at the end of round 6. 2024-11-14 11:01:36,441 (server:359) INFO: ----------- Starting a new training round (Round #7) ------------- 2024-11-14 11:04:22,841 (client:354) INFO: {'Role': 'Client #8', 'Round': 7, 'Results_raw': {'train_loss': 22.436661, 'val_loss': 20.93003, 'test_loss': 21.774937}} 2024-11-14 11:05:29,874 (client:354) INFO: {'Role': 'Client #4', 'Round': 7, 'Results_raw': {'train_loss': 25.191164, 'val_loss': 22.553322, 'test_loss': 23.986045}} 2024-11-14 11:06:33,703 (client:354) INFO: {'Role': 'Client #6', 'Round': 7, 'Results_raw': {'train_loss': 25.596282, 'val_loss': 22.908242, 'test_loss': 24.2405}} 2024-11-14 11:07:37,418 (client:354) INFO: {'Role': 'Client #10', 'Round': 7, 'Results_raw': {'train_loss': 24.652794, 'val_loss': 23.441089, 'test_loss': 24.868421}} 2024-11-14 11:08:42,498 (client:354) INFO: {'Role': 'Client #9', 'Round': 7, 'Results_raw': {'train_loss': 32.137125, 'val_loss': 28.762737, 'test_loss': 28.285737}} 2024-11-14 11:09:46,493 (client:354) INFO: {'Role': 'Client #7', 'Round': 7, 'Results_raw': {'train_loss': 25.427129, 'val_loss': 23.953037, 'test_loss': 23.811305}} 2024-11-14 11:10:49,445 (client:354) INFO: {'Role': 'Client #1', 'Round': 7, 'Results_raw': {'train_loss': 19.277826, 'val_loss': 17.311034, 'test_loss': 19.1638}} 2024-11-14 11:11:53,370 (client:354) INFO: {'Role': 'Client #3', 'Round': 7, 'Results_raw': {'train_loss': 18.460118, 'val_loss': 17.521771, 'test_loss': 19.847126}} 2024-11-14 11:12:58,854 (client:354) INFO: {'Role': 'Client #5', 'Round': 7, 'Results_raw': {'train_loss': 26.350973, 'val_loss': 26.028137, 'test_loss': 31.929382}} 2024-11-14 11:14:05,691 (client:354) INFO: {'Role': 'Client #2', 'Round': 7, 'Results_raw': {'train_loss': 14.894432, 'val_loss': 13.30056, 'test_loss': 13.954745}} 2024-11-14 11:14:05,696 (server:615) INFO: {'Role': 'Server #', 'Round': 6, 'Results_weighted_avg': {'test_avg_loss': np.float64(32.324454), 'test_loss': np.float64(167569.966974), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.454521), 'val_loss': np.float64(168244.235089), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(32.324454), 'test_loss': np.float64(167569.966974), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.454521), 'val_loss': np.float64(168244.235089), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.080043), 'test_avg_loss_bottom_decile': np.float64(28.732051), 'test_avg_loss_top_decile': np.float64(43.956714), 'test_avg_loss_min': np.float64(22.985793), 'test_avg_loss_max': np.float64(43.956714), 'test_avg_loss_bottom10%': np.float64(22.985793), 'test_avg_loss_top10%': np.float64(43.956714), 'test_avg_loss_cos1': np.float64(0.987875), 'test_avg_loss_entropy': np.float64(2.290398), 'test_loss_std': np.float64(26334.943928), 'test_loss_bottom_decile': np.float64(148946.950256), 'test_loss_top_decile': np.float64(227871.607239), 'test_loss_min': np.float64(119158.352234), 'test_loss_max': np.float64(227871.607239), 'test_loss_bottom10%': np.float64(119158.352234), 'test_loss_top10%': np.float64(227871.607239), 'test_loss_cos1': np.float64(0.987875), 'test_loss_entropy': np.float64(2.290398), 'val_avg_loss_std': np.float64(4.698711), 'val_avg_loss_bottom_decile': np.float64(28.570356), 'val_avg_loss_top_decile': np.float64(40.582629), 'val_avg_loss_min': np.float64(23.23932), 'val_avg_loss_max': np.float64(40.582629), 'val_avg_loss_bottom10%': np.float64(23.23932), 'val_avg_loss_top10%': np.float64(40.582629), 'val_avg_loss_cos1': np.float64(0.989682), 'val_avg_loss_entropy': np.float64(2.291884), 'val_loss_std': np.float64(24358.117994), 'val_loss_bottom_decile': np.float64(148108.727661), 'val_loss_top_decile': np.float64(210380.346497), 'val_loss_min': np.float64(120472.63678), 'val_loss_max': np.float64(210380.346497), 'val_loss_bottom10%': np.float64(120472.63678), 'val_loss_top10%': np.float64(210380.346497), 'val_loss_cos1': np.float64(0.989682), 'val_loss_entropy': np.float64(2.291884)}} 2024-11-14 11:14:05,741 (server:353) INFO: Server: Starting evaluation at the end of round 7. 2024-11-14 11:14:05,742 (server:359) INFO: ----------- Starting a new training round (Round #8) ------------- 2024-11-14 11:16:55,885 (client:354) INFO: {'Role': 'Client #6', 'Round': 8, 'Results_raw': {'train_loss': 25.280609, 'val_loss': 22.728309, 'test_loss': 23.656797}} 2024-11-14 11:18:02,330 (client:354) INFO: {'Role': 'Client #7', 'Round': 8, 'Results_raw': {'train_loss': 25.272615, 'val_loss': 23.715636, 'test_loss': 24.009617}} 2024-11-14 11:19:07,843 (client:354) INFO: {'Role': 'Client #2', 'Round': 8, 'Results_raw': {'train_loss': 14.716266, 'val_loss': 13.399186, 'test_loss': 14.103247}} 2024-11-14 11:20:10,613 (client:354) INFO: {'Role': 'Client #4', 'Round': 8, 'Results_raw': {'train_loss': 25.107682, 'val_loss': 22.467048, 'test_loss': 23.632692}} 2024-11-14 11:21:12,378 (client:354) INFO: {'Role': 'Client #1', 'Round': 8, 'Results_raw': {'train_loss': 19.050009, 'val_loss': 17.003541, 'test_loss': 18.832139}} 2024-11-14 11:22:17,042 (client:354) INFO: {'Role': 'Client #8', 'Round': 8, 'Results_raw': {'train_loss': 22.266783, 'val_loss': 20.873432, 'test_loss': 21.628731}} 2024-11-14 11:23:20,199 (client:354) INFO: {'Role': 'Client #5', 'Round': 8, 'Results_raw': {'train_loss': 26.213403, 'val_loss': 25.978014, 'test_loss': 32.338112}} 2024-11-14 11:24:21,462 (client:354) INFO: {'Role': 'Client #3', 'Round': 8, 'Results_raw': {'train_loss': 18.323461, 'val_loss': 17.618609, 'test_loss': 20.003682}} 2024-11-14 11:25:25,818 (client:354) INFO: {'Role': 'Client #10', 'Round': 8, 'Results_raw': {'train_loss': 24.463825, 'val_loss': 23.037748, 'test_loss': 24.415144}} 2024-11-14 11:26:25,974 (client:354) INFO: {'Role': 'Client #9', 'Round': 8, 'Results_raw': {'train_loss': 31.994094, 'val_loss': 28.493361, 'test_loss': 27.834427}} 2024-11-14 11:26:25,979 (server:615) INFO: {'Role': 'Server #', 'Round': 7, 'Results_weighted_avg': {'test_avg_loss': np.float64(32.200139), 'test_loss': np.float64(166925.518384), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.268755), 'val_loss': np.float64(167281.228387), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(32.200139), 'test_loss': np.float64(166925.518384), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.268755), 'val_loss': np.float64(167281.228387), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.044689), 'test_avg_loss_bottom_decile': np.float64(28.414083), 'test_avg_loss_top_decile': np.float64(43.760232), 'test_avg_loss_min': np.float64(22.993952), 'test_avg_loss_max': np.float64(43.760232), 'test_avg_loss_bottom10%': np.float64(22.993952), 'test_avg_loss_top10%': np.float64(43.760232), 'test_avg_loss_cos1': np.float64(0.987949), 'test_avg_loss_entropy': np.float64(2.290484), 'test_loss_std': np.float64(26151.668983), 'test_loss_bottom_decile': np.float64(147298.606567), 'test_loss_top_decile': np.float64(226853.040527), 'test_loss_min': np.float64(119200.648071), 'test_loss_max': np.float64(226853.040527), 'test_loss_bottom10%': np.float64(119200.648071), 'test_loss_top10%': np.float64(226853.040527), 'test_loss_cos1': np.float64(0.987949), 'test_loss_entropy': np.float64(2.290484), 'val_avg_loss_std': np.float64(4.599995), 'val_avg_loss_bottom_decile': np.float64(28.272665), 'val_avg_loss_top_decile': np.float64(40.072471), 'val_avg_loss_min': np.float64(23.23154), 'val_avg_loss_max': np.float64(40.072471), 'val_avg_loss_bottom10%': np.float64(23.23154), 'val_avg_loss_top10%': np.float64(40.072471), 'val_avg_loss_cos1': np.float64(0.989992), 'val_avg_loss_entropy': np.float64(2.292203), 'val_loss_std': np.float64(23846.376462), 'val_loss_bottom_decile': np.float64(146565.493347), 'val_loss_top_decile': np.float64(207735.688904), 'val_loss_min': np.float64(120432.305908), 'val_loss_max': np.float64(207735.688904), 'val_loss_bottom10%': np.float64(120432.305908), 'val_loss_top10%': np.float64(207735.688904), 'val_loss_cos1': np.float64(0.989992), 'val_loss_entropy': np.float64(2.292203)}} 2024-11-14 11:26:26,019 (server:353) INFO: Server: Starting evaluation at the end of round 8. 2024-11-14 11:26:26,019 (server:359) INFO: ----------- Starting a new training round (Round #9) ------------- 2024-11-14 11:28:57,853 (client:354) INFO: {'Role': 'Client #2', 'Round': 9, 'Results_raw': {'train_loss': 14.634655, 'val_loss': 13.107577, 'test_loss': 13.673207}} 2024-11-14 11:29:50,016 (client:354) INFO: {'Role': 'Client #9', 'Round': 9, 'Results_raw': {'train_loss': 31.869384, 'val_loss': 28.570713, 'test_loss': 28.203366}} 2024-11-14 11:30:43,199 (client:354) INFO: {'Role': 'Client #8', 'Round': 9, 'Results_raw': {'train_loss': 22.141081, 'val_loss': 20.874271, 'test_loss': 21.792532}} 2024-11-14 11:31:36,568 (client:354) INFO: {'Role': 'Client #5', 'Round': 9, 'Results_raw': {'train_loss': 25.977276, 'val_loss': 25.81578, 'test_loss': 32.133712}} 2024-11-14 11:32:29,713 (client:354) INFO: {'Role': 'Client #1', 'Round': 9, 'Results_raw': {'train_loss': 18.921861, 'val_loss': 17.050485, 'test_loss': 18.843571}} 2024-11-14 11:33:23,054 (client:354) INFO: {'Role': 'Client #4', 'Round': 9, 'Results_raw': {'train_loss': 24.934693, 'val_loss': 22.34777, 'test_loss': 23.74476}} 2024-11-14 11:34:14,515 (client:354) INFO: {'Role': 'Client #3', 'Round': 9, 'Results_raw': {'train_loss': 18.173243, 'val_loss': 17.329546, 'test_loss': 19.716075}} 2024-11-14 11:35:05,871 (client:354) INFO: {'Role': 'Client #6', 'Round': 9, 'Results_raw': {'train_loss': 25.132812, 'val_loss': 22.714579, 'test_loss': 23.81502}} 2024-11-14 11:35:56,860 (client:354) INFO: {'Role': 'Client #10', 'Round': 9, 'Results_raw': {'train_loss': 24.316963, 'val_loss': 23.016751, 'test_loss': 24.349449}} 2024-11-14 11:36:48,698 (client:354) INFO: {'Role': 'Client #7', 'Round': 9, 'Results_raw': {'train_loss': 25.193072, 'val_loss': 23.910189, 'test_loss': 24.089161}} 2024-11-14 11:36:48,701 (server:615) INFO: {'Role': 'Server #', 'Round': 8, 'Results_weighted_avg': {'test_avg_loss': np.float64(32.052759), 'test_loss': np.float64(166161.500928), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.134151), 'val_loss': np.float64(166583.43905), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(32.052759), 'test_loss': np.float64(166161.500928), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(32.134151), 'val_loss': np.float64(166583.43905), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.148362), 'test_avg_loss_bottom_decile': np.float64(28.042359), 'test_avg_loss_top_decile': np.float64(43.844957), 'test_avg_loss_min': np.float64(22.720511), 'test_avg_loss_max': np.float64(43.844957), 'test_avg_loss_bottom10%': np.float64(22.720511), 'test_avg_loss_top10%': np.float64(43.844957), 'test_avg_loss_cos1': np.float64(0.987345), 'test_avg_loss_entropy': np.float64(2.289871), 'test_loss_std': np.float64(26689.106321), 'test_loss_bottom_decile': np.float64(145371.590393), 'test_loss_top_decile': np.float64(227292.255005), 'test_loss_min': np.float64(117783.130554), 'test_loss_max': np.float64(227292.255005), 'test_loss_bottom10%': np.float64(117783.130554), 'test_loss_top10%': np.float64(227292.255005), 'test_loss_cos1': np.float64(0.987345), 'test_loss_entropy': np.float64(2.289871), 'val_avg_loss_std': np.float64(4.689956), 'val_avg_loss_bottom_decile': np.float64(27.902926), 'val_avg_loss_top_decile': np.float64(40.097969), 'val_avg_loss_min': np.float64(22.974203), 'val_avg_loss_max': np.float64(40.097969), 'val_avg_loss_bottom10%': np.float64(22.974203), 'val_avg_loss_top10%': np.float64(40.097969), 'val_avg_loss_cos1': np.float64(0.989517), 'val_avg_loss_entropy': np.float64(2.291696), 'val_loss_std': np.float64(24312.731961), 'val_loss_bottom_decile': np.float64(144648.768188), 'val_loss_top_decile': np.float64(207867.8703), 'val_loss_min': np.float64(119098.2677), 'val_loss_max': np.float64(207867.8703), 'val_loss_bottom10%': np.float64(119098.2677), 'val_loss_top10%': np.float64(207867.8703), 'val_loss_cos1': np.float64(0.989517), 'val_loss_entropy': np.float64(2.291696)}} 2024-11-14 11:36:48,742 (server:353) INFO: Server: Starting evaluation at the end of round 9. 2024-11-14 11:36:48,742 (server:359) INFO: ----------- Starting a new training round (Round #10) ------------- 2024-11-14 11:39:10,359 (client:354) INFO: {'Role': 'Client #6', 'Round': 10, 'Results_raw': {'train_loss': 24.972371, 'val_loss': 22.644875, 'test_loss': 24.216219}} 2024-11-14 11:40:02,659 (client:354) INFO: {'Role': 'Client #10', 'Round': 10, 'Results_raw': {'train_loss': 24.194632, 'val_loss': 22.869897, 'test_loss': 24.52722}} 2024-11-14 11:40:56,453 (client:354) INFO: {'Role': 'Client #4', 'Round': 10, 'Results_raw': {'train_loss': 24.782131, 'val_loss': 22.182587, 'test_loss': 23.357943}} 2024-11-14 11:41:50,608 (client:354) INFO: {'Role': 'Client #9', 'Round': 10, 'Results_raw': {'train_loss': 31.751497, 'val_loss': 28.522159, 'test_loss': 27.599016}} 2024-11-14 11:42:43,236 (client:354) INFO: {'Role': 'Client #3', 'Round': 10, 'Results_raw': {'train_loss': 18.108865, 'val_loss': 17.533722, 'test_loss': 19.703765}} 2024-11-14 11:43:34,995 (client:354) INFO: {'Role': 'Client #1', 'Round': 10, 'Results_raw': {'train_loss': 18.825699, 'val_loss': 16.820894, 'test_loss': 18.563698}} 2024-11-14 11:44:26,955 (client:354) INFO: {'Role': 'Client #5', 'Round': 10, 'Results_raw': {'train_loss': 25.902879, 'val_loss': 25.688991, 'test_loss': 31.87643}} 2024-11-14 11:45:18,656 (client:354) INFO: {'Role': 'Client #8', 'Round': 10, 'Results_raw': {'train_loss': 21.99179, 'val_loss': 20.595593, 'test_loss': 21.489027}} 2024-11-14 11:46:11,749 (client:354) INFO: {'Role': 'Client #2', 'Round': 10, 'Results_raw': {'train_loss': 14.498543, 'val_loss': 12.964915, 'test_loss': 13.468867}} 2024-11-14 11:47:03,567 (client:354) INFO: {'Role': 'Client #7', 'Round': 10, 'Results_raw': {'train_loss': 24.836693, 'val_loss': 23.386402, 'test_loss': 23.827438}} 2024-11-14 11:47:03,570 (server:615) INFO: {'Role': 'Server #', 'Round': 9, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.832676), 'test_loss': np.float64(165020.591901), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.861926), 'val_loss': np.float64(165172.22312), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.832676), 'test_loss': np.float64(165020.591901), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.861926), 'val_loss': np.float64(165172.22312), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.026136), 'test_avg_loss_bottom_decile': np.float64(27.802081), 'test_avg_loss_top_decile': np.float64(43.495237), 'test_avg_loss_min': np.float64(22.966792), 'test_avg_loss_max': np.float64(43.495237), 'test_avg_loss_bottom10%': np.float64(22.966792), 'test_avg_loss_top10%': np.float64(43.495237), 'test_avg_loss_cos1': np.float64(0.987763), 'test_avg_loss_entropy': np.float64(2.29036), 'test_loss_std': np.float64(26055.489351), 'test_loss_bottom_decile': np.float64(144125.989197), 'test_loss_top_decile': np.float64(225479.308777), 'test_loss_min': np.float64(119059.852173), 'test_loss_max': np.float64(225479.308777), 'test_loss_bottom10%': np.float64(119059.852173), 'test_loss_top10%': np.float64(225479.308777), 'test_loss_cos1': np.float64(0.987763), 'test_loss_entropy': np.float64(2.29036), 'val_avg_loss_std': np.float64(4.491747), 'val_avg_loss_bottom_decile': np.float64(27.632349), 'val_avg_loss_top_decile': np.float64(39.53216), 'val_avg_loss_min': np.float64(23.27512), 'val_avg_loss_max': np.float64(39.53216), 'val_avg_loss_bottom10%': np.float64(23.27512), 'val_avg_loss_top10%': np.float64(39.53216), 'val_avg_loss_cos1': np.float64(0.990209), 'val_avg_loss_entropy': np.float64(2.292467), 'val_loss_std': np.float64(23285.217376), 'val_loss_bottom_decile': np.float64(143246.097473), 'val_loss_top_decile': np.float64(204934.71814), 'val_loss_min': np.float64(120658.223511), 'val_loss_max': np.float64(204934.71814), 'val_loss_bottom10%': np.float64(120658.223511), 'val_loss_top10%': np.float64(204934.71814), 'val_loss_cos1': np.float64(0.990209), 'val_loss_entropy': np.float64(2.292467)}} 2024-11-14 11:47:03,602 (server:353) INFO: Server: Starting evaluation at the end of round 10. 2024-11-14 11:47:03,603 (server:359) INFO: ----------- Starting a new training round (Round #11) ------------- 2024-11-14 11:49:31,113 (client:354) INFO: {'Role': 'Client #4', 'Round': 11, 'Results_raw': {'train_loss': 24.66279, 'val_loss': 22.271135, 'test_loss': 23.688901}} 2024-11-14 11:50:24,713 (client:354) INFO: {'Role': 'Client #9', 'Round': 11, 'Results_raw': {'train_loss': 31.615214, 'val_loss': 28.514671, 'test_loss': 27.972824}} 2024-11-14 11:51:17,188 (client:354) INFO: {'Role': 'Client #7', 'Round': 11, 'Results_raw': {'train_loss': 24.828669, 'val_loss': 23.50767, 'test_loss': 23.433714}} 2024-11-14 11:52:10,154 (client:354) INFO: {'Role': 'Client #5', 'Round': 11, 'Results_raw': {'train_loss': 25.717854, 'val_loss': 25.992331, 'test_loss': 32.665554}} 2024-11-14 11:53:03,680 (client:354) INFO: {'Role': 'Client #8', 'Round': 11, 'Results_raw': {'train_loss': 21.882099, 'val_loss': 20.648881, 'test_loss': 21.620076}} 2024-11-14 11:53:56,887 (client:354) INFO: {'Role': 'Client #10', 'Round': 11, 'Results_raw': {'train_loss': 24.050552, 'val_loss': 22.818643, 'test_loss': 24.117353}} 2024-11-14 11:54:50,507 (client:354) INFO: {'Role': 'Client #1', 'Round': 11, 'Results_raw': {'train_loss': 18.721355, 'val_loss': 16.825489, 'test_loss': 18.564934}} 2024-11-14 11:55:43,006 (client:354) INFO: {'Role': 'Client #6', 'Round': 11, 'Results_raw': {'train_loss': 24.884291, 'val_loss': 22.642151, 'test_loss': 24.010717}} 2024-11-14 11:56:32,255 (client:354) INFO: {'Role': 'Client #3', 'Round': 11, 'Results_raw': {'train_loss': 18.009305, 'val_loss': 17.500453, 'test_loss': 19.823347}} 2024-11-14 11:57:22,321 (client:354) INFO: {'Role': 'Client #2', 'Round': 11, 'Results_raw': {'train_loss': 14.461568, 'val_loss': 13.106499, 'test_loss': 13.85926}} 2024-11-14 11:57:22,326 (server:615) INFO: {'Role': 'Server #', 'Round': 10, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.739283), 'test_loss': np.float64(164536.44162), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.769848), 'val_loss': np.float64(164694.893768), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.739283), 'test_loss': np.float64(164536.44162), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.769848), 'val_loss': np.float64(164694.893768), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.057787), 'test_avg_loss_bottom_decile': np.float64(27.409331), 'test_avg_loss_top_decile': np.float64(43.530875), 'test_avg_loss_min': np.float64(23.05574), 'test_avg_loss_max': np.float64(43.530875), 'test_avg_loss_bottom10%': np.float64(23.05574), 'test_avg_loss_top10%': np.float64(43.530875), 'test_avg_loss_cos1': np.float64(0.98754), 'test_avg_loss_entropy': np.float64(2.29017), 'test_loss_std': np.float64(26219.56535), 'test_loss_bottom_decile': np.float64(142089.970093), 'test_loss_top_decile': np.float64(225664.058044), 'test_loss_min': np.float64(119520.956482), 'test_loss_max': np.float64(225664.058044), 'test_loss_bottom10%': np.float64(119520.956482), 'test_loss_top10%': np.float64(225664.058044), 'test_loss_cos1': np.float64(0.98754), 'test_loss_entropy': np.float64(2.29017), 'val_avg_loss_std': np.float64(4.529234), 'val_avg_loss_bottom_decile': np.float64(27.196667), 'val_avg_loss_top_decile': np.float64(39.512941), 'val_avg_loss_min': np.float64(23.340377), 'val_avg_loss_max': np.float64(39.512941), 'val_avg_loss_bottom10%': np.float64(23.340377), 'val_avg_loss_top10%': np.float64(39.512941), 'val_avg_loss_cos1': np.float64(0.98999), 'val_avg_loss_entropy': np.float64(2.292261), 'val_loss_std': np.float64(23479.54761), 'val_loss_bottom_decile': np.float64(140987.521912), 'val_loss_top_decile': np.float64(204835.085876), 'val_loss_min': np.float64(120996.512024), 'val_loss_max': np.float64(204835.085876), 'val_loss_bottom10%': np.float64(120996.512024), 'val_loss_top10%': np.float64(204835.085876), 'val_loss_cos1': np.float64(0.98999), 'val_loss_entropy': np.float64(2.292261)}} 2024-11-14 11:57:22,370 (server:353) INFO: Server: Starting evaluation at the end of round 11. 2024-11-14 11:57:22,371 (server:359) INFO: ----------- Starting a new training round (Round #12) ------------- 2024-11-14 11:59:49,008 (client:354) INFO: {'Role': 'Client #9', 'Round': 12, 'Results_raw': {'train_loss': 31.492574, 'val_loss': 28.314774, 'test_loss': 27.436505}} 2024-11-14 12:00:47,572 (client:354) INFO: {'Role': 'Client #2', 'Round': 12, 'Results_raw': {'train_loss': 14.30261, 'val_loss': 12.995144, 'test_loss': 13.713522}} 2024-11-14 12:01:42,860 (client:354) INFO: {'Role': 'Client #10', 'Round': 12, 'Results_raw': {'train_loss': 23.927623, 'val_loss': 22.935861, 'test_loss': 24.429301}} 2024-11-14 12:02:39,680 (client:354) INFO: {'Role': 'Client #7', 'Round': 12, 'Results_raw': {'train_loss': 24.668492, 'val_loss': 23.277853, 'test_loss': 23.410713}} 2024-11-14 12:03:34,769 (client:354) INFO: {'Role': 'Client #3', 'Round': 12, 'Results_raw': {'train_loss': 17.916383, 'val_loss': 17.256459, 'test_loss': 19.615306}} 2024-11-14 12:04:37,237 (client:354) INFO: {'Role': 'Client #5', 'Round': 12, 'Results_raw': {'train_loss': 25.625709, 'val_loss': 25.685212, 'test_loss': 32.392381}} 2024-11-14 12:05:43,254 (client:354) INFO: {'Role': 'Client #1', 'Round': 12, 'Results_raw': {'train_loss': 18.660971, 'val_loss': 16.909823, 'test_loss': 18.79776}} 2024-11-14 12:06:43,825 (client:354) INFO: {'Role': 'Client #6', 'Round': 12, 'Results_raw': {'train_loss': 24.75032, 'val_loss': 22.288352, 'test_loss': 23.550833}} 2024-11-14 12:07:46,445 (client:354) INFO: {'Role': 'Client #4', 'Round': 12, 'Results_raw': {'train_loss': 24.559937, 'val_loss': 22.266836, 'test_loss': 23.565146}} 2024-11-14 12:08:50,616 (client:354) INFO: {'Role': 'Client #8', 'Round': 12, 'Results_raw': {'train_loss': 21.806798, 'val_loss': 20.42417, 'test_loss': 21.398976}} 2024-11-14 12:08:50,635 (server:615) INFO: {'Role': 'Server #', 'Round': 11, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.835074), 'test_loss': np.float64(165033.02265), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.829266), 'val_loss': np.float64(165002.913196), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.835074), 'test_loss': np.float64(165033.02265), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.829266), 'val_loss': np.float64(165002.913196), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.08367), 'test_avg_loss_bottom_decile': np.float64(27.426303), 'test_avg_loss_top_decile': np.float64(43.642123), 'test_avg_loss_min': np.float64(23.051589), 'test_avg_loss_max': np.float64(43.642123), 'test_avg_loss_bottom10%': np.float64(23.051589), 'test_avg_loss_top10%': np.float64(43.642123), 'test_avg_loss_cos1': np.float64(0.987489), 'test_avg_loss_entropy': np.float64(2.290102), 'test_loss_std': np.float64(26353.745971), 'test_loss_bottom_decile': np.float64(142177.956604), 'test_loss_top_decile': np.float64(226240.763062), 'test_loss_min': np.float64(119499.435608), 'test_loss_max': np.float64(226240.763062), 'test_loss_bottom10%': np.float64(119499.435608), 'test_loss_top10%': np.float64(226240.763062), 'test_loss_cos1': np.float64(0.987489), 'test_loss_entropy': np.float64(2.290102), 'val_avg_loss_std': np.float64(4.508296), 'val_avg_loss_bottom_decile': np.float64(27.215234), 'val_avg_loss_top_decile': np.float64(39.404062), 'val_avg_loss_min': np.float64(23.359819), 'val_avg_loss_max': np.float64(39.404062), 'val_avg_loss_bottom10%': np.float64(23.359819), 'val_avg_loss_top10%': np.float64(39.404062), 'val_avg_loss_cos1': np.float64(0.990118), 'val_avg_loss_entropy': np.float64(2.292376), 'val_loss_std': np.float64(23371.004778), 'val_loss_bottom_decile': np.float64(141083.771423), 'val_loss_top_decile': np.float64(204270.655762), 'val_loss_min': np.float64(121097.302246), 'val_loss_max': np.float64(204270.655762), 'val_loss_bottom10%': np.float64(121097.302246), 'val_loss_top10%': np.float64(204270.655762), 'val_loss_cos1': np.float64(0.990118), 'val_loss_entropy': np.float64(2.292376)}} 2024-11-14 12:08:50,678 (server:353) INFO: Server: Starting evaluation at the end of round 12. 2024-11-14 12:08:50,679 (server:359) INFO: ----------- Starting a new training round (Round #13) ------------- 2024-11-14 12:11:25,192 (client:354) INFO: {'Role': 'Client #10', 'Round': 13, 'Results_raw': {'train_loss': 23.822599, 'val_loss': 22.775241, 'test_loss': 24.061589}} 2024-11-14 12:12:23,538 (client:354) INFO: {'Role': 'Client #5', 'Round': 13, 'Results_raw': {'train_loss': 25.504007, 'val_loss': 25.529641, 'test_loss': 32.28657}} 2024-11-14 12:13:20,737 (client:354) INFO: {'Role': 'Client #7', 'Round': 13, 'Results_raw': {'train_loss': 24.667481, 'val_loss': 23.144783, 'test_loss': 23.539004}} 2024-11-14 12:14:16,147 (client:354) INFO: {'Role': 'Client #8', 'Round': 13, 'Results_raw': {'train_loss': 21.681625, 'val_loss': 20.702072, 'test_loss': 21.654264}} 2024-11-14 12:15:12,705 (client:354) INFO: {'Role': 'Client #1', 'Round': 13, 'Results_raw': {'train_loss': 18.540248, 'val_loss': 16.576722, 'test_loss': 18.471163}} 2024-11-14 12:16:07,614 (client:354) INFO: {'Role': 'Client #9', 'Round': 13, 'Results_raw': {'train_loss': 31.400799, 'val_loss': 28.119907, 'test_loss': 27.229383}} 2024-11-14 12:17:01,747 (client:354) INFO: {'Role': 'Client #6', 'Round': 13, 'Results_raw': {'train_loss': 24.696178, 'val_loss': 22.497929, 'test_loss': 23.458706}} 2024-11-14 12:17:56,144 (client:354) INFO: {'Role': 'Client #3', 'Round': 13, 'Results_raw': {'train_loss': 17.844823, 'val_loss': 17.272885, 'test_loss': 19.684774}} 2024-11-14 12:18:52,142 (client:354) INFO: {'Role': 'Client #2', 'Round': 13, 'Results_raw': {'train_loss': 14.213617, 'val_loss': 12.866188, 'test_loss': 13.548787}} 2024-11-14 12:19:46,840 (client:354) INFO: {'Role': 'Client #4', 'Round': 13, 'Results_raw': {'train_loss': 24.475385, 'val_loss': 22.123862, 'test_loss': 23.409956}} 2024-11-14 12:19:46,843 (server:615) INFO: {'Role': 'Server #', 'Round': 12, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.911308), 'test_loss': np.float64(165428.221838), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.902521), 'val_loss': np.float64(165382.669446), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.911308), 'test_loss': np.float64(165428.221838), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.902521), 'val_loss': np.float64(165382.669446), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.169985), 'test_avg_loss_bottom_decile': np.float64(27.280274), 'test_avg_loss_top_decile': np.float64(43.936603), 'test_avg_loss_min': np.float64(23.127775), 'test_avg_loss_max': np.float64(43.936603), 'test_avg_loss_bottom10%': np.float64(23.127775), 'test_avg_loss_top10%': np.float64(43.936603), 'test_avg_loss_cos1': np.float64(0.987129), 'test_avg_loss_entropy': np.float64(2.289759), 'test_loss_std': np.float64(26801.203308), 'test_loss_bottom_decile': np.float64(141420.93866), 'test_loss_top_decile': np.float64(227767.349182), 'test_loss_min': np.float64(119894.387329), 'test_loss_max': np.float64(227767.349182), 'test_loss_bottom10%': np.float64(119894.387329), 'test_loss_top10%': np.float64(227767.349182), 'test_loss_cos1': np.float64(0.987129), 'test_loss_entropy': np.float64(2.289759), 'val_avg_loss_std': np.float64(4.585131), 'val_avg_loss_bottom_decile': np.float64(27.031299), 'val_avg_loss_top_decile': np.float64(39.616022), 'val_avg_loss_min': np.float64(23.446259), 'val_avg_loss_max': np.float64(39.616022), 'val_avg_loss_bottom10%': np.float64(23.446259), 'val_avg_loss_top10%': np.float64(39.616022), 'val_avg_loss_cos1': np.float64(0.989829), 'val_avg_loss_entropy': np.float64(2.29209), 'val_loss_std': np.float64(23769.316782), 'val_loss_bottom_decile': np.float64(140130.252563), 'val_loss_top_decile': np.float64(205369.458557), 'val_loss_min': np.float64(121545.408569), 'val_loss_max': np.float64(205369.458557), 'val_loss_bottom10%': np.float64(121545.408569), 'val_loss_top10%': np.float64(205369.458557), 'val_loss_cos1': np.float64(0.989829), 'val_loss_entropy': np.float64(2.29209)}} 2024-11-14 12:19:46,879 (server:353) INFO: Server: Starting evaluation at the end of round 13. 2024-11-14 12:19:46,880 (server:359) INFO: ----------- Starting a new training round (Round #14) ------------- 2024-11-14 12:22:14,593 (client:354) INFO: {'Role': 'Client #9', 'Round': 14, 'Results_raw': {'train_loss': 31.337541, 'val_loss': 28.036312, 'test_loss': 27.038299}} 2024-11-14 12:23:09,512 (client:354) INFO: {'Role': 'Client #4', 'Round': 14, 'Results_raw': {'train_loss': 24.415516, 'val_loss': 22.224302, 'test_loss': 23.383497}} 2024-11-14 12:24:05,058 (client:354) INFO: {'Role': 'Client #8', 'Round': 14, 'Results_raw': {'train_loss': 21.628955, 'val_loss': 20.428833, 'test_loss': 21.627565}} 2024-11-14 12:25:00,570 (client:354) INFO: {'Role': 'Client #6', 'Round': 14, 'Results_raw': {'train_loss': 24.547663, 'val_loss': 22.301854, 'test_loss': 23.799474}} 2024-11-14 12:25:55,572 (client:354) INFO: {'Role': 'Client #7', 'Round': 14, 'Results_raw': {'train_loss': 24.483611, 'val_loss': 23.390907, 'test_loss': 23.895868}} 2024-11-14 12:26:50,552 (client:354) INFO: {'Role': 'Client #5', 'Round': 14, 'Results_raw': {'train_loss': 25.385592, 'val_loss': 25.62598, 'test_loss': 32.665189}} 2024-11-14 12:27:45,380 (client:354) INFO: {'Role': 'Client #3', 'Round': 14, 'Results_raw': {'train_loss': 17.739959, 'val_loss': 17.414622, 'test_loss': 19.839693}} 2024-11-14 12:28:39,943 (client:354) INFO: {'Role': 'Client #10', 'Round': 14, 'Results_raw': {'train_loss': 23.685169, 'val_loss': 22.687391, 'test_loss': 24.055551}} 2024-11-14 12:29:34,439 (client:354) INFO: {'Role': 'Client #1', 'Round': 14, 'Results_raw': {'train_loss': 18.494406, 'val_loss': 16.659681, 'test_loss': 18.691424}} 2024-11-14 12:30:28,541 (client:354) INFO: {'Role': 'Client #2', 'Round': 14, 'Results_raw': {'train_loss': 14.159208, 'val_loss': 12.88684, 'test_loss': 13.59707}} 2024-11-14 12:30:28,543 (server:615) INFO: {'Role': 'Server #', 'Round': 13, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.578822), 'test_loss': np.float64(163704.611285), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.544723), 'val_loss': np.float64(163527.846356), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.578822), 'test_loss': np.float64(163704.611285), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.544723), 'val_loss': np.float64(163527.846356), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.1642), 'test_avg_loss_bottom_decile': np.float64(26.850836), 'test_avg_loss_top_decile': np.float64(43.564504), 'test_avg_loss_min': np.float64(22.787944), 'test_avg_loss_max': np.float64(43.564504), 'test_avg_loss_bottom10%': np.float64(22.787944), 'test_avg_loss_top10%': np.float64(43.564504), 'test_avg_loss_cos1': np.float64(0.986891), 'test_avg_loss_entropy': np.float64(2.289508), 'test_loss_std': np.float64(26771.213017), 'test_loss_bottom_decile': np.float64(139194.734009), 'test_loss_top_decile': np.float64(225838.386414), 'test_loss_min': np.float64(118132.702209), 'test_loss_max': np.float64(225838.386414), 'test_loss_bottom10%': np.float64(118132.702209), 'test_loss_top10%': np.float64(225838.386414), 'test_loss_cos1': np.float64(0.986891), 'test_loss_entropy': np.float64(2.289508), 'val_avg_loss_std': np.float64(4.553707), 'val_avg_loss_bottom_decile': np.float64(26.601428), 'val_avg_loss_top_decile': np.float64(39.082237), 'val_avg_loss_min': np.float64(23.127642), 'val_avg_loss_max': np.float64(39.082237), 'val_avg_loss_bottom10%': np.float64(23.127642), 'val_avg_loss_top10%': np.float64(39.082237), 'val_avg_loss_cos1': np.float64(0.989741), 'val_avg_loss_entropy': np.float64(2.291982), 'val_loss_std': np.float64(23606.415025), 'val_loss_bottom_decile': np.float64(137901.80426), 'val_loss_top_decile': np.float64(202602.314392), 'val_loss_min': np.float64(119893.693848), 'val_loss_max': np.float64(202602.314392), 'val_loss_bottom10%': np.float64(119893.693848), 'val_loss_top10%': np.float64(202602.314392), 'val_loss_cos1': np.float64(0.989741), 'val_loss_entropy': np.float64(2.291982)}} 2024-11-14 12:30:28,572 (server:353) INFO: Server: Starting evaluation at the end of round 14. 2024-11-14 12:30:28,573 (server:359) INFO: ----------- Starting a new training round (Round #15) ------------- 2024-11-14 12:32:58,293 (client:354) INFO: {'Role': 'Client #7', 'Round': 15, 'Results_raw': {'train_loss': 24.322196, 'val_loss': 22.987756, 'test_loss': 23.331715}} 2024-11-14 12:33:54,712 (client:354) INFO: {'Role': 'Client #3', 'Round': 15, 'Results_raw': {'train_loss': 17.681283, 'val_loss': 17.229468, 'test_loss': 20.010664}} 2024-11-14 12:34:49,603 (client:354) INFO: {'Role': 'Client #4', 'Round': 15, 'Results_raw': {'train_loss': 24.294592, 'val_loss': 22.291001, 'test_loss': 23.41669}} 2024-11-14 12:35:44,348 (client:354) INFO: {'Role': 'Client #5', 'Round': 15, 'Results_raw': {'train_loss': 25.348884, 'val_loss': 25.691208, 'test_loss': 32.769855}} 2024-11-14 12:36:39,164 (client:354) INFO: {'Role': 'Client #9', 'Round': 15, 'Results_raw': {'train_loss': 31.256793, 'val_loss': 27.875757, 'test_loss': 26.94626}} 2024-11-14 12:37:36,397 (client:354) INFO: {'Role': 'Client #2', 'Round': 15, 'Results_raw': {'train_loss': 14.105518, 'val_loss': 12.858934, 'test_loss': 13.43882}} 2024-11-14 12:38:34,119 (client:354) INFO: {'Role': 'Client #6', 'Round': 15, 'Results_raw': {'train_loss': 24.453641, 'val_loss': 22.170765, 'test_loss': 23.575812}} 2024-11-14 12:39:39,814 (client:354) INFO: {'Role': 'Client #8', 'Round': 15, 'Results_raw': {'train_loss': 21.526627, 'val_loss': 20.53543, 'test_loss': 21.350791}} 2024-11-14 12:40:47,965 (client:354) INFO: {'Role': 'Client #10', 'Round': 15, 'Results_raw': {'train_loss': 23.547589, 'val_loss': 22.737592, 'test_loss': 24.121434}} 2024-11-14 12:41:56,745 (client:354) INFO: {'Role': 'Client #1', 'Round': 15, 'Results_raw': {'train_loss': 18.359216, 'val_loss': 16.708357, 'test_loss': 18.551451}} 2024-11-14 12:41:56,750 (server:615) INFO: {'Role': 'Server #', 'Round': 14, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.448396), 'test_loss': np.float64(163028.482971), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.354881), 'val_loss': np.float64(162543.70423), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.448396), 'test_loss': np.float64(163028.482971), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.354881), 'val_loss': np.float64(162543.70423), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.967403), 'test_avg_loss_bottom_decile': np.float64(26.849449), 'test_avg_loss_top_decile': np.float64(43.125359), 'test_avg_loss_min': np.float64(23.211806), 'test_avg_loss_max': np.float64(43.125359), 'test_avg_loss_bottom10%': np.float64(23.211806), 'test_avg_loss_top10%': np.float64(43.125359), 'test_avg_loss_cos1': np.float64(0.987754), 'test_avg_loss_entropy': np.float64(2.290436), 'test_loss_std': np.float64(25751.015775), 'test_loss_bottom_decile': np.float64(139187.545166), 'test_loss_top_decile': np.float64(223561.860596), 'test_loss_min': np.float64(120330.001221), 'test_loss_max': np.float64(223561.860596), 'test_loss_bottom10%': np.float64(120330.001221), 'test_loss_top10%': np.float64(223561.860596), 'test_loss_cos1': np.float64(0.987754), 'test_loss_entropy': np.float64(2.290436), 'val_avg_loss_std': np.float64(4.301035), 'val_avg_loss_bottom_decile': np.float64(26.565869), 'val_avg_loss_top_decile': np.float64(38.446939), 'val_avg_loss_min': np.float64(23.5476), 'val_avg_loss_max': np.float64(38.446939), 'val_avg_loss_bottom10%': np.float64(23.5476), 'val_avg_loss_top10%': np.float64(38.446939), 'val_avg_loss_cos1': np.float64(0.990723), 'val_avg_loss_entropy': np.float64(2.293043), 'val_loss_std': np.float64(22296.565391), 'val_loss_bottom_decile': np.float64(137717.465881), 'val_loss_top_decile': np.float64(199308.931396), 'val_loss_min': np.float64(122070.756714), 'val_loss_max': np.float64(199308.931396), 'val_loss_bottom10%': np.float64(122070.756714), 'val_loss_top10%': np.float64(199308.931396), 'val_loss_cos1': np.float64(0.990723), 'val_loss_entropy': np.float64(2.293043)}} 2024-11-14 12:41:56,791 (server:353) INFO: Server: Starting evaluation at the end of round 15. 2024-11-14 12:41:56,792 (server:359) INFO: ----------- Starting a new training round (Round #16) ------------- 2024-11-14 12:44:34,787 (client:354) INFO: {'Role': 'Client #2', 'Round': 16, 'Results_raw': {'train_loss': 14.053121, 'val_loss': 12.842686, 'test_loss': 13.651006}} 2024-11-14 12:45:32,518 (client:354) INFO: {'Role': 'Client #9', 'Round': 16, 'Results_raw': {'train_loss': 31.173103, 'val_loss': 27.998304, 'test_loss': 27.109375}} 2024-11-14 12:46:30,502 (client:354) INFO: {'Role': 'Client #5', 'Round': 16, 'Results_raw': {'train_loss': 25.266366, 'val_loss': 25.502366, 'test_loss': 32.275099}} 2024-11-14 12:47:28,260 (client:354) INFO: {'Role': 'Client #8', 'Round': 16, 'Results_raw': {'train_loss': 21.475047, 'val_loss': 20.22465, 'test_loss': 21.116621}} 2024-11-14 12:48:23,139 (client:354) INFO: {'Role': 'Client #6', 'Round': 16, 'Results_raw': {'train_loss': 24.358296, 'val_loss': 22.199489, 'test_loss': 23.380574}} 2024-11-14 12:49:19,426 (client:354) INFO: {'Role': 'Client #10', 'Round': 16, 'Results_raw': {'train_loss': 23.425362, 'val_loss': 22.648425, 'test_loss': 23.814636}} 2024-11-14 12:50:16,353 (client:354) INFO: {'Role': 'Client #4', 'Round': 16, 'Results_raw': {'train_loss': 24.229157, 'val_loss': 21.915293, 'test_loss': 23.297438}} 2024-11-14 12:51:11,879 (client:354) INFO: {'Role': 'Client #3', 'Round': 16, 'Results_raw': {'train_loss': 17.552002, 'val_loss': 17.334046, 'test_loss': 20.183595}} 2024-11-14 12:52:06,871 (client:354) INFO: {'Role': 'Client #1', 'Round': 16, 'Results_raw': {'train_loss': 18.306708, 'val_loss': 16.755101, 'test_loss': 18.524905}} 2024-11-14 12:53:01,856 (client:354) INFO: {'Role': 'Client #7', 'Round': 16, 'Results_raw': {'train_loss': 24.245162, 'val_loss': 22.970276, 'test_loss': 23.138278}} 2024-11-14 12:53:01,859 (server:615) INFO: {'Role': 'Server #', 'Round': 15, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.404195), 'test_loss': np.float64(162799.348157), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.341683), 'val_loss': np.float64(162475.284125), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.404195), 'test_loss': np.float64(162799.348157), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.341683), 'val_loss': np.float64(162475.284125), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.140769), 'test_avg_loss_bottom_decile': np.float64(26.59509), 'test_avg_loss_top_decile': np.float64(43.322287), 'test_avg_loss_min': np.float64(22.761953), 'test_avg_loss_max': np.float64(43.322287), 'test_avg_loss_bottom10%': np.float64(22.761953), 'test_avg_loss_top10%': np.float64(43.322287), 'test_avg_loss_cos1': np.float64(0.986865), 'test_avg_loss_entropy': np.float64(2.289492), 'test_loss_std': np.float64(26649.746438), 'test_loss_bottom_decile': np.float64(137868.949097), 'test_loss_top_decile': np.float64(224582.737122), 'test_loss_min': np.float64(117997.963989), 'test_loss_max': np.float64(224582.737122), 'test_loss_bottom10%': np.float64(117997.963989), 'test_loss_top10%': np.float64(224582.737122), 'test_loss_cos1': np.float64(0.986865), 'test_loss_entropy': np.float64(2.289492), 'val_avg_loss_std': np.float64(4.509752), 'val_avg_loss_bottom_decile': np.float64(26.323706), 'val_avg_loss_top_decile': np.float64(38.619664), 'val_avg_loss_min': np.float64(23.09337), 'val_avg_loss_max': np.float64(38.619664), 'val_avg_loss_bottom10%': np.float64(23.09337), 'val_avg_loss_top10%': np.float64(38.619664), 'val_avg_loss_cos1': np.float64(0.989806), 'val_avg_loss_entropy': np.float64(2.292049), 'val_loss_std': np.float64(23378.554257), 'val_loss_bottom_decile': np.float64(136462.09259), 'val_loss_top_decile': np.float64(200204.339355), 'val_loss_min': np.float64(119716.028137), 'val_loss_max': np.float64(200204.339355), 'val_loss_bottom10%': np.float64(119716.028137), 'val_loss_top10%': np.float64(200204.339355), 'val_loss_cos1': np.float64(0.989806), 'val_loss_entropy': np.float64(2.292049)}} 2024-11-14 12:53:01,900 (server:353) INFO: Server: Starting evaluation at the end of round 16. 2024-11-14 12:53:01,901 (server:359) INFO: ----------- Starting a new training round (Round #17) ------------- 2024-11-14 12:55:28,928 (client:354) INFO: {'Role': 'Client #10', 'Round': 17, 'Results_raw': {'train_loss': 23.34509, 'val_loss': 22.527248, 'test_loss': 24.336599}} 2024-11-14 12:56:23,460 (client:354) INFO: {'Role': 'Client #5', 'Round': 17, 'Results_raw': {'train_loss': 25.174533, 'val_loss': 25.426282, 'test_loss': 32.724143}} 2024-11-14 12:57:18,636 (client:354) INFO: {'Role': 'Client #3', 'Round': 17, 'Results_raw': {'train_loss': 17.518808, 'val_loss': 17.458965, 'test_loss': 19.927009}} 2024-11-14 12:58:14,205 (client:354) INFO: {'Role': 'Client #7', 'Round': 17, 'Results_raw': {'train_loss': 24.20091, 'val_loss': 22.932096, 'test_loss': 23.315345}} 2024-11-14 12:59:08,984 (client:354) INFO: {'Role': 'Client #2', 'Round': 17, 'Results_raw': {'train_loss': 13.965708, 'val_loss': 12.864028, 'test_loss': 13.486585}} 2024-11-14 13:00:04,816 (client:354) INFO: {'Role': 'Client #4', 'Round': 17, 'Results_raw': {'train_loss': 24.184441, 'val_loss': 21.866771, 'test_loss': 23.039932}} 2024-11-14 13:00:59,472 (client:354) INFO: {'Role': 'Client #8', 'Round': 17, 'Results_raw': {'train_loss': 21.433611, 'val_loss': 20.290713, 'test_loss': 21.057713}} 2024-11-14 13:01:54,143 (client:354) INFO: {'Role': 'Client #9', 'Round': 17, 'Results_raw': {'train_loss': 31.056952, 'val_loss': 28.125916, 'test_loss': 27.710938}} 2024-11-14 13:02:49,140 (client:354) INFO: {'Role': 'Client #6', 'Round': 17, 'Results_raw': {'train_loss': 24.21746, 'val_loss': 22.057662, 'test_loss': 23.46031}} 2024-11-14 13:03:44,259 (client:354) INFO: {'Role': 'Client #1', 'Round': 17, 'Results_raw': {'train_loss': 18.205913, 'val_loss': 16.736745, 'test_loss': 18.632716}} 2024-11-14 13:03:44,264 (server:615) INFO: {'Role': 'Server #', 'Round': 16, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.196261), 'test_loss': np.float64(161721.41712), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.147403), 'val_loss': np.float64(161468.137616), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.196261), 'test_loss': np.float64(161721.41712), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.147403), 'val_loss': np.float64(161468.137616), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.185707), 'test_avg_loss_bottom_decile': np.float64(26.213461), 'test_avg_loss_top_decile': np.float64(43.193456), 'test_avg_loss_min': np.float64(22.510805), 'test_avg_loss_max': np.float64(43.193456), 'test_avg_loss_bottom10%': np.float64(22.510805), 'test_avg_loss_top10%': np.float64(43.193456), 'test_avg_loss_cos1': np.float64(0.986464), 'test_avg_loss_entropy': np.float64(2.289083), 'test_loss_std': np.float64(26882.707375), 'test_loss_bottom_decile': np.float64(135890.582764), 'test_loss_top_decile': np.float64(223914.876587), 'test_loss_min': np.float64(116696.012024), 'test_loss_max': np.float64(223914.876587), 'test_loss_bottom10%': np.float64(116696.012024), 'test_loss_top10%': np.float64(223914.876587), 'test_loss_cos1': np.float64(0.986464), 'test_loss_entropy': np.float64(2.289083), 'val_avg_loss_std': np.float64(4.571504), 'val_avg_loss_bottom_decile': np.float64(25.920253), 'val_avg_loss_top_decile': np.float64(38.46784), 'val_avg_loss_min': np.float64(22.824157), 'val_avg_loss_max': np.float64(38.46784), 'val_avg_loss_bottom10%': np.float64(22.824157), 'val_avg_loss_top10%': np.float64(38.46784), 'val_avg_loss_cos1': np.float64(0.9894), 'val_avg_loss_entropy': np.float64(2.291616), 'val_loss_std': np.float64(23698.677653), 'val_loss_bottom_decile': np.float64(134370.590393), 'val_loss_top_decile': np.float64(199417.280945), 'val_loss_min': np.float64(118320.429016), 'val_loss_max': np.float64(199417.280945), 'val_loss_bottom10%': np.float64(118320.429016), 'val_loss_top10%': np.float64(199417.280945), 'val_loss_cos1': np.float64(0.9894), 'val_loss_entropy': np.float64(2.291616)}} 2024-11-14 13:03:44,299 (server:353) INFO: Server: Starting evaluation at the end of round 17. 2024-11-14 13:03:44,300 (server:359) INFO: ----------- Starting a new training round (Round #18) ------------- 2024-11-14 13:06:28,876 (client:354) INFO: {'Role': 'Client #3', 'Round': 18, 'Results_raw': {'train_loss': 17.408714, 'val_loss': 17.293774, 'test_loss': 20.176726}} 2024-11-14 13:07:29,683 (client:354) INFO: {'Role': 'Client #5', 'Round': 18, 'Results_raw': {'train_loss': 25.113087, 'val_loss': 25.225497, 'test_loss': 32.442237}} 2024-11-14 13:08:30,812 (client:354) INFO: {'Role': 'Client #7', 'Round': 18, 'Results_raw': {'train_loss': 24.148865, 'val_loss': 23.082371, 'test_loss': 23.548817}} 2024-11-14 13:09:31,460 (client:354) INFO: {'Role': 'Client #9', 'Round': 18, 'Results_raw': {'train_loss': 31.025212, 'val_loss': 27.997738, 'test_loss': 27.309393}} 2024-11-14 13:10:30,330 (client:354) INFO: {'Role': 'Client #10', 'Round': 18, 'Results_raw': {'train_loss': 23.251802, 'val_loss': 22.436134, 'test_loss': 23.774014}} 2024-11-14 13:11:29,444 (client:354) INFO: {'Role': 'Client #1', 'Round': 18, 'Results_raw': {'train_loss': 18.137113, 'val_loss': 16.75978, 'test_loss': 18.50231}} 2024-11-14 13:12:28,634 (client:354) INFO: {'Role': 'Client #4', 'Round': 18, 'Results_raw': {'train_loss': 24.106876, 'val_loss': 21.919272, 'test_loss': 23.238747}} 2024-11-14 13:13:27,511 (client:354) INFO: {'Role': 'Client #8', 'Round': 18, 'Results_raw': {'train_loss': 21.313022, 'val_loss': 20.481462, 'test_loss': 21.242503}} 2024-11-14 13:14:26,237 (client:354) INFO: {'Role': 'Client #2', 'Round': 18, 'Results_raw': {'train_loss': 13.899017, 'val_loss': 12.684812, 'test_loss': 13.446514}} 2024-11-14 13:15:26,026 (client:354) INFO: {'Role': 'Client #6', 'Round': 18, 'Results_raw': {'train_loss': 24.245124, 'val_loss': 22.054008, 'test_loss': 23.638176}} 2024-11-14 13:15:26,030 (server:615) INFO: {'Role': 'Server #', 'Round': 17, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.253053), 'test_loss': np.float64(162015.828461), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.152804), 'val_loss': np.float64(161496.136932), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.253053), 'test_loss': np.float64(162015.828461), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.152804), 'val_loss': np.float64(161496.136932), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.191294), 'test_avg_loss_bottom_decile': np.float64(26.233151), 'test_avg_loss_top_decile': np.float64(43.45185), 'test_avg_loss_min': np.float64(22.761381), 'test_avg_loss_max': np.float64(43.45185), 'test_avg_loss_bottom10%': np.float64(22.761381), 'test_avg_loss_top10%': np.float64(43.45185), 'test_avg_loss_cos1': np.float64(0.986484), 'test_avg_loss_entropy': np.float64(2.289164), 'test_loss_std': np.float64(26911.666272), 'test_loss_bottom_decile': np.float64(135992.654663), 'test_loss_top_decile': np.float64(225254.392395), 'test_loss_min': np.float64(117994.999146), 'test_loss_max': np.float64(225254.392395), 'test_loss_bottom10%': np.float64(117994.999146), 'test_loss_top10%': np.float64(225254.392395), 'test_loss_cos1': np.float64(0.986484), 'test_loss_entropy': np.float64(2.289164), 'val_avg_loss_std': np.float64(4.516055), 'val_avg_loss_bottom_decile': np.float64(25.917155), 'val_avg_loss_top_decile': np.float64(38.556249), 'val_avg_loss_min': np.float64(23.06712), 'val_avg_loss_max': np.float64(38.556249), 'val_avg_loss_bottom10%': np.float64(23.06712), 'val_avg_loss_top10%': np.float64(38.556249), 'val_avg_loss_cos1': np.float64(0.989655), 'val_avg_loss_entropy': np.float64(2.29191), 'val_loss_std': np.float64(23411.227847), 'val_loss_bottom_decile': np.float64(134354.533813), 'val_loss_top_decile': np.float64(199875.593384), 'val_loss_min': np.float64(119579.949036), 'val_loss_max': np.float64(199875.593384), 'val_loss_bottom10%': np.float64(119579.949036), 'val_loss_top10%': np.float64(199875.593384), 'val_loss_cos1': np.float64(0.989655), 'val_loss_entropy': np.float64(2.29191)}} 2024-11-14 13:15:26,076 (server:353) INFO: Server: Starting evaluation at the end of round 18. 2024-11-14 13:15:26,076 (server:359) INFO: ----------- Starting a new training round (Round #19) ------------- 2024-11-14 13:17:55,770 (client:354) INFO: {'Role': 'Client #3', 'Round': 19, 'Results_raw': {'train_loss': 17.447228, 'val_loss': 17.322807, 'test_loss': 20.139221}} 2024-11-14 13:18:46,298 (client:354) INFO: {'Role': 'Client #7', 'Round': 19, 'Results_raw': {'train_loss': 24.04614, 'val_loss': 23.005038, 'test_loss': 23.168257}} 2024-11-14 13:19:38,982 (client:354) INFO: {'Role': 'Client #2', 'Round': 19, 'Results_raw': {'train_loss': 13.912177, 'val_loss': 12.814259, 'test_loss': 13.562409}} 2024-11-14 13:20:33,292 (client:354) INFO: {'Role': 'Client #4', 'Round': 19, 'Results_raw': {'train_loss': 23.991447, 'val_loss': 21.90882, 'test_loss': 23.230174}} 2024-11-14 13:21:31,898 (client:354) INFO: {'Role': 'Client #5', 'Round': 19, 'Results_raw': {'train_loss': 24.965471, 'val_loss': 25.516635, 'test_loss': 32.707998}} 2024-11-14 13:22:29,393 (client:354) INFO: {'Role': 'Client #9', 'Round': 19, 'Results_raw': {'train_loss': 30.950902, 'val_loss': 27.983362, 'test_loss': 27.354233}} 2024-11-14 13:23:25,203 (client:354) INFO: {'Role': 'Client #10', 'Round': 19, 'Results_raw': {'train_loss': 23.192676, 'val_loss': 22.59084, 'test_loss': 23.997564}} 2024-11-14 13:24:20,893 (client:354) INFO: {'Role': 'Client #1', 'Round': 19, 'Results_raw': {'train_loss': 18.12352, 'val_loss': 16.475057, 'test_loss': 18.399815}} 2024-11-14 13:25:22,476 (client:354) INFO: {'Role': 'Client #8', 'Round': 19, 'Results_raw': {'train_loss': 21.26641, 'val_loss': 20.15807, 'test_loss': 21.150502}} 2024-11-14 13:26:17,864 (client:354) INFO: {'Role': 'Client #6', 'Round': 19, 'Results_raw': {'train_loss': 24.113942, 'val_loss': 22.022188, 'test_loss': 23.220508}} 2024-11-14 13:26:17,866 (server:615) INFO: {'Role': 'Server #', 'Round': 18, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.260289), 'test_loss': np.float64(162053.336523), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.149303), 'val_loss': np.float64(161477.985773), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.260289), 'test_loss': np.float64(162053.336523), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.149303), 'val_loss': np.float64(161477.985773), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.042031), 'test_avg_loss_bottom_decile': np.float64(26.200965), 'test_avg_loss_top_decile': np.float64(43.131208), 'test_avg_loss_min': np.float64(23.136318), 'test_avg_loss_max': np.float64(43.131208), 'test_avg_loss_bottom10%': np.float64(23.136318), 'test_avg_loss_top10%': np.float64(43.131208), 'test_avg_loss_cos1': np.float64(0.987241), 'test_avg_loss_entropy': np.float64(2.289941), 'test_loss_std': np.float64(26137.891253), 'test_loss_bottom_decile': np.float64(135825.80481), 'test_loss_top_decile': np.float64(223592.184021), 'test_loss_min': np.float64(119938.670044), 'test_loss_max': np.float64(223592.184021), 'test_loss_bottom10%': np.float64(119938.670044), 'test_loss_top10%': np.float64(223592.184021), 'test_loss_cos1': np.float64(0.987241), 'test_loss_entropy': np.float64(2.289941), 'val_avg_loss_std': np.float64(4.369695), 'val_avg_loss_bottom_decile': np.float64(25.866797), 'val_avg_loss_top_decile': np.float64(38.224642), 'val_avg_loss_min': np.float64(23.446025), 'val_avg_loss_max': np.float64(38.224642), 'val_avg_loss_bottom10%': np.float64(23.446025), 'val_avg_loss_top10%': np.float64(38.224642), 'val_avg_loss_cos1': np.float64(0.990303), 'val_avg_loss_entropy': np.float64(2.292606), 'val_loss_std': np.float64(22652.497391), 'val_loss_bottom_decile': np.float64(134093.476379), 'val_loss_top_decile': np.float64(198156.546448), 'val_loss_min': np.float64(121544.193237), 'val_loss_max': np.float64(198156.546448), 'val_loss_bottom10%': np.float64(121544.193237), 'val_loss_top10%': np.float64(198156.546448), 'val_loss_cos1': np.float64(0.990303), 'val_loss_entropy': np.float64(2.292606)}} 2024-11-14 13:26:17,906 (server:353) INFO: Server: Starting evaluation at the end of round 19. 2024-11-14 13:26:17,907 (server:359) INFO: ----------- Starting a new training round (Round #20) ------------- 2024-11-14 13:28:48,155 (client:354) INFO: {'Role': 'Client #3', 'Round': 20, 'Results_raw': {'train_loss': 17.317035, 'val_loss': 17.365271, 'test_loss': 20.204251}} 2024-11-14 13:29:44,660 (client:354) INFO: {'Role': 'Client #4', 'Round': 20, 'Results_raw': {'train_loss': 23.96306, 'val_loss': 21.877432, 'test_loss': 23.264997}} 2024-11-14 13:30:42,426 (client:354) INFO: {'Role': 'Client #1', 'Round': 20, 'Results_raw': {'train_loss': 18.047233, 'val_loss': 16.528656, 'test_loss': 18.743463}} 2024-11-14 13:31:39,114 (client:354) INFO: {'Role': 'Client #5', 'Round': 20, 'Results_raw': {'train_loss': 24.970863, 'val_loss': 25.471472, 'test_loss': 32.837759}} 2024-11-14 13:32:35,770 (client:354) INFO: {'Role': 'Client #9', 'Round': 20, 'Results_raw': {'train_loss': 30.883391, 'val_loss': 27.830692, 'test_loss': 27.134252}} 2024-11-14 13:33:32,294 (client:354) INFO: {'Role': 'Client #8', 'Round': 20, 'Results_raw': {'train_loss': 21.2307, 'val_loss': 20.227769, 'test_loss': 21.111744}} 2024-11-14 13:34:32,765 (client:354) INFO: {'Role': 'Client #7', 'Round': 20, 'Results_raw': {'train_loss': 23.973715, 'val_loss': 23.018914, 'test_loss': 23.582016}} 2024-11-14 13:35:29,567 (client:354) INFO: {'Role': 'Client #6', 'Round': 20, 'Results_raw': {'train_loss': 24.069556, 'val_loss': 22.138361, 'test_loss': 23.839131}} 2024-11-14 13:36:26,577 (client:354) INFO: {'Role': 'Client #2', 'Round': 20, 'Results_raw': {'train_loss': 13.781344, 'val_loss': 12.694939, 'test_loss': 13.678796}} 2024-11-14 13:37:23,254 (client:354) INFO: {'Role': 'Client #10', 'Round': 20, 'Results_raw': {'train_loss': 23.201516, 'val_loss': 22.534417, 'test_loss': 23.994194}} 2024-11-14 13:37:23,257 (server:615) INFO: {'Role': 'Server #', 'Round': 19, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.317326), 'test_loss': np.float64(162349.019879), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.203182), 'val_loss': np.float64(161757.293506), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.317326), 'test_loss': np.float64(162349.019879), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(31.203182), 'val_loss': np.float64(161757.293506), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.124196), 'test_avg_loss_bottom_decile': np.float64(26.058697), 'test_avg_loss_top_decile': np.float64(43.384168), 'test_avg_loss_min': np.float64(23.038603), 'test_avg_loss_max': np.float64(43.384168), 'test_avg_loss_bottom10%': np.float64(23.038603), 'test_avg_loss_top10%': np.float64(43.384168), 'test_avg_loss_cos1': np.float64(0.986877), 'test_avg_loss_entropy': np.float64(2.289567), 'test_loss_std': np.float64(26563.829811), 'test_loss_bottom_decile': np.float64(135088.282898), 'test_loss_top_decile': np.float64(224903.527649), 'test_loss_min': np.float64(119432.120361), 'test_loss_max': np.float64(224903.527649), 'test_loss_bottom10%': np.float64(119432.120361), 'test_loss_top10%': np.float64(224903.527649), 'test_loss_cos1': np.float64(0.986877), 'test_loss_entropy': np.float64(2.289567), 'val_avg_loss_std': np.float64(4.43395), 'val_avg_loss_bottom_decile': np.float64(25.705156), 'val_avg_loss_top_decile': np.float64(38.378708), 'val_avg_loss_min': np.float64(23.32224), 'val_avg_loss_max': np.float64(38.378708), 'val_avg_loss_bottom10%': np.float64(23.32224), 'val_avg_loss_top10%': np.float64(38.378708), 'val_avg_loss_cos1': np.float64(0.990054), 'val_avg_loss_entropy': np.float64(2.292325), 'val_loss_std': np.float64(22985.594822), 'val_loss_bottom_decile': np.float64(133255.528931), 'val_loss_top_decile': np.float64(198955.22345), 'val_loss_min': np.float64(120902.491394), 'val_loss_max': np.float64(198955.22345), 'val_loss_bottom10%': np.float64(120902.491394), 'val_loss_top10%': np.float64(198955.22345), 'val_loss_cos1': np.float64(0.990054), 'val_loss_entropy': np.float64(2.292325)}} 2024-11-14 13:37:23,290 (server:353) INFO: Server: Starting evaluation at the end of round 20. 2024-11-14 13:37:23,291 (server:359) INFO: ----------- Starting a new training round (Round #21) ------------- 2024-11-14 13:39:56,611 (client:354) INFO: {'Role': 'Client #5', 'Round': 21, 'Results_raw': {'train_loss': 24.866644, 'val_loss': 25.316626, 'test_loss': 32.799948}} 2024-11-14 13:40:53,253 (client:354) INFO: {'Role': 'Client #3', 'Round': 21, 'Results_raw': {'train_loss': 17.315158, 'val_loss': 17.320725, 'test_loss': 20.119048}} 2024-11-14 13:41:51,422 (client:354) INFO: {'Role': 'Client #6', 'Round': 21, 'Results_raw': {'train_loss': 24.012451, 'val_loss': 22.11775, 'test_loss': 23.18235}} 2024-11-14 13:42:52,439 (client:354) INFO: {'Role': 'Client #9', 'Round': 21, 'Results_raw': {'train_loss': 30.85549, 'val_loss': 27.871459, 'test_loss': 27.292528}} 2024-11-14 13:43:58,881 (client:354) INFO: {'Role': 'Client #4', 'Round': 21, 'Results_raw': {'train_loss': 23.917755, 'val_loss': 21.940111, 'test_loss': 23.301256}} 2024-11-14 13:45:06,850 (client:354) INFO: {'Role': 'Client #1', 'Round': 21, 'Results_raw': {'train_loss': 17.961754, 'val_loss': 16.433875, 'test_loss': 18.598677}} 2024-11-14 13:46:07,807 (client:354) INFO: {'Role': 'Client #7', 'Round': 21, 'Results_raw': {'train_loss': 23.925019, 'val_loss': 22.849877, 'test_loss': 22.976192}} 2024-11-14 13:47:06,448 (client:354) INFO: {'Role': 'Client #2', 'Round': 21, 'Results_raw': {'train_loss': 13.781632, 'val_loss': 12.601614, 'test_loss': 13.412707}} 2024-11-14 13:48:04,905 (client:354) INFO: {'Role': 'Client #10', 'Round': 21, 'Results_raw': {'train_loss': 23.125292, 'val_loss': 22.421831, 'test_loss': 23.654172}} 2024-11-14 13:49:03,334 (client:354) INFO: {'Role': 'Client #8', 'Round': 21, 'Results_raw': {'train_loss': 21.198081, 'val_loss': 20.19442, 'test_loss': 21.16067}} 2024-11-14 13:49:03,337 (server:615) INFO: {'Role': 'Server #', 'Round': 20, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.002671), 'test_loss': np.float64(160717.845044), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.841935), 'val_loss': np.float64(159884.588672), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.002671), 'test_loss': np.float64(160717.845044), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.841935), 'val_loss': np.float64(159884.588672), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.005145), 'test_avg_loss_bottom_decile': np.float64(25.837943), 'test_avg_loss_top_decile': np.float64(42.84935), 'test_avg_loss_min': np.float64(23.057523), 'test_avg_loss_max': np.float64(42.84935), 'test_avg_loss_bottom10%': np.float64(23.057523), 'test_avg_loss_top10%': np.float64(42.84935), 'test_avg_loss_cos1': np.float64(0.987218), 'test_avg_loss_entropy': np.float64(2.289938), 'test_loss_std': np.float64(25946.670414), 'test_loss_bottom_decile': np.float64(133943.894226), 'test_loss_top_decile': np.float64(222131.031677), 'test_loss_min': np.float64(119530.201294), 'test_loss_max': np.float64(222131.031677), 'test_loss_bottom10%': np.float64(119530.201294), 'test_loss_top10%': np.float64(222131.031677), 'test_loss_cos1': np.float64(0.987218), 'test_loss_entropy': np.float64(2.289938), 'val_avg_loss_std': np.float64(4.295072), 'val_avg_loss_bottom_decile': np.float64(25.460968), 'val_avg_loss_top_decile': np.float64(37.79714), 'val_avg_loss_min': np.float64(23.339965), 'val_avg_loss_max': np.float64(37.79714), 'val_avg_loss_bottom10%': np.float64(23.339965), 'val_avg_loss_top10%': np.float64(37.79714), 'val_avg_loss_cos1': np.float64(0.990442), 'val_avg_loss_entropy': np.float64(2.292751), 'val_loss_std': np.float64(22265.654818), 'val_loss_bottom_decile': np.float64(131989.65741), 'val_loss_top_decile': np.float64(195940.37439), 'val_loss_min': np.float64(120994.38092), 'val_loss_max': np.float64(195940.37439), 'val_loss_bottom10%': np.float64(120994.38092), 'val_loss_top10%': np.float64(195940.37439), 'val_loss_cos1': np.float64(0.990442), 'val_loss_entropy': np.float64(2.292751)}} 2024-11-14 13:49:03,371 (server:353) INFO: Server: Starting evaluation at the end of round 21. 2024-11-14 13:49:03,371 (server:359) INFO: ----------- Starting a new training round (Round #22) ------------- 2024-11-14 13:51:43,795 (client:354) INFO: {'Role': 'Client #4', 'Round': 22, 'Results_raw': {'train_loss': 23.813979, 'val_loss': 21.779986, 'test_loss': 23.486372}} 2024-11-14 13:52:52,122 (client:354) INFO: {'Role': 'Client #8', 'Round': 22, 'Results_raw': {'train_loss': 21.126168, 'val_loss': 20.218634, 'test_loss': 20.946314}} 2024-11-14 13:53:52,984 (client:354) INFO: {'Role': 'Client #2', 'Round': 22, 'Results_raw': {'train_loss': 13.769204, 'val_loss': 12.831622, 'test_loss': 13.629407}} 2024-11-14 13:55:02,158 (client:354) INFO: {'Role': 'Client #6', 'Round': 22, 'Results_raw': {'train_loss': 23.937611, 'val_loss': 22.190236, 'test_loss': 23.541435}} 2024-11-14 13:56:11,031 (client:354) INFO: {'Role': 'Client #3', 'Round': 22, 'Results_raw': {'train_loss': 17.262193, 'val_loss': 17.176848, 'test_loss': 19.563887}} 2024-11-14 13:57:19,879 (client:354) INFO: {'Role': 'Client #10', 'Round': 22, 'Results_raw': {'train_loss': 23.080653, 'val_loss': 22.55897, 'test_loss': 24.060899}} 2024-11-14 13:58:27,941 (client:354) INFO: {'Role': 'Client #1', 'Round': 22, 'Results_raw': {'train_loss': 17.970567, 'val_loss': 16.368875, 'test_loss': 18.290111}} 2024-11-14 13:59:36,766 (client:354) INFO: {'Role': 'Client #9', 'Round': 22, 'Results_raw': {'train_loss': 30.829537, 'val_loss': 27.762136, 'test_loss': 27.213845}} 2024-11-14 14:00:45,139 (client:354) INFO: {'Role': 'Client #7', 'Round': 22, 'Results_raw': {'train_loss': 23.887168, 'val_loss': 23.008608, 'test_loss': 23.056201}} 2024-11-14 14:01:47,733 (client:354) INFO: {'Role': 'Client #5', 'Round': 22, 'Results_raw': {'train_loss': 24.860715, 'val_loss': 25.121756, 'test_loss': 32.637417}} 2024-11-14 14:01:47,738 (server:615) INFO: {'Role': 'Server #', 'Round': 21, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.997669), 'test_loss': np.float64(160691.916443), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.858891), 'val_loss': np.float64(159972.493304), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.997669), 'test_loss': np.float64(160691.916443), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.858891), 'val_loss': np.float64(159972.493304), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.171163), 'test_avg_loss_bottom_decile': np.float64(25.687174), 'test_avg_loss_top_decile': np.float64(43.270017), 'test_avg_loss_min': np.float64(22.803192), 'test_avg_loss_max': np.float64(43.270017), 'test_avg_loss_bottom10%': np.float64(22.803192), 'test_avg_loss_top10%': np.float64(43.270017), 'test_avg_loss_cos1': np.float64(0.986369), 'test_avg_loss_entropy': np.float64(2.289097), 'test_loss_std': np.float64(26807.309985), 'test_loss_bottom_decile': np.float64(133162.311462), 'test_loss_top_decile': np.float64(224311.767639), 'test_loss_min': np.float64(118211.748474), 'test_loss_max': np.float64(224311.767639), 'test_loss_bottom10%': np.float64(118211.748474), 'test_loss_top10%': np.float64(224311.767639), 'test_loss_cos1': np.float64(0.986369), 'test_loss_entropy': np.float64(2.289097), 'val_avg_loss_std': np.float64(4.451611), 'val_avg_loss_bottom_decile': np.float64(25.319117), 'val_avg_loss_top_decile': np.float64(38.133896), 'val_avg_loss_min': np.float64(23.091795), 'val_avg_loss_max': np.float64(38.133896), 'val_avg_loss_bottom10%': np.float64(23.091795), 'val_avg_loss_top10%': np.float64(38.133896), 'val_avg_loss_cos1': np.float64(0.989755), 'val_avg_loss_entropy': np.float64(2.29203), 'val_loss_std': np.float64(23077.148846), 'val_loss_bottom_decile': np.float64(131254.30481), 'val_loss_top_decile': np.float64(197686.118591), 'val_loss_min': np.float64(119707.865417), 'val_loss_max': np.float64(197686.118591), 'val_loss_bottom10%': np.float64(119707.865417), 'val_loss_top10%': np.float64(197686.118591), 'val_loss_cos1': np.float64(0.989755), 'val_loss_entropy': np.float64(2.29203)}} 2024-11-14 14:01:47,778 (server:353) INFO: Server: Starting evaluation at the end of round 22. 2024-11-14 14:01:47,779 (server:359) INFO: ----------- Starting a new training round (Round #23) ------------- 2024-11-14 14:04:24,783 (client:354) INFO: {'Role': 'Client #5', 'Round': 23, 'Results_raw': {'train_loss': 24.756135, 'val_loss': 25.138429, 'test_loss': 32.769019}} 2024-11-14 14:05:23,350 (client:354) INFO: {'Role': 'Client #6', 'Round': 23, 'Results_raw': {'train_loss': 23.909244, 'val_loss': 22.104952, 'test_loss': 23.472236}} 2024-11-14 14:06:23,308 (client:354) INFO: {'Role': 'Client #9', 'Round': 23, 'Results_raw': {'train_loss': 30.7531, 'val_loss': 27.818725, 'test_loss': 27.165506}} 2024-11-14 14:07:22,476 (client:354) INFO: {'Role': 'Client #1', 'Round': 23, 'Results_raw': {'train_loss': 17.929064, 'val_loss': 16.334117, 'test_loss': 18.456854}} 2024-11-14 14:08:21,474 (client:354) INFO: {'Role': 'Client #8', 'Round': 23, 'Results_raw': {'train_loss': 21.089805, 'val_loss': 20.092017, 'test_loss': 21.000948}} 2024-11-14 14:09:24,561 (client:354) INFO: {'Role': 'Client #3', 'Round': 23, 'Results_raw': {'train_loss': 17.193408, 'val_loss': 17.270195, 'test_loss': 20.207054}} 2024-11-14 14:10:28,010 (client:354) INFO: {'Role': 'Client #4', 'Round': 23, 'Results_raw': {'train_loss': 23.769601, 'val_loss': 21.758279, 'test_loss': 23.074548}} 2024-11-14 14:11:26,776 (client:354) INFO: {'Role': 'Client #7', 'Round': 23, 'Results_raw': {'train_loss': 23.856315, 'val_loss': 22.970758, 'test_loss': 23.327141}} 2024-11-14 14:12:25,752 (client:354) INFO: {'Role': 'Client #10', 'Round': 23, 'Results_raw': {'train_loss': 23.019825, 'val_loss': 22.454311, 'test_loss': 23.900991}} 2024-11-14 14:13:24,340 (client:354) INFO: {'Role': 'Client #2', 'Round': 23, 'Results_raw': {'train_loss': 13.685194, 'val_loss': 12.694201, 'test_loss': 13.555219}} 2024-11-14 14:13:24,344 (server:615) INFO: {'Role': 'Server #', 'Round': 22, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.778317), 'test_loss': np.float64(159554.793555), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.621111), 'val_loss': np.float64(158739.841541), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.778317), 'test_loss': np.float64(159554.793555), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.621111), 'val_loss': np.float64(158739.841541), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.004271), 'test_avg_loss_bottom_decile': np.float64(25.602808), 'test_avg_loss_top_decile': np.float64(42.682552), 'test_avg_loss_min': np.float64(22.918736), 'test_avg_loss_max': np.float64(42.682552), 'test_avg_loss_bottom10%': np.float64(22.918736), 'test_avg_loss_top10%': np.float64(42.682552), 'test_avg_loss_cos1': np.float64(0.987039), 'test_avg_loss_entropy': np.float64(2.289781), 'test_loss_std': np.float64(25942.140429), 'test_loss_bottom_decile': np.float64(132724.957581), 'test_loss_top_decile': np.float64(221266.351135), 'test_loss_min': np.float64(118810.729248), 'test_loss_max': np.float64(221266.351135), 'test_loss_bottom10%': np.float64(118810.729248), 'test_loss_top10%': np.float64(221266.351135), 'test_loss_cos1': np.float64(0.987039), 'test_loss_entropy': np.float64(2.289781), 'val_avg_loss_std': np.float64(4.271783), 'val_avg_loss_bottom_decile': np.float64(25.222765), 'val_avg_loss_top_decile': np.float64(37.449184), 'val_avg_loss_min': np.float64(23.195864), 'val_avg_loss_max': np.float64(37.449184), 'val_avg_loss_bottom10%': np.float64(23.195864), 'val_avg_loss_top10%': np.float64(37.449184), 'val_avg_loss_cos1': np.float64(0.990409), 'val_avg_loss_entropy': np.float64(2.292715), 'val_loss_std': np.float64(22144.920609), 'val_loss_bottom_decile': np.float64(130754.815186), 'val_loss_top_decile': np.float64(194136.569397), 'val_loss_min': np.float64(120247.36084), 'val_loss_max': np.float64(194136.569397), 'val_loss_bottom10%': np.float64(120247.36084), 'val_loss_top10%': np.float64(194136.569397), 'val_loss_cos1': np.float64(0.990409), 'val_loss_entropy': np.float64(2.292715)}} 2024-11-14 14:13:24,388 (server:353) INFO: Server: Starting evaluation at the end of round 23. 2024-11-14 14:13:24,388 (server:359) INFO: ----------- Starting a new training round (Round #24) ------------- 2024-11-14 14:16:01,923 (client:354) INFO: {'Role': 'Client #4', 'Round': 24, 'Results_raw': {'train_loss': 23.74565, 'val_loss': 21.660179, 'test_loss': 23.051988}} 2024-11-14 14:17:01,586 (client:354) INFO: {'Role': 'Client #2', 'Round': 24, 'Results_raw': {'train_loss': 13.645725, 'val_loss': 12.733511, 'test_loss': 13.564441}} 2024-11-14 14:18:01,260 (client:354) INFO: {'Role': 'Client #10', 'Round': 24, 'Results_raw': {'train_loss': 22.958289, 'val_loss': 22.311222, 'test_loss': 23.591187}} 2024-11-14 14:19:00,177 (client:354) INFO: {'Role': 'Client #6', 'Round': 24, 'Results_raw': {'train_loss': 23.848343, 'val_loss': 22.019796, 'test_loss': 23.552305}} 2024-11-14 14:20:03,237 (client:354) INFO: {'Role': 'Client #9', 'Round': 24, 'Results_raw': {'train_loss': 30.735778, 'val_loss': 27.747955, 'test_loss': 26.888177}} 2024-11-14 14:21:02,286 (client:354) INFO: {'Role': 'Client #8', 'Round': 24, 'Results_raw': {'train_loss': 21.048664, 'val_loss': 20.171146, 'test_loss': 21.031077}} 2024-11-14 14:22:00,821 (client:354) INFO: {'Role': 'Client #3', 'Round': 24, 'Results_raw': {'train_loss': 17.138148, 'val_loss': 17.190524, 'test_loss': 20.119509}} 2024-11-14 14:22:59,012 (client:354) INFO: {'Role': 'Client #7', 'Round': 24, 'Results_raw': {'train_loss': 23.759678, 'val_loss': 22.989688, 'test_loss': 23.245014}} 2024-11-14 14:23:57,669 (client:354) INFO: {'Role': 'Client #1', 'Round': 24, 'Results_raw': {'train_loss': 17.865925, 'val_loss': 16.418635, 'test_loss': 18.380047}} 2024-11-14 14:24:57,089 (client:354) INFO: {'Role': 'Client #5', 'Round': 24, 'Results_raw': {'train_loss': 24.71046, 'val_loss': 25.235367, 'test_loss': 33.049003}} 2024-11-14 14:24:57,093 (server:615) INFO: {'Role': 'Server #', 'Round': 23, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.927693), 'test_loss': np.float64(160329.161407), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.747533), 'val_loss': np.float64(159395.208789), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.927693), 'test_loss': np.float64(160329.161407), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.747533), 'val_loss': np.float64(159395.208789), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.066953), 'test_avg_loss_bottom_decile': np.float64(25.603134), 'test_avg_loss_top_decile': np.float64(43.069372), 'test_avg_loss_min': np.float64(23.09126), 'test_avg_loss_max': np.float64(43.069372), 'test_avg_loss_bottom10%': np.float64(23.09126), 'test_avg_loss_top10%': np.float64(43.069372), 'test_avg_loss_cos1': np.float64(0.986844), 'test_avg_loss_entropy': np.float64(2.289614), 'test_loss_std': np.float64(26267.086927), 'test_loss_bottom_decile': np.float64(132726.645752), 'test_loss_top_decile': np.float64(223271.623901), 'test_loss_min': np.float64(119705.0896), 'test_loss_max': np.float64(223271.623901), 'test_loss_bottom10%': np.float64(119705.0896), 'test_loss_top10%': np.float64(223271.623901), 'test_loss_cos1': np.float64(0.986844), 'test_loss_entropy': np.float64(2.289614), 'val_avg_loss_std': np.float64(4.300476), 'val_avg_loss_bottom_decile': np.float64(25.203769), 'val_avg_loss_top_decile': np.float64(37.74614), 'val_avg_loss_min': np.float64(23.359942), 'val_avg_loss_max': np.float64(37.74614), 'val_avg_loss_bottom10%': np.float64(23.359942), 'val_avg_loss_top10%': np.float64(37.74614), 'val_avg_loss_cos1': np.float64(0.99036), 'val_avg_loss_entropy': np.float64(2.292673), 'val_loss_std': np.float64(22293.666215), 'val_loss_bottom_decile': np.float64(130656.336731), 'val_loss_top_decile': np.float64(195675.992004), 'val_loss_min': np.float64(121097.938232), 'val_loss_max': np.float64(195675.992004), 'val_loss_bottom10%': np.float64(121097.938232), 'val_loss_top10%': np.float64(195675.992004), 'val_loss_cos1': np.float64(0.99036), 'val_loss_entropy': np.float64(2.292673)}} 2024-11-14 14:24:57,129 (server:353) INFO: Server: Starting evaluation at the end of round 24. 2024-11-14 14:24:57,130 (server:359) INFO: ----------- Starting a new training round (Round #25) ------------- 2024-11-14 14:27:34,663 (client:354) INFO: {'Role': 'Client #5', 'Round': 25, 'Results_raw': {'train_loss': 24.616382, 'val_loss': 25.199016, 'test_loss': 33.081233}} 2024-11-14 14:28:39,004 (client:354) INFO: {'Role': 'Client #2', 'Round': 25, 'Results_raw': {'train_loss': 13.664136, 'val_loss': 12.67951, 'test_loss': 13.567572}} 2024-11-14 14:29:36,956 (client:354) INFO: {'Role': 'Client #4', 'Round': 25, 'Results_raw': {'train_loss': 23.711283, 'val_loss': 21.835801, 'test_loss': 23.440077}} 2024-11-14 14:30:27,891 (client:354) INFO: {'Role': 'Client #8', 'Round': 25, 'Results_raw': {'train_loss': 20.996428, 'val_loss': 20.17256, 'test_loss': 21.074476}} 2024-11-14 14:31:18,174 (client:354) INFO: {'Role': 'Client #10', 'Round': 25, 'Results_raw': {'train_loss': 22.916615, 'val_loss': 22.390138, 'test_loss': 23.52601}} 2024-11-14 14:32:08,660 (client:354) INFO: {'Role': 'Client #3', 'Round': 25, 'Results_raw': {'train_loss': 17.11721, 'val_loss': 17.008523, 'test_loss': 19.748089}} 2024-11-14 14:32:58,336 (client:354) INFO: {'Role': 'Client #9', 'Round': 25, 'Results_raw': {'train_loss': 30.632681, 'val_loss': 28.205023, 'test_loss': 27.824588}} 2024-11-14 14:33:48,535 (client:354) INFO: {'Role': 'Client #1', 'Round': 25, 'Results_raw': {'train_loss': 17.837501, 'val_loss': 16.441235, 'test_loss': 18.401514}} 2024-11-14 14:34:39,207 (client:354) INFO: {'Role': 'Client #6', 'Round': 25, 'Results_raw': {'train_loss': 23.822235, 'val_loss': 21.978033, 'test_loss': 23.639636}} 2024-11-14 14:35:29,633 (client:354) INFO: {'Role': 'Client #7', 'Round': 25, 'Results_raw': {'train_loss': 23.751362, 'val_loss': 22.801292, 'test_loss': 23.311107}} 2024-11-14 14:35:29,636 (server:615) INFO: {'Role': 'Server #', 'Round': 24, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.851289), 'test_loss': np.float64(159933.080072), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.696134), 'val_loss': np.float64(159128.757703), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.851289), 'test_loss': np.float64(159933.080072), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.696134), 'val_loss': np.float64(159128.757703), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.09522), 'test_avg_loss_bottom_decile': np.float64(25.518758), 'test_avg_loss_top_decile': np.float64(43.133746), 'test_avg_loss_min': np.float64(23.087687), 'test_avg_loss_max': np.float64(43.133746), 'test_avg_loss_bottom10%': np.float64(23.087687), 'test_avg_loss_top10%': np.float64(43.133746), 'test_avg_loss_cos1': np.float64(0.986635), 'test_avg_loss_entropy': np.float64(2.289434), 'test_loss_std': np.float64(26413.622976), 'test_loss_bottom_decile': np.float64(132289.243652), 'test_loss_top_decile': np.float64(223605.339539), 'test_loss_min': np.float64(119686.569458), 'test_loss_max': np.float64(223605.339539), 'test_loss_bottom10%': np.float64(119686.569458), 'test_loss_top10%': np.float64(223605.339539), 'test_loss_cos1': np.float64(0.986635), 'test_loss_entropy': np.float64(2.289434), 'val_avg_loss_std': np.float64(4.304109), 'val_avg_loss_bottom_decile': np.float64(25.155591), 'val_avg_loss_top_decile': np.float64(37.713116), 'val_avg_loss_min': np.float64(23.374364), 'val_avg_loss_max': np.float64(37.713116), 'val_avg_loss_bottom10%': np.float64(23.374364), 'val_avg_loss_top10%': np.float64(37.713116), 'val_avg_loss_cos1': np.float64(0.990312), 'val_avg_loss_entropy': np.float64(2.292627), 'val_loss_std': np.float64(22312.501749), 'val_loss_bottom_decile': np.float64(130406.584412), 'val_loss_top_decile': np.float64(195504.792114), 'val_loss_min': np.float64(121172.705017), 'val_loss_max': np.float64(195504.792114), 'val_loss_bottom10%': np.float64(121172.705017), 'val_loss_top10%': np.float64(195504.792114), 'val_loss_cos1': np.float64(0.990312), 'val_loss_entropy': np.float64(2.292627)}} 2024-11-14 14:35:29,666 (server:353) INFO: Server: Starting evaluation at the end of round 25. 2024-11-14 14:35:29,666 (server:359) INFO: ----------- Starting a new training round (Round #26) ------------- 2024-11-14 14:37:58,583 (client:354) INFO: {'Role': 'Client #8', 'Round': 26, 'Results_raw': {'train_loss': 20.95854, 'val_loss': 20.017799, 'test_loss': 20.870567}} 2024-11-14 14:38:55,621 (client:354) INFO: {'Role': 'Client #9', 'Round': 26, 'Results_raw': {'train_loss': 30.566604, 'val_loss': 28.037837, 'test_loss': 27.528291}} 2024-11-14 14:39:52,687 (client:354) INFO: {'Role': 'Client #7', 'Round': 26, 'Results_raw': {'train_loss': 23.684588, 'val_loss': 22.780073, 'test_loss': 23.07927}} 2024-11-14 14:40:49,772 (client:354) INFO: {'Role': 'Client #10', 'Round': 26, 'Results_raw': {'train_loss': 22.903383, 'val_loss': 22.324406, 'test_loss': 23.780162}} 2024-11-14 14:41:45,776 (client:354) INFO: {'Role': 'Client #2', 'Round': 26, 'Results_raw': {'train_loss': 13.650947, 'val_loss': 12.841428, 'test_loss': 13.409869}} 2024-11-14 14:42:42,874 (client:354) INFO: {'Role': 'Client #5', 'Round': 26, 'Results_raw': {'train_loss': 24.607848, 'val_loss': 25.100511, 'test_loss': 32.875661}} 2024-11-14 14:43:41,110 (client:354) INFO: {'Role': 'Client #1', 'Round': 26, 'Results_raw': {'train_loss': 17.788229, 'val_loss': 16.268919, 'test_loss': 18.424087}} 2024-11-14 14:44:40,150 (client:354) INFO: {'Role': 'Client #6', 'Round': 26, 'Results_raw': {'train_loss': 23.768979, 'val_loss': 21.965243, 'test_loss': 23.450831}} 2024-11-14 14:45:42,579 (client:354) INFO: {'Role': 'Client #4', 'Round': 26, 'Results_raw': {'train_loss': 23.643784, 'val_loss': 21.743115, 'test_loss': 23.352228}} 2024-11-14 14:46:41,107 (client:354) INFO: {'Role': 'Client #3', 'Round': 26, 'Results_raw': {'train_loss': 17.112328, 'val_loss': 17.19499, 'test_loss': 20.043533}} 2024-11-14 14:46:41,110 (server:615) INFO: {'Role': 'Server #', 'Round': 25, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.770738), 'test_loss': np.float64(159515.506757), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.573576), 'val_loss': np.float64(158493.417773), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.770738), 'test_loss': np.float64(159515.506757), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.573576), 'val_loss': np.float64(158493.417773), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.100992), 'test_avg_loss_bottom_decile': np.float64(25.48681), 'test_avg_loss_top_decile': np.float64(43.127705), 'test_avg_loss_min': np.float64(22.96952), 'test_avg_loss_max': np.float64(43.127705), 'test_avg_loss_bottom10%': np.float64(22.96952), 'test_avg_loss_top10%': np.float64(43.127705), 'test_avg_loss_cos1': np.float64(0.986536), 'test_avg_loss_entropy': np.float64(2.289348), 'test_loss_std': np.float64(26443.540502), 'test_loss_bottom_decile': np.float64(132123.623657), 'test_loss_top_decile': np.float64(223574.022827), 'test_loss_min': np.float64(119073.98999), 'test_loss_max': np.float64(223574.022827), 'test_loss_bottom10%': np.float64(119073.98999), 'test_loss_top10%': np.float64(223574.022827), 'test_loss_cos1': np.float64(0.986536), 'test_loss_entropy': np.float64(2.289348), 'val_avg_loss_std': np.float64(4.297426), 'val_avg_loss_bottom_decile': np.float64(25.066208), 'val_avg_loss_top_decile': np.float64(37.66357), 'val_avg_loss_min': np.float64(23.207687), 'val_avg_loss_max': np.float64(37.66357), 'val_avg_loss_bottom10%': np.float64(23.207687), 'val_avg_loss_top10%': np.float64(37.66357), 'val_avg_loss_cos1': np.float64(0.990265), 'val_avg_loss_entropy': np.float64(2.292578), 'val_loss_std': np.float64(22277.8566), 'val_loss_bottom_decile': np.float64(129943.222595), 'val_loss_top_decile': np.float64(195247.944824), 'val_loss_min': np.float64(120308.647522), 'val_loss_max': np.float64(195247.944824), 'val_loss_bottom10%': np.float64(120308.647522), 'val_loss_top10%': np.float64(195247.944824), 'val_loss_cos1': np.float64(0.990265), 'val_loss_entropy': np.float64(2.292578)}} 2024-11-14 14:46:41,147 (server:353) INFO: Server: Starting evaluation at the end of round 26. 2024-11-14 14:46:41,148 (server:359) INFO: ----------- Starting a new training round (Round #27) ------------- 2024-11-14 14:49:18,287 (client:354) INFO: {'Role': 'Client #9', 'Round': 27, 'Results_raw': {'train_loss': 30.55825, 'val_loss': 27.838204, 'test_loss': 27.106453}} 2024-11-14 14:50:17,127 (client:354) INFO: {'Role': 'Client #8', 'Round': 27, 'Results_raw': {'train_loss': 20.918455, 'val_loss': 20.173419, 'test_loss': 21.143353}} 2024-11-14 14:51:15,784 (client:354) INFO: {'Role': 'Client #4', 'Round': 27, 'Results_raw': {'train_loss': 23.611169, 'val_loss': 21.755587, 'test_loss': 23.245802}} 2024-11-14 14:52:14,647 (client:354) INFO: {'Role': 'Client #1', 'Round': 27, 'Results_raw': {'train_loss': 17.796331, 'val_loss': 16.32212, 'test_loss': 18.489493}} 2024-11-14 14:53:12,847 (client:354) INFO: {'Role': 'Client #2', 'Round': 27, 'Results_raw': {'train_loss': 13.583738, 'val_loss': 12.776445, 'test_loss': 13.660801}} 2024-11-14 14:54:11,095 (client:354) INFO: {'Role': 'Client #3', 'Round': 27, 'Results_raw': {'train_loss': 17.029432, 'val_loss': 17.156572, 'test_loss': 20.147411}} 2024-11-14 14:55:14,497 (client:354) INFO: {'Role': 'Client #7', 'Round': 27, 'Results_raw': {'train_loss': 23.611257, 'val_loss': 22.780475, 'test_loss': 23.2923}} 2024-11-14 14:56:11,790 (client:354) INFO: {'Role': 'Client #10', 'Round': 27, 'Results_raw': {'train_loss': 22.811559, 'val_loss': 22.151668, 'test_loss': 23.616245}} 2024-11-14 14:57:08,484 (client:354) INFO: {'Role': 'Client #6', 'Round': 27, 'Results_raw': {'train_loss': 23.727112, 'val_loss': 21.973909, 'test_loss': 23.083413}} 2024-11-14 14:58:04,869 (client:354) INFO: {'Role': 'Client #5', 'Round': 27, 'Results_raw': {'train_loss': 24.565759, 'val_loss': 25.162704, 'test_loss': 32.986931}} 2024-11-14 14:58:04,872 (server:615) INFO: {'Role': 'Server #', 'Round': 26, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.600367), 'test_loss': np.float64(158632.304474), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.407806), 'val_loss': np.float64(157634.065491), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.600367), 'test_loss': np.float64(158632.304474), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.407806), 'val_loss': np.float64(157634.065491), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.155838), 'test_avg_loss_bottom_decile': np.float64(25.226264), 'test_avg_loss_top_decile': np.float64(43.046383), 'test_avg_loss_min': np.float64(22.734579), 'test_avg_loss_max': np.float64(43.046383), 'test_avg_loss_bottom10%': np.float64(22.734579), 'test_avg_loss_top10%': np.float64(43.046383), 'test_avg_loss_cos1': np.float64(0.986101), 'test_avg_loss_entropy': np.float64(2.288909), 'test_loss_std': np.float64(26727.865423), 'test_loss_bottom_decile': np.float64(130772.953003), 'test_loss_top_decile': np.float64(223152.448914), 'test_loss_min': np.float64(117856.057068), 'test_loss_max': np.float64(223152.448914), 'test_loss_bottom10%': np.float64(117856.057068), 'test_loss_top10%': np.float64(223152.448914), 'test_loss_cos1': np.float64(0.986101), 'test_loss_entropy': np.float64(2.288909), 'val_avg_loss_std': np.float64(4.362164), 'val_avg_loss_bottom_decile': np.float64(24.795394), 'val_avg_loss_top_decile': np.float64(37.547759), 'val_avg_loss_min': np.float64(22.99053), 'val_avg_loss_max': np.float64(37.547759), 'val_avg_loss_bottom10%': np.float64(22.99053), 'val_avg_loss_top10%': np.float64(37.547759), 'val_avg_loss_cos1': np.float64(0.989866), 'val_avg_loss_entropy': np.float64(2.292161), 'val_loss_std': np.float64(22613.455726), 'val_loss_bottom_decile': np.float64(128539.322815), 'val_loss_top_decile': np.float64(194647.581543), 'val_loss_min': np.float64(119182.908386), 'val_loss_max': np.float64(194647.581543), 'val_loss_bottom10%': np.float64(119182.908386), 'val_loss_top10%': np.float64(194647.581543), 'val_loss_cos1': np.float64(0.989866), 'val_loss_entropy': np.float64(2.292161)}} 2024-11-14 14:58:04,919 (server:353) INFO: Server: Starting evaluation at the end of round 27. 2024-11-14 14:58:04,920 (server:359) INFO: ----------- Starting a new training round (Round #28) ------------- 2024-11-14 15:00:38,762 (client:354) INFO: {'Role': 'Client #8', 'Round': 28, 'Results_raw': {'train_loss': 20.885458, 'val_loss': 20.215506, 'test_loss': 21.028436}} 2024-11-14 15:01:36,414 (client:354) INFO: {'Role': 'Client #9', 'Round': 28, 'Results_raw': {'train_loss': 30.519301, 'val_loss': 27.83715, 'test_loss': 27.051421}} 2024-11-14 15:02:35,436 (client:354) INFO: {'Role': 'Client #3', 'Round': 28, 'Results_raw': {'train_loss': 16.993466, 'val_loss': 17.088514, 'test_loss': 19.879395}} 2024-11-14 15:03:34,298 (client:354) INFO: {'Role': 'Client #4', 'Round': 28, 'Results_raw': {'train_loss': 23.582484, 'val_loss': 21.691021, 'test_loss': 23.125339}} 2024-11-14 15:04:35,844 (client:354) INFO: {'Role': 'Client #6', 'Round': 28, 'Results_raw': {'train_loss': 23.645799, 'val_loss': 21.972546, 'test_loss': 23.308734}} 2024-11-14 15:05:34,202 (client:354) INFO: {'Role': 'Client #1', 'Round': 28, 'Results_raw': {'train_loss': 17.697877, 'val_loss': 16.345039, 'test_loss': 18.413106}} 2024-11-14 15:06:32,251 (client:354) INFO: {'Role': 'Client #2', 'Round': 28, 'Results_raw': {'train_loss': 13.57116, 'val_loss': 12.694949, 'test_loss': 13.542007}} 2024-11-14 15:07:30,560 (client:354) INFO: {'Role': 'Client #10', 'Round': 28, 'Results_raw': {'train_loss': 22.783262, 'val_loss': 22.490911, 'test_loss': 23.742096}} 2024-11-14 15:08:30,345 (client:354) INFO: {'Role': 'Client #7', 'Round': 28, 'Results_raw': {'train_loss': 23.631324, 'val_loss': 22.741205, 'test_loss': 23.207079}} 2024-11-14 15:09:28,489 (client:354) INFO: {'Role': 'Client #5', 'Round': 28, 'Results_raw': {'train_loss': 24.483169, 'val_loss': 25.257748, 'test_loss': 33.624927}} 2024-11-14 15:09:28,493 (server:615) INFO: {'Role': 'Server #', 'Round': 27, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.642271), 'test_loss': np.float64(158849.534406), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.434108), 'val_loss': np.float64(157770.417474), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.642271), 'test_loss': np.float64(158849.534406), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.434108), 'val_loss': np.float64(157770.417474), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.172377), 'test_avg_loss_bottom_decile': np.float64(25.244105), 'test_avg_loss_top_decile': np.float64(43.205765), 'test_avg_loss_min': np.float64(22.762423), 'test_avg_loss_max': np.float64(43.205765), 'test_avg_loss_bottom10%': np.float64(22.762423), 'test_avg_loss_top10%': np.float64(43.205765), 'test_avg_loss_cos1': np.float64(0.986051), 'test_avg_loss_entropy': np.float64(2.288876), 'test_loss_std': np.float64(26813.604223), 'test_loss_bottom_decile': np.float64(130865.439087), 'test_loss_top_decile': np.float64(223978.684265), 'test_loss_min': np.float64(118000.401428), 'test_loss_max': np.float64(223978.684265), 'test_loss_bottom10%': np.float64(118000.401428), 'test_loss_top10%': np.float64(223978.684265), 'test_loss_cos1': np.float64(0.986051), 'test_loss_entropy': np.float64(2.288876), 'val_avg_loss_std': np.float64(4.350195), 'val_avg_loss_bottom_decile': np.float64(24.798959), 'val_avg_loss_top_decile': np.float64(37.673885), 'val_avg_loss_min': np.float64(22.983811), 'val_avg_loss_max': np.float64(37.673885), 'val_avg_loss_bottom10%': np.float64(22.983811), 'val_avg_loss_top10%': np.float64(37.673885), 'val_avg_loss_cos1': np.float64(0.989938), 'val_avg_loss_entropy': np.float64(2.292234), 'val_loss_std': np.float64(22551.411651), 'val_loss_bottom_decile': np.float64(128557.802673), 'val_loss_top_decile': np.float64(195301.417542), 'val_loss_min': np.float64(119148.077332), 'val_loss_max': np.float64(195301.417542), 'val_loss_bottom10%': np.float64(119148.077332), 'val_loss_top10%': np.float64(195301.417542), 'val_loss_cos1': np.float64(0.989938), 'val_loss_entropy': np.float64(2.292234)}} 2024-11-14 15:09:28,532 (server:353) INFO: Server: Starting evaluation at the end of round 28. 2024-11-14 15:09:28,532 (server:359) INFO: ----------- Starting a new training round (Round #29) ------------- 2024-11-14 15:12:01,023 (client:354) INFO: {'Role': 'Client #1', 'Round': 29, 'Results_raw': {'train_loss': 17.670864, 'val_loss': 16.402078, 'test_loss': 18.505223}} 2024-11-14 15:12:59,307 (client:354) INFO: {'Role': 'Client #5', 'Round': 29, 'Results_raw': {'train_loss': 24.438531, 'val_loss': 25.263141, 'test_loss': 33.030482}} 2024-11-14 15:13:58,718 (client:354) INFO: {'Role': 'Client #7', 'Round': 29, 'Results_raw': {'train_loss': 23.629355, 'val_loss': 22.775647, 'test_loss': 23.394977}} 2024-11-14 15:14:55,414 (client:354) INFO: {'Role': 'Client #6', 'Round': 29, 'Results_raw': {'train_loss': 23.615986, 'val_loss': 21.962027, 'test_loss': 23.3349}} 2024-11-14 15:15:51,911 (client:354) INFO: {'Role': 'Client #3', 'Round': 29, 'Results_raw': {'train_loss': 16.953926, 'val_loss': 17.061995, 'test_loss': 20.03403}} 2024-11-14 15:16:47,437 (client:354) INFO: {'Role': 'Client #4', 'Round': 29, 'Results_raw': {'train_loss': 23.554181, 'val_loss': 21.505926, 'test_loss': 23.201732}} 2024-11-14 15:17:42,901 (client:354) INFO: {'Role': 'Client #9', 'Round': 29, 'Results_raw': {'train_loss': 30.488811, 'val_loss': 27.909654, 'test_loss': 27.307827}} 2024-11-14 15:18:38,861 (client:354) INFO: {'Role': 'Client #10', 'Round': 29, 'Results_raw': {'train_loss': 22.757876, 'val_loss': 22.232901, 'test_loss': 23.913911}} 2024-11-14 15:19:35,401 (client:354) INFO: {'Role': 'Client #2', 'Round': 29, 'Results_raw': {'train_loss': 13.538275, 'val_loss': 12.598776, 'test_loss': 13.279189}} 2024-11-14 15:20:31,666 (client:354) INFO: {'Role': 'Client #8', 'Round': 29, 'Results_raw': {'train_loss': 20.885473, 'val_loss': 20.097447, 'test_loss': 21.147227}} 2024-11-14 15:20:31,671 (server:615) INFO: {'Role': 'Server #', 'Round': 28, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.484981), 'test_loss': np.float64(158034.141583), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.295182), 'val_loss': np.float64(157050.220978), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.484981), 'test_loss': np.float64(158034.141583), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.295182), 'val_loss': np.float64(157050.220978), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.106244), 'test_avg_loss_bottom_decile': np.float64(25.106477), 'test_avg_loss_top_decile': np.float64(42.957374), 'test_avg_loss_min': np.float64(22.853996), 'test_avg_loss_max': np.float64(42.957374), 'test_avg_loss_bottom10%': np.float64(22.853996), 'test_avg_loss_top10%': np.float64(42.957374), 'test_avg_loss_cos1': np.float64(0.98626), 'test_avg_loss_entropy': np.float64(2.289114), 'test_loss_std': np.float64(26470.766831), 'test_loss_bottom_decile': np.float64(130151.975342), 'test_loss_top_decile': np.float64(222691.026428), 'test_loss_min': np.float64(118475.114319), 'test_loss_max': np.float64(222691.026428), 'test_loss_bottom10%': np.float64(118475.114319), 'test_loss_top10%': np.float64(222691.026428), 'test_loss_cos1': np.float64(0.98626), 'test_loss_entropy': np.float64(2.289114), 'val_avg_loss_std': np.float64(4.261147), 'val_avg_loss_bottom_decile': np.float64(24.68135), 'val_avg_loss_top_decile': np.float64(37.366857), 'val_avg_loss_min': np.float64(23.12345), 'val_avg_loss_max': np.float64(37.366857), 'val_avg_loss_bottom10%': np.float64(23.12345), 'val_avg_loss_top10%': np.float64(37.366857), 'val_avg_loss_cos1': np.float64(0.990253), 'val_avg_loss_entropy': np.float64(2.292575), 'val_loss_std': np.float64(22089.788088), 'val_loss_bottom_decile': np.float64(127948.118347), 'val_loss_top_decile': np.float64(193709.7854), 'val_loss_min': np.float64(119871.963196), 'val_loss_max': np.float64(193709.7854), 'val_loss_bottom10%': np.float64(119871.963196), 'val_loss_top10%': np.float64(193709.7854), 'val_loss_cos1': np.float64(0.990253), 'val_loss_entropy': np.float64(2.292575)}} 2024-11-14 15:20:31,713 (server:353) INFO: Server: Starting evaluation at the end of round 29. 2024-11-14 15:20:31,714 (server:359) INFO: ----------- Starting a new training round (Round #30) ------------- 2024-11-14 15:23:02,765 (client:354) INFO: {'Role': 'Client #7', 'Round': 30, 'Results_raw': {'train_loss': 23.531054, 'val_loss': 22.734091, 'test_loss': 23.35022}} 2024-11-14 15:24:01,605 (client:354) INFO: {'Role': 'Client #6', 'Round': 30, 'Results_raw': {'train_loss': 23.621573, 'val_loss': 21.976964, 'test_loss': 23.155131}} 2024-11-14 15:24:58,450 (client:354) INFO: {'Role': 'Client #10', 'Round': 30, 'Results_raw': {'train_loss': 22.696145, 'val_loss': 22.220735, 'test_loss': 23.813324}} 2024-11-14 15:25:54,961 (client:354) INFO: {'Role': 'Client #8', 'Round': 30, 'Results_raw': {'train_loss': 20.833316, 'val_loss': 20.1016, 'test_loss': 21.041037}} 2024-11-14 15:26:51,955 (client:354) INFO: {'Role': 'Client #3', 'Round': 30, 'Results_raw': {'train_loss': 16.929797, 'val_loss': 17.195638, 'test_loss': 20.152784}} 2024-11-14 15:27:52,273 (client:354) INFO: {'Role': 'Client #2', 'Round': 30, 'Results_raw': {'train_loss': 13.548414, 'val_loss': 12.759124, 'test_loss': 13.562923}} 2024-11-14 15:28:52,950 (client:354) INFO: {'Role': 'Client #5', 'Round': 30, 'Results_raw': {'train_loss': 24.428342, 'val_loss': 25.179475, 'test_loss': 33.691897}} 2024-11-14 15:29:53,531 (client:354) INFO: {'Role': 'Client #4', 'Round': 30, 'Results_raw': {'train_loss': 23.483858, 'val_loss': 21.594796, 'test_loss': 23.185608}} 2024-11-14 15:30:58,383 (client:354) INFO: {'Role': 'Client #9', 'Round': 30, 'Results_raw': {'train_loss': 30.429867, 'val_loss': 27.707934, 'test_loss': 27.060438}} 2024-11-14 15:32:00,201 (client:354) INFO: {'Role': 'Client #1', 'Round': 30, 'Results_raw': {'train_loss': 17.679569, 'val_loss': 16.248294, 'test_loss': 18.190889}} 2024-11-14 15:32:00,204 (server:615) INFO: {'Role': 'Server #', 'Round': 29, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.345987), 'test_loss': np.float64(157313.595203), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.124852), 'val_loss': np.float64(156167.230334), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.345987), 'test_loss': np.float64(157313.595203), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.124852), 'val_loss': np.float64(156167.230334), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.082228), 'test_avg_loss_bottom_decile': np.float64(25.035958), 'test_avg_loss_top_decile': np.float64(42.764401), 'test_avg_loss_min': np.float64(22.723719), 'test_avg_loss_max': np.float64(42.764401), 'test_avg_loss_bottom10%': np.float64(22.723719), 'test_avg_loss_top10%': np.float64(42.764401), 'test_avg_loss_cos1': np.float64(0.986264), 'test_avg_loss_entropy': np.float64(2.289118), 'test_loss_std': np.float64(26346.268795), 'test_loss_bottom_decile': np.float64(129786.404297), 'test_loss_top_decile': np.float64(221690.652893), 'test_loss_min': np.float64(117799.761841), 'test_loss_max': np.float64(221690.652893), 'test_loss_bottom10%': np.float64(117799.761841), 'test_loss_top10%': np.float64(221690.652893), 'test_loss_cos1': np.float64(0.986264), 'test_loss_entropy': np.float64(2.289118), 'val_avg_loss_std': np.float64(4.217295), 'val_avg_loss_bottom_decile': np.float64(24.594831), 'val_avg_loss_top_decile': np.float64(37.108627), 'val_avg_loss_min': np.float64(22.986212), 'val_avg_loss_max': np.float64(37.108627), 'val_avg_loss_bottom10%': np.float64(22.986212), 'val_avg_loss_top10%': np.float64(37.108627), 'val_avg_loss_cos1': np.float64(0.990343), 'val_avg_loss_entropy': np.float64(2.292666), 'val_loss_std': np.float64(21862.455177), 'val_loss_bottom_decile': np.float64(127499.603821), 'val_loss_top_decile': np.float64(192371.120605), 'val_loss_min': np.float64(119160.52478), 'val_loss_max': np.float64(192371.120605), 'val_loss_bottom10%': np.float64(119160.52478), 'val_loss_top10%': np.float64(192371.120605), 'val_loss_cos1': np.float64(0.990343), 'val_loss_entropy': np.float64(2.292666)}} 2024-11-14 15:32:00,238 (server:353) INFO: Server: Starting evaluation at the end of round 30. 2024-11-14 15:32:00,238 (server:359) INFO: ----------- Starting a new training round (Round #31) ------------- 2024-11-14 15:34:34,919 (client:354) INFO: {'Role': 'Client #4', 'Round': 31, 'Results_raw': {'train_loss': 23.477187, 'val_loss': 21.864759, 'test_loss': 23.502289}} 2024-11-14 15:35:31,877 (client:354) INFO: {'Role': 'Client #2', 'Round': 31, 'Results_raw': {'train_loss': 13.517959, 'val_loss': 12.711717, 'test_loss': 13.593221}} 2024-11-14 15:36:27,781 (client:354) INFO: {'Role': 'Client #5', 'Round': 31, 'Results_raw': {'train_loss': 24.362816, 'val_loss': 25.015644, 'test_loss': 33.142705}} 2024-11-14 15:37:24,359 (client:354) INFO: {'Role': 'Client #10', 'Round': 31, 'Results_raw': {'train_loss': 22.700126, 'val_loss': 22.186027, 'test_loss': 23.951207}} 2024-11-14 15:38:23,629 (client:354) INFO: {'Role': 'Client #3', 'Round': 31, 'Results_raw': {'train_loss': 16.926873, 'val_loss': 17.293764, 'test_loss': 20.643372}} 2024-11-14 15:39:20,054 (client:354) INFO: {'Role': 'Client #9', 'Round': 31, 'Results_raw': {'train_loss': 30.377488, 'val_loss': 27.569128, 'test_loss': 26.79292}} 2024-11-14 15:40:16,148 (client:354) INFO: {'Role': 'Client #1', 'Round': 31, 'Results_raw': {'train_loss': 17.60998, 'val_loss': 16.277459, 'test_loss': 18.275819}} 2024-11-14 15:41:12,538 (client:354) INFO: {'Role': 'Client #6', 'Round': 31, 'Results_raw': {'train_loss': 23.597926, 'val_loss': 21.892413, 'test_loss': 23.616695}} 2024-11-14 15:42:12,445 (client:354) INFO: {'Role': 'Client #7', 'Round': 31, 'Results_raw': {'train_loss': 23.535816, 'val_loss': 22.736908, 'test_loss': 23.502829}} 2024-11-14 15:43:12,695 (client:354) INFO: {'Role': 'Client #8', 'Round': 31, 'Results_raw': {'train_loss': 20.807454, 'val_loss': 20.125836, 'test_loss': 21.158435}} 2024-11-14 15:43:12,698 (server:615) INFO: {'Role': 'Server #', 'Round': 30, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.339011), 'test_loss': np.float64(157277.431616), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.101984), 'val_loss': np.float64(156048.683966), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.339011), 'test_loss': np.float64(157277.431616), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.101984), 'val_loss': np.float64(156048.683966), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.042483), 'test_avg_loss_bottom_decile': np.float64(25.049846), 'test_avg_loss_top_decile': np.float64(42.776153), 'test_avg_loss_min': np.float64(22.99692), 'test_avg_loss_max': np.float64(42.776153), 'test_avg_loss_bottom10%': np.float64(22.99692), 'test_avg_loss_top10%': np.float64(42.776153), 'test_avg_loss_cos1': np.float64(0.986468), 'test_avg_loss_entropy': np.float64(2.28937), 'test_loss_std': np.float64(26140.233818), 'test_loss_bottom_decile': np.float64(129858.40094), 'test_loss_top_decile': np.float64(221751.576843), 'test_loss_min': np.float64(119216.034058), 'test_loss_max': np.float64(221751.576843), 'test_loss_bottom10%': np.float64(119216.034058), 'test_loss_top10%': np.float64(221751.576843), 'test_loss_cos1': np.float64(0.986468), 'test_loss_entropy': np.float64(2.28937), 'val_avg_loss_std': np.float64(4.157606), 'val_avg_loss_bottom_decile': np.float64(24.604171), 'val_avg_loss_top_decile': np.float64(37.092572), 'val_avg_loss_min': np.float64(23.246208), 'val_avg_loss_max': np.float64(37.092572), 'val_avg_loss_bottom10%': np.float64(23.246208), 'val_avg_loss_top10%': np.float64(37.092572), 'val_avg_loss_cos1': np.float64(0.990596), 'val_avg_loss_entropy': np.float64(2.292961), 'val_loss_std': np.float64(21553.027006), 'val_loss_bottom_decile': np.float64(127548.022095), 'val_loss_top_decile': np.float64(192287.891174), 'val_loss_min': np.float64(120508.341797), 'val_loss_max': np.float64(192287.891174), 'val_loss_bottom10%': np.float64(120508.341797), 'val_loss_top10%': np.float64(192287.891174), 'val_loss_cos1': np.float64(0.990596), 'val_loss_entropy': np.float64(2.292961)}} 2024-11-14 15:43:12,747 (server:353) INFO: Server: Starting evaluation at the end of round 31. 2024-11-14 15:43:12,748 (server:359) INFO: ----------- Starting a new training round (Round #32) ------------- 2024-11-14 15:45:43,899 (client:354) INFO: {'Role': 'Client #5', 'Round': 32, 'Results_raw': {'train_loss': 24.348793, 'val_loss': 25.112327, 'test_loss': 33.015154}} 2024-11-14 15:46:42,622 (client:354) INFO: {'Role': 'Client #10', 'Round': 32, 'Results_raw': {'train_loss': 22.65456, 'val_loss': 22.278626, 'test_loss': 23.768215}} 2024-11-14 15:47:42,109 (client:354) INFO: {'Role': 'Client #3', 'Round': 32, 'Results_raw': {'train_loss': 16.899002, 'val_loss': 16.965359, 'test_loss': 19.895334}} 2024-11-14 15:48:44,637 (client:354) INFO: {'Role': 'Client #2', 'Round': 32, 'Results_raw': {'train_loss': 13.48056, 'val_loss': 12.725454, 'test_loss': 13.482592}} 2024-11-14 15:49:41,387 (client:354) INFO: {'Role': 'Client #7', 'Round': 32, 'Results_raw': {'train_loss': 23.472532, 'val_loss': 23.059069, 'test_loss': 23.695969}} 2024-11-14 15:50:36,449 (client:354) INFO: {'Role': 'Client #8', 'Round': 32, 'Results_raw': {'train_loss': 20.776629, 'val_loss': 20.121282, 'test_loss': 21.173372}} 2024-11-14 15:51:30,578 (client:354) INFO: {'Role': 'Client #9', 'Round': 32, 'Results_raw': {'train_loss': 30.34412, 'val_loss': 27.671831, 'test_loss': 27.097894}} 2024-11-14 15:52:26,303 (client:354) INFO: {'Role': 'Client #1', 'Round': 32, 'Results_raw': {'train_loss': 17.619456, 'val_loss': 16.230224, 'test_loss': 18.332776}} 2024-11-14 15:53:19,947 (client:354) INFO: {'Role': 'Client #4', 'Round': 32, 'Results_raw': {'train_loss': 23.466576, 'val_loss': 21.535975, 'test_loss': 23.114169}} 2024-11-14 15:54:14,406 (client:354) INFO: {'Role': 'Client #6', 'Round': 32, 'Results_raw': {'train_loss': 23.507378, 'val_loss': 21.779012, 'test_loss': 23.260023}} 2024-11-14 15:54:14,409 (server:615) INFO: {'Role': 'Server #', 'Round': 31, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.325327), 'test_loss': np.float64(157206.493262), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.076043), 'val_loss': np.float64(155914.208826), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.325327), 'test_loss': np.float64(157206.493262), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.076043), 'val_loss': np.float64(155914.208826), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.098601), 'test_avg_loss_bottom_decile': np.float64(24.949871), 'test_avg_loss_top_decile': np.float64(42.888403), 'test_avg_loss_min': np.float64(22.868098), 'test_avg_loss_max': np.float64(42.888403), 'test_avg_loss_bottom10%': np.float64(22.868098), 'test_avg_loss_top10%': np.float64(42.888403), 'test_avg_loss_cos1': np.float64(0.986159), 'test_avg_loss_entropy': np.float64(2.289058), 'test_loss_std': np.float64(26431.148918), 'test_loss_bottom_decile': np.float64(129340.13208), 'test_loss_top_decile': np.float64(222333.482727), 'test_loss_min': np.float64(118548.220886), 'test_loss_max': np.float64(222333.482727), 'test_loss_bottom10%': np.float64(118548.220886), 'test_loss_top10%': np.float64(222333.482727), 'test_loss_cos1': np.float64(0.986159), 'test_loss_entropy': np.float64(2.289058), 'val_avg_loss_std': np.float64(4.208144), 'val_avg_loss_bottom_decile': np.float64(24.465616), 'val_avg_loss_top_decile': np.float64(37.101177), 'val_avg_loss_min': np.float64(23.094329), 'val_avg_loss_max': np.float64(37.101177), 'val_avg_loss_bottom10%': np.float64(23.094329), 'val_avg_loss_top10%': np.float64(37.101177), 'val_avg_loss_cos1': np.float64(0.990353), 'val_avg_loss_entropy': np.float64(2.292694), 'val_loss_std': np.float64(21815.016344), 'val_loss_bottom_decile': np.float64(126829.752197), 'val_loss_top_decile': np.float64(192332.503174), 'val_loss_min': np.float64(119721.000427), 'val_loss_max': np.float64(192332.503174), 'val_loss_bottom10%': np.float64(119721.000427), 'val_loss_top10%': np.float64(192332.503174), 'val_loss_cos1': np.float64(0.990353), 'val_loss_entropy': np.float64(2.292694)}} 2024-11-14 15:54:14,447 (server:353) INFO: Server: Starting evaluation at the end of round 32. 2024-11-14 15:54:14,448 (server:359) INFO: ----------- Starting a new training round (Round #33) ------------- 2024-11-14 15:56:41,429 (client:354) INFO: {'Role': 'Client #9', 'Round': 33, 'Results_raw': {'train_loss': 30.308876, 'val_loss': 28.072808, 'test_loss': 27.517705}} 2024-11-14 15:57:34,697 (client:354) INFO: {'Role': 'Client #8', 'Round': 33, 'Results_raw': {'train_loss': 20.734146, 'val_loss': 20.136338, 'test_loss': 21.213525}} 2024-11-14 15:58:28,617 (client:354) INFO: {'Role': 'Client #7', 'Round': 33, 'Results_raw': {'train_loss': 23.432734, 'val_loss': 22.718429, 'test_loss': 23.210363}} 2024-11-14 15:59:23,655 (client:354) INFO: {'Role': 'Client #10', 'Round': 33, 'Results_raw': {'train_loss': 22.599708, 'val_loss': 22.261549, 'test_loss': 23.930997}} 2024-11-14 16:00:20,110 (client:354) INFO: {'Role': 'Client #1', 'Round': 33, 'Results_raw': {'train_loss': 17.567706, 'val_loss': 16.328283, 'test_loss': 18.435303}} 2024-11-14 16:01:17,640 (client:354) INFO: {'Role': 'Client #5', 'Round': 33, 'Results_raw': {'train_loss': 24.26788, 'val_loss': 24.899473, 'test_loss': 33.143808}} 2024-11-14 16:02:13,711 (client:354) INFO: {'Role': 'Client #3', 'Round': 33, 'Results_raw': {'train_loss': 16.860727, 'val_loss': 17.386047, 'test_loss': 20.603037}} 2024-11-14 16:03:09,719 (client:354) INFO: {'Role': 'Client #6', 'Round': 33, 'Results_raw': {'train_loss': 23.475867, 'val_loss': 21.665792, 'test_loss': 23.181887}} 2024-11-14 16:04:07,020 (client:354) INFO: {'Role': 'Client #2', 'Round': 33, 'Results_raw': {'train_loss': 13.4664, 'val_loss': 12.55626, 'test_loss': 13.462449}} 2024-11-14 16:05:04,054 (client:354) INFO: {'Role': 'Client #4', 'Round': 33, 'Results_raw': {'train_loss': 23.404672, 'val_loss': 21.494949, 'test_loss': 23.254176}} 2024-11-14 16:05:04,056 (server:615) INFO: {'Role': 'Server #', 'Round': 32, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.163541), 'test_loss': np.float64(156367.795746), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.921196), 'val_loss': np.float64(155111.479694), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.163541), 'test_loss': np.float64(156367.795746), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.921196), 'val_loss': np.float64(155111.479694), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.108773), 'test_avg_loss_bottom_decile': np.float64(24.802597), 'test_avg_loss_top_decile': np.float64(42.680062), 'test_avg_loss_min': np.float64(22.556806), 'test_avg_loss_max': np.float64(42.680062), 'test_avg_loss_bottom10%': np.float64(22.556806), 'test_avg_loss_top10%': np.float64(42.680062), 'test_avg_loss_cos1': np.float64(0.985958), 'test_avg_loss_entropy': np.float64(2.28883), 'test_loss_std': np.float64(26483.880013), 'test_loss_bottom_decile': np.float64(128576.661987), 'test_loss_top_decile': np.float64(221253.442139), 'test_loss_min': np.float64(116934.481323), 'test_loss_max': np.float64(221253.442139), 'test_loss_bottom10%': np.float64(116934.481323), 'test_loss_top10%': np.float64(221253.442139), 'test_loss_cos1': np.float64(0.985958), 'test_loss_entropy': np.float64(2.28883), 'val_avg_loss_std': np.float64(4.231817), 'val_avg_loss_bottom_decile': np.float64(24.33639), 'val_avg_loss_top_decile': np.float64(36.920621), 'val_avg_loss_min': np.float64(22.784781), 'val_avg_loss_max': np.float64(36.920621), 'val_avg_loss_bottom10%': np.float64(22.784781), 'val_avg_loss_top10%': np.float64(36.920621), 'val_avg_loss_cos1': np.float64(0.990146), 'val_avg_loss_entropy': np.float64(2.292461), 'val_loss_std': np.float64(21937.738613), 'val_loss_bottom_decile': np.float64(126159.845215), 'val_loss_top_decile': np.float64(191396.498169), 'val_loss_min': np.float64(118116.30249), 'val_loss_max': np.float64(191396.498169), 'val_loss_bottom10%': np.float64(118116.30249), 'val_loss_top10%': np.float64(191396.498169), 'val_loss_cos1': np.float64(0.990146), 'val_loss_entropy': np.float64(2.292461)}} 2024-11-14 16:05:04,094 (server:353) INFO: Server: Starting evaluation at the end of round 33. 2024-11-14 16:05:04,094 (server:359) INFO: ----------- Starting a new training round (Round #34) ------------- 2024-11-14 16:07:32,458 (client:354) INFO: {'Role': 'Client #4', 'Round': 34, 'Results_raw': {'train_loss': 23.379416, 'val_loss': 21.617787, 'test_loss': 23.031163}} 2024-11-14 16:08:27,709 (client:354) INFO: {'Role': 'Client #7', 'Round': 34, 'Results_raw': {'train_loss': 23.399815, 'val_loss': 22.725647, 'test_loss': 23.215453}} 2024-11-14 16:09:22,519 (client:354) INFO: {'Role': 'Client #8', 'Round': 34, 'Results_raw': {'train_loss': 20.716724, 'val_loss': 20.136807, 'test_loss': 21.159096}} 2024-11-14 16:10:16,672 (client:354) INFO: {'Role': 'Client #10', 'Round': 34, 'Results_raw': {'train_loss': 22.563919, 'val_loss': 22.311467, 'test_loss': 24.036719}} 2024-11-14 16:11:10,157 (client:354) INFO: {'Role': 'Client #5', 'Round': 34, 'Results_raw': {'train_loss': 24.267192, 'val_loss': 25.220166, 'test_loss': 33.708503}} 2024-11-14 16:12:11,648 (client:354) INFO: {'Role': 'Client #2', 'Round': 34, 'Results_raw': {'train_loss': 13.429666, 'val_loss': 12.602533, 'test_loss': 13.385577}} 2024-11-14 16:13:13,860 (client:354) INFO: {'Role': 'Client #9', 'Round': 34, 'Results_raw': {'train_loss': 30.291793, 'val_loss': 28.033861, 'test_loss': 27.685844}} 2024-11-14 16:14:17,164 (client:354) INFO: {'Role': 'Client #3', 'Round': 34, 'Results_raw': {'train_loss': 16.812187, 'val_loss': 17.12846, 'test_loss': 20.102243}} 2024-11-14 16:15:15,201 (client:354) INFO: {'Role': 'Client #1', 'Round': 34, 'Results_raw': {'train_loss': 17.511103, 'val_loss': 16.323832, 'test_loss': 18.2192}} 2024-11-14 16:16:13,908 (client:354) INFO: {'Role': 'Client #6', 'Round': 34, 'Results_raw': {'train_loss': 23.498098, 'val_loss': 22.020644, 'test_loss': 23.627774}} 2024-11-14 16:16:13,912 (server:615) INFO: {'Role': 'Server #', 'Round': 33, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.242773), 'test_loss': np.float64(156778.536469), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.978121), 'val_loss': np.float64(155406.57843), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.242773), 'test_loss': np.float64(156778.536469), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.978121), 'val_loss': np.float64(155406.57843), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.082205), 'test_avg_loss_bottom_decile': np.float64(24.851842), 'test_avg_loss_top_decile': np.float64(42.824662), 'test_avg_loss_min': np.float64(22.850351), 'test_avg_loss_max': np.float64(42.824662), 'test_avg_loss_bottom10%': np.float64(22.850351), 'test_avg_loss_top10%': np.float64(42.824662), 'test_avg_loss_cos1': np.float64(0.986172), 'test_avg_loss_entropy': np.float64(2.289087), 'test_loss_std': np.float64(26346.148445), 'test_loss_bottom_decile': np.float64(128831.948364), 'test_loss_top_decile': np.float64(222003.049255), 'test_loss_min': np.float64(118456.218872), 'test_loss_max': np.float64(222003.049255), 'test_loss_bottom10%': np.float64(118456.218872), 'test_loss_top10%': np.float64(222003.049255), 'test_loss_cos1': np.float64(0.986172), 'test_loss_entropy': np.float64(2.289087), 'val_avg_loss_std': np.float64(4.170713), 'val_avg_loss_bottom_decile': np.float64(24.379012), 'val_avg_loss_top_decile': np.float64(37.062757), 'val_avg_loss_min': np.float64(23.082365), 'val_avg_loss_max': np.float64(37.062757), 'val_avg_loss_bottom10%': np.float64(23.082365), 'val_avg_loss_top10%': np.float64(37.062757), 'val_avg_loss_cos1': np.float64(0.99046), 'val_avg_loss_entropy': np.float64(2.292813), 'val_loss_std': np.float64(21620.977368), 'val_loss_bottom_decile': np.float64(126380.795898), 'val_loss_top_decile': np.float64(192133.331543), 'val_loss_min': np.float64(119658.979004), 'val_loss_max': np.float64(192133.331543), 'val_loss_bottom10%': np.float64(119658.979004), 'val_loss_top10%': np.float64(192133.331543), 'val_loss_cos1': np.float64(0.99046), 'val_loss_entropy': np.float64(2.292813)}} 2024-11-14 16:16:13,956 (server:353) INFO: Server: Starting evaluation at the end of round 34. 2024-11-14 16:16:13,956 (server:359) INFO: ----------- Starting a new training round (Round #35) ------------- 2024-11-14 16:19:05,758 (client:354) INFO: {'Role': 'Client #3', 'Round': 35, 'Results_raw': {'train_loss': 16.835946, 'val_loss': 16.982084, 'test_loss': 19.990961}} 2024-11-14 16:20:01,835 (client:354) INFO: {'Role': 'Client #9', 'Round': 35, 'Results_raw': {'train_loss': 30.22919, 'val_loss': 27.951243, 'test_loss': 27.552036}} 2024-11-14 16:20:56,333 (client:354) INFO: {'Role': 'Client #2', 'Round': 35, 'Results_raw': {'train_loss': 13.401151, 'val_loss': 12.778218, 'test_loss': 13.429507}} 2024-11-14 16:21:51,665 (client:354) INFO: {'Role': 'Client #8', 'Round': 35, 'Results_raw': {'train_loss': 20.714515, 'val_loss': 20.102695, 'test_loss': 21.062088}} 2024-11-14 16:22:45,989 (client:354) INFO: {'Role': 'Client #5', 'Round': 35, 'Results_raw': {'train_loss': 24.23792, 'val_loss': 24.888138, 'test_loss': 33.364676}} 2024-11-14 16:23:41,580 (client:354) INFO: {'Role': 'Client #4', 'Round': 35, 'Results_raw': {'train_loss': 23.373945, 'val_loss': 21.482388, 'test_loss': 23.015783}} 2024-11-14 16:24:32,562 (client:354) INFO: {'Role': 'Client #6', 'Round': 35, 'Results_raw': {'train_loss': 23.443233, 'val_loss': 21.764195, 'test_loss': 23.171249}} 2024-11-14 16:25:23,996 (client:354) INFO: {'Role': 'Client #7', 'Round': 35, 'Results_raw': {'train_loss': 23.414574, 'val_loss': 22.657229, 'test_loss': 23.280731}} 2024-11-14 16:26:15,504 (client:354) INFO: {'Role': 'Client #10', 'Round': 35, 'Results_raw': {'train_loss': 22.522223, 'val_loss': 22.285771, 'test_loss': 24.045745}} 2024-11-14 16:27:07,101 (client:354) INFO: {'Role': 'Client #1', 'Round': 35, 'Results_raw': {'train_loss': 17.502963, 'val_loss': 16.298263, 'test_loss': 18.402018}} 2024-11-14 16:27:07,104 (server:615) INFO: {'Role': 'Server #', 'Round': 34, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.131103), 'test_loss': np.float64(156199.638074), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.86722), 'val_loss': np.float64(154831.670209), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.131103), 'test_loss': np.float64(156199.638074), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.86722), 'val_loss': np.float64(154831.670209), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.016375), 'test_avg_loss_bottom_decile': np.float64(24.871335), 'test_avg_loss_top_decile': np.float64(42.489687), 'test_avg_loss_min': np.float64(22.724437), 'test_avg_loss_max': np.float64(42.489687), 'test_avg_loss_bottom10%': np.float64(22.724437), 'test_avg_loss_top10%': np.float64(42.489687), 'test_avg_loss_cos1': np.float64(0.986423), 'test_avg_loss_entropy': np.float64(2.28931), 'test_loss_std': np.float64(26004.888431), 'test_loss_bottom_decile': np.float64(128932.998962), 'test_loss_top_decile': np.float64(220266.539978), 'test_loss_min': np.float64(117803.482239), 'test_loss_max': np.float64(220266.539978), 'test_loss_bottom10%': np.float64(117803.482239), 'test_loss_top10%': np.float64(220266.539978), 'test_loss_cos1': np.float64(0.986423), 'test_loss_entropy': np.float64(2.28931), 'val_avg_loss_std': np.float64(4.124142), 'val_avg_loss_bottom_decile': np.float64(24.397509), 'val_avg_loss_top_decile': np.float64(36.720581), 'val_avg_loss_min': np.float64(22.929265), 'val_avg_loss_max': np.float64(36.720581), 'val_avg_loss_bottom10%': np.float64(22.929265), 'val_avg_loss_top10%': np.float64(36.720581), 'val_avg_loss_cos1': np.float64(0.990601), 'val_avg_loss_entropy': np.float64(2.292939), 'val_loss_std': np.float64(21379.549555), 'val_loss_bottom_decile': np.float64(126476.684448), 'val_loss_top_decile': np.float64(190359.492737), 'val_loss_min': np.float64(118865.309875), 'val_loss_max': np.float64(190359.492737), 'val_loss_bottom10%': np.float64(118865.309875), 'val_loss_top10%': np.float64(190359.492737), 'val_loss_cos1': np.float64(0.990601), 'val_loss_entropy': np.float64(2.292939)}} 2024-11-14 16:27:07,136 (server:353) INFO: Server: Starting evaluation at the end of round 35. 2024-11-14 16:27:07,136 (server:359) INFO: ----------- Starting a new training round (Round #36) ------------- 2024-11-14 16:29:28,259 (client:354) INFO: {'Role': 'Client #9', 'Round': 36, 'Results_raw': {'train_loss': 30.215305, 'val_loss': 27.595006, 'test_loss': 26.940835}} 2024-11-14 16:30:19,110 (client:354) INFO: {'Role': 'Client #6', 'Round': 36, 'Results_raw': {'train_loss': 23.423306, 'val_loss': 21.866191, 'test_loss': 23.648467}} 2024-11-14 16:31:10,594 (client:354) INFO: {'Role': 'Client #8', 'Round': 36, 'Results_raw': {'train_loss': 20.701669, 'val_loss': 20.093604, 'test_loss': 20.992147}} 2024-11-14 16:32:02,247 (client:354) INFO: {'Role': 'Client #5', 'Round': 36, 'Results_raw': {'train_loss': 24.197026, 'val_loss': 25.539342, 'test_loss': 33.961646}} 2024-11-14 16:32:53,844 (client:354) INFO: {'Role': 'Client #7', 'Round': 36, 'Results_raw': {'train_loss': 23.353485, 'val_loss': 22.795222, 'test_loss': 23.158502}} 2024-11-14 16:33:45,825 (client:354) INFO: {'Role': 'Client #4', 'Round': 36, 'Results_raw': {'train_loss': 23.317633, 'val_loss': 21.689905, 'test_loss': 23.232633}} 2024-11-14 16:34:37,858 (client:354) INFO: {'Role': 'Client #3', 'Round': 36, 'Results_raw': {'train_loss': 16.77964, 'val_loss': 17.146797, 'test_loss': 20.446931}} 2024-11-14 16:35:29,083 (client:354) INFO: {'Role': 'Client #2', 'Round': 36, 'Results_raw': {'train_loss': 13.442792, 'val_loss': 12.605957, 'test_loss': 13.476092}} 2024-11-14 16:36:19,578 (client:354) INFO: {'Role': 'Client #10', 'Round': 36, 'Results_raw': {'train_loss': 22.496456, 'val_loss': 22.17145, 'test_loss': 23.909659}} 2024-11-14 16:37:09,584 (client:354) INFO: {'Role': 'Client #1', 'Round': 36, 'Results_raw': {'train_loss': 17.47588, 'val_loss': 16.168168, 'test_loss': 18.227138}} 2024-11-14 16:37:09,587 (server:615) INFO: {'Role': 'Server #', 'Round': 35, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.330831), 'test_loss': np.float64(157235.027295), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.047095), 'val_loss': np.float64(155764.138971), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.330831), 'test_loss': np.float64(157235.027295), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(30.047095), 'val_loss': np.float64(155764.138971), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.021971), 'test_avg_loss_bottom_decile': np.float64(24.989615), 'test_avg_loss_top_decile': np.float64(42.65767), 'test_avg_loss_min': np.float64(22.890829), 'test_avg_loss_max': np.float64(42.65767), 'test_avg_loss_bottom10%': np.float64(22.890829), 'test_avg_loss_top10%': np.float64(42.65767), 'test_avg_loss_cos1': np.float64(0.986568), 'test_avg_loss_entropy': np.float64(2.289436), 'test_loss_std': np.float64(26033.897358), 'test_loss_bottom_decile': np.float64(129546.166382), 'test_loss_top_decile': np.float64(221137.36322), 'test_loss_min': np.float64(118666.056213), 'test_loss_max': np.float64(221137.36322), 'test_loss_bottom10%': np.float64(118666.056213), 'test_loss_top10%': np.float64(221137.36322), 'test_loss_cos1': np.float64(0.986568), 'test_loss_entropy': np.float64(2.289436), 'val_avg_loss_std': np.float64(4.119095), 'val_avg_loss_bottom_decile': np.float64(24.510031), 'val_avg_loss_top_decile': np.float64(36.818237), 'val_avg_loss_min': np.float64(23.109604), 'val_avg_loss_max': np.float64(36.818237), 'val_avg_loss_bottom10%': np.float64(23.109604), 'val_avg_loss_top10%': np.float64(36.818237), 'val_avg_loss_cos1': np.float64(0.990734), 'val_avg_loss_entropy': np.float64(2.293071), 'val_loss_std': np.float64(21353.38762), 'val_loss_bottom_decile': np.float64(127060.002686), 'val_loss_top_decile': np.float64(190865.739075), 'val_loss_min': np.float64(119800.184631), 'val_loss_max': np.float64(190865.739075), 'val_loss_bottom10%': np.float64(119800.184631), 'val_loss_top10%': np.float64(190865.739075), 'val_loss_cos1': np.float64(0.990734), 'val_loss_entropy': np.float64(2.293071)}} 2024-11-14 16:37:09,623 (server:353) INFO: Server: Starting evaluation at the end of round 36. 2024-11-14 16:37:09,624 (server:359) INFO: ----------- Starting a new training round (Round #37) ------------- 2024-11-14 16:39:39,625 (client:354) INFO: {'Role': 'Client #9', 'Round': 37, 'Results_raw': {'train_loss': 30.16336, 'val_loss': 27.665372, 'test_loss': 26.923159}} 2024-11-14 16:40:37,354 (client:354) INFO: {'Role': 'Client #8', 'Round': 37, 'Results_raw': {'train_loss': 20.650045, 'val_loss': 20.093065, 'test_loss': 21.051703}} 2024-11-14 16:41:34,608 (client:354) INFO: {'Role': 'Client #1', 'Round': 37, 'Results_raw': {'train_loss': 17.475227, 'val_loss': 16.388196, 'test_loss': 18.539515}} 2024-11-14 16:42:35,413 (client:354) INFO: {'Role': 'Client #2', 'Round': 37, 'Results_raw': {'train_loss': 13.380249, 'val_loss': 12.918841, 'test_loss': 13.571875}} 2024-11-14 16:43:39,734 (client:354) INFO: {'Role': 'Client #4', 'Round': 37, 'Results_raw': {'train_loss': 23.305726, 'val_loss': 21.635062, 'test_loss': 23.105141}} 2024-11-14 16:44:37,755 (client:354) INFO: {'Role': 'Client #5', 'Round': 37, 'Results_raw': {'train_loss': 24.159166, 'val_loss': 25.004829, 'test_loss': 33.180981}} 2024-11-14 16:45:36,424 (client:354) INFO: {'Role': 'Client #3', 'Round': 37, 'Results_raw': {'train_loss': 16.785823, 'val_loss': 17.185384, 'test_loss': 20.295096}} 2024-11-14 16:46:34,944 (client:354) INFO: {'Role': 'Client #6', 'Round': 37, 'Results_raw': {'train_loss': 23.368505, 'val_loss': 21.863444, 'test_loss': 23.381647}} 2024-11-14 16:47:32,516 (client:354) INFO: {'Role': 'Client #10', 'Round': 37, 'Results_raw': {'train_loss': 22.500804, 'val_loss': 22.372188, 'test_loss': 23.650333}} 2024-11-14 16:48:29,967 (client:354) INFO: {'Role': 'Client #7', 'Round': 37, 'Results_raw': {'train_loss': 23.411033, 'val_loss': 22.696146, 'test_loss': 23.384702}} 2024-11-14 16:48:29,970 (server:615) INFO: {'Role': 'Server #', 'Round': 36, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.185644), 'test_loss': np.float64(156482.376868), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.906285), 'val_loss': np.float64(155034.179742), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.185644), 'test_loss': np.float64(156482.376868), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.906285), 'val_loss': np.float64(155034.179742), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.164905), 'test_avg_loss_bottom_decile': np.float64(24.772701), 'test_avg_loss_top_decile': np.float64(42.8395), 'test_avg_loss_min': np.float64(22.43885), 'test_avg_loss_max': np.float64(42.8395), 'test_avg_loss_bottom10%': np.float64(22.43885), 'test_avg_loss_top10%': np.float64(42.8395), 'test_avg_loss_cos1': np.float64(0.985675), 'test_avg_loss_entropy': np.float64(2.288543), 'test_loss_std': np.float64(26774.867441), 'test_loss_bottom_decile': np.float64(128421.684082), 'test_loss_top_decile': np.float64(222079.96759), 'test_loss_min': np.float64(116322.996582), 'test_loss_max': np.float64(222079.96759), 'test_loss_bottom10%': np.float64(116322.996582), 'test_loss_top10%': np.float64(222079.96759), 'test_loss_cos1': np.float64(0.985675), 'test_loss_entropy': np.float64(2.288543), 'val_avg_loss_std': np.float64(4.278887), 'val_avg_loss_bottom_decile': np.float64(24.250538), 'val_avg_loss_top_decile': np.float64(36.985681), 'val_avg_loss_min': np.float64(22.619613), 'val_avg_loss_max': np.float64(36.985681), 'val_avg_loss_bottom10%': np.float64(22.619613), 'val_avg_loss_top10%': np.float64(36.985681), 'val_avg_loss_cos1': np.float64(0.989919), 'val_avg_loss_entropy': np.float64(2.29221), 'val_loss_std': np.float64(22181.748608), 'val_loss_bottom_decile': np.float64(125714.789551), 'val_loss_top_decile': np.float64(191733.772156), 'val_loss_min': np.float64(117260.073975), 'val_loss_max': np.float64(191733.772156), 'val_loss_bottom10%': np.float64(117260.073975), 'val_loss_top10%': np.float64(191733.772156), 'val_loss_cos1': np.float64(0.989919), 'val_loss_entropy': np.float64(2.29221)}} 2024-11-14 16:48:30,017 (server:353) INFO: Server: Starting evaluation at the end of round 37. 2024-11-14 16:48:30,017 (server:359) INFO: ----------- Starting a new training round (Round #38) ------------- 2024-11-14 16:51:05,086 (client:354) INFO: {'Role': 'Client #4', 'Round': 38, 'Results_raw': {'train_loss': 23.2747, 'val_loss': 21.551486, 'test_loss': 23.135859}} 2024-11-14 16:52:05,086 (client:354) INFO: {'Role': 'Client #2', 'Round': 38, 'Results_raw': {'train_loss': 13.365993, 'val_loss': 12.666172, 'test_loss': 13.609093}} 2024-11-14 16:53:04,248 (client:354) INFO: {'Role': 'Client #9', 'Round': 38, 'Results_raw': {'train_loss': 30.213735, 'val_loss': 27.574056, 'test_loss': 26.798729}} 2024-11-14 16:54:04,929 (client:354) INFO: {'Role': 'Client #6', 'Round': 38, 'Results_raw': {'train_loss': 23.368688, 'val_loss': 21.805447, 'test_loss': 23.232093}} 2024-11-14 16:54:57,502 (client:354) INFO: {'Role': 'Client #5', 'Round': 38, 'Results_raw': {'train_loss': 24.142575, 'val_loss': 24.956301, 'test_loss': 33.589133}} 2024-11-14 16:55:53,338 (client:354) INFO: {'Role': 'Client #7', 'Round': 38, 'Results_raw': {'train_loss': 23.301406, 'val_loss': 22.529789, 'test_loss': 23.037751}} 2024-11-14 16:56:51,036 (client:354) INFO: {'Role': 'Client #10', 'Round': 38, 'Results_raw': {'train_loss': 22.445112, 'val_loss': 22.085482, 'test_loss': 23.74351}} 2024-11-14 16:57:47,336 (client:354) INFO: {'Role': 'Client #3', 'Round': 38, 'Results_raw': {'train_loss': 16.77745, 'val_loss': 17.222391, 'test_loss': 20.468349}} 2024-11-14 16:58:45,346 (client:354) INFO: {'Role': 'Client #1', 'Round': 38, 'Results_raw': {'train_loss': 17.415868, 'val_loss': 16.230558, 'test_loss': 18.535415}} 2024-11-14 16:59:36,511 (client:354) INFO: {'Role': 'Client #8', 'Round': 38, 'Results_raw': {'train_loss': 20.649298, 'val_loss': 20.07864, 'test_loss': 21.148154}} 2024-11-14 16:59:36,515 (server:615) INFO: {'Role': 'Server #', 'Round': 37, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.920901), 'test_loss': np.float64(155109.95238), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.643544), 'val_loss': np.float64(153672.131036), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.920901), 'test_loss': np.float64(155109.95238), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.643544), 'val_loss': np.float64(153672.131036), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.09163), 'test_avg_loss_bottom_decile': np.float64(24.574093), 'test_avg_loss_top_decile': np.float64(42.42868), 'test_avg_loss_min': np.float64(22.420282), 'test_avg_loss_max': np.float64(42.42868), 'test_avg_loss_bottom10%': np.float64(22.420282), 'test_avg_loss_top10%': np.float64(42.42868), 'test_avg_loss_cos1': np.float64(0.985828), 'test_avg_loss_entropy': np.float64(2.288719), 'test_loss_std': np.float64(26395.008923), 'test_loss_bottom_decile': np.float64(127392.098267), 'test_loss_top_decile': np.float64(219950.275391), 'test_loss_min': np.float64(116226.742432), 'test_loss_max': np.float64(219950.275391), 'test_loss_bottom10%': np.float64(116226.742432), 'test_loss_top10%': np.float64(219950.275391), 'test_loss_cos1': np.float64(0.985828), 'test_loss_entropy': np.float64(2.288719), 'val_avg_loss_std': np.float64(4.205006), 'val_avg_loss_bottom_decile': np.float64(24.066476), 'val_avg_loss_top_decile': np.float64(36.592941), 'val_avg_loss_min': np.float64(22.627338), 'val_avg_loss_max': np.float64(36.592941), 'val_avg_loss_bottom10%': np.float64(22.627338), 'val_avg_loss_top10%': np.float64(36.592941), 'val_avg_loss_cos1': np.float64(0.990088), 'val_avg_loss_entropy': np.float64(2.292408), 'val_loss_std': np.float64(21798.75306), 'val_loss_bottom_decile': np.float64(124760.609314), 'val_loss_top_decile': np.float64(189697.803772), 'val_loss_min': np.float64(117300.119934), 'val_loss_max': np.float64(189697.803772), 'val_loss_bottom10%': np.float64(117300.119934), 'val_loss_top10%': np.float64(189697.803772), 'val_loss_cos1': np.float64(0.990088), 'val_loss_entropy': np.float64(2.292408)}} 2024-11-14 16:59:36,556 (server:353) INFO: Server: Starting evaluation at the end of round 38. 2024-11-14 16:59:36,556 (server:359) INFO: ----------- Starting a new training round (Round #39) ------------- 2024-11-14 17:01:55,859 (client:354) INFO: {'Role': 'Client #5', 'Round': 39, 'Results_raw': {'train_loss': 24.104114, 'val_loss': 25.00179, 'test_loss': 33.222633}} 2024-11-14 17:02:47,569 (client:354) INFO: {'Role': 'Client #10', 'Round': 39, 'Results_raw': {'train_loss': 22.413262, 'val_loss': 22.265218, 'test_loss': 23.933006}} 2024-11-14 17:03:44,486 (client:354) INFO: {'Role': 'Client #7', 'Round': 39, 'Results_raw': {'train_loss': 23.325649, 'val_loss': 22.666242, 'test_loss': 23.139602}} 2024-11-14 17:04:42,414 (client:354) INFO: {'Role': 'Client #3', 'Round': 39, 'Results_raw': {'train_loss': 16.736755, 'val_loss': 17.357413, 'test_loss': 20.598797}} 2024-11-14 17:05:39,490 (client:354) INFO: {'Role': 'Client #8', 'Round': 39, 'Results_raw': {'train_loss': 20.649589, 'val_loss': 20.027827, 'test_loss': 21.103724}} 2024-11-14 17:06:38,127 (client:354) INFO: {'Role': 'Client #4', 'Round': 39, 'Results_raw': {'train_loss': 23.258065, 'val_loss': 21.473801, 'test_loss': 23.058575}} 2024-11-14 17:07:35,870 (client:354) INFO: {'Role': 'Client #2', 'Round': 39, 'Results_raw': {'train_loss': 13.375407, 'val_loss': 12.595731, 'test_loss': 13.41917}} 2024-11-14 17:08:33,031 (client:354) INFO: {'Role': 'Client #6', 'Round': 39, 'Results_raw': {'train_loss': 23.322301, 'val_loss': 21.897308, 'test_loss': 23.036106}} 2024-11-14 17:09:31,755 (client:354) INFO: {'Role': 'Client #1', 'Round': 39, 'Results_raw': {'train_loss': 17.393994, 'val_loss': 16.396513, 'test_loss': 18.589113}} 2024-11-14 17:10:29,420 (client:354) INFO: {'Role': 'Client #9', 'Round': 39, 'Results_raw': {'train_loss': 30.170036, 'val_loss': 27.533687, 'test_loss': 26.847598}} 2024-11-14 17:10:29,423 (server:615) INFO: {'Role': 'Server #', 'Round': 38, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.086478), 'test_loss': np.float64(155968.303168), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.792103), 'val_loss': np.float64(154442.261151), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.086478), 'test_loss': np.float64(155968.303168), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.792103), 'val_loss': np.float64(154442.261151), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.074814), 'test_avg_loss_bottom_decile': np.float64(24.682877), 'test_avg_loss_top_decile': np.float64(42.603951), 'test_avg_loss_min': np.float64(22.717485), 'test_avg_loss_max': np.float64(42.603951), 'test_avg_loss_bottom10%': np.float64(22.717485), 'test_avg_loss_top10%': np.float64(42.603951), 'test_avg_loss_cos1': np.float64(0.986071), 'test_avg_loss_entropy': np.float64(2.288977), 'test_loss_std': np.float64(26307.835896), 'test_loss_bottom_decile': np.float64(127956.036865), 'test_loss_top_decile': np.float64(220858.882263), 'test_loss_min': np.float64(117767.440002), 'test_loss_max': np.float64(220858.882263), 'test_loss_bottom10%': np.float64(117767.440002), 'test_loss_top10%': np.float64(220858.882263), 'test_loss_cos1': np.float64(0.986071), 'test_loss_entropy': np.float64(2.288977), 'val_avg_loss_std': np.float64(4.149475), 'val_avg_loss_bottom_decile': np.float64(24.186236), 'val_avg_loss_top_decile': np.float64(36.624169), 'val_avg_loss_min': np.float64(22.92343), 'val_avg_loss_max': np.float64(36.624169), 'val_avg_loss_bottom10%': np.float64(22.92343), 'val_avg_loss_top10%': np.float64(36.624169), 'val_avg_loss_cos1': np.float64(0.990439), 'val_avg_loss_entropy': np.float64(2.292776), 'val_loss_std': np.float64(21510.876433), 'val_loss_bottom_decile': np.float64(125381.445679), 'val_loss_top_decile': np.float64(189859.692627), 'val_loss_min': np.float64(118835.059326), 'val_loss_max': np.float64(189859.692627), 'val_loss_bottom10%': np.float64(118835.059326), 'val_loss_top10%': np.float64(189859.692627), 'val_loss_cos1': np.float64(0.990439), 'val_loss_entropy': np.float64(2.292776)}} 2024-11-14 17:10:29,456 (server:353) INFO: Server: Starting evaluation at the end of round 39. 2024-11-14 17:10:29,457 (server:359) INFO: ----------- Starting a new training round (Round #40) ------------- 2024-11-14 17:13:01,446 (client:354) INFO: {'Role': 'Client #3', 'Round': 40, 'Results_raw': {'train_loss': 16.7033, 'val_loss': 16.954697, 'test_loss': 20.210832}} 2024-11-14 17:13:58,641 (client:354) INFO: {'Role': 'Client #5', 'Round': 40, 'Results_raw': {'train_loss': 24.090649, 'val_loss': 24.953413, 'test_loss': 33.607416}} 2024-11-14 17:14:55,674 (client:354) INFO: {'Role': 'Client #4', 'Round': 40, 'Results_raw': {'train_loss': 23.202664, 'val_loss': 21.594568, 'test_loss': 23.275692}} 2024-11-14 17:15:53,266 (client:354) INFO: {'Role': 'Client #6', 'Round': 40, 'Results_raw': {'train_loss': 23.341158, 'val_loss': 21.949054, 'test_loss': 23.583026}} 2024-11-14 17:16:51,177 (client:354) INFO: {'Role': 'Client #7', 'Round': 40, 'Results_raw': {'train_loss': 23.250448, 'val_loss': 22.602576, 'test_loss': 22.982175}} 2024-11-14 17:17:49,720 (client:354) INFO: {'Role': 'Client #2', 'Round': 40, 'Results_raw': {'train_loss': 13.34298, 'val_loss': 12.466361, 'test_loss': 13.342742}} 2024-11-14 17:18:46,937 (client:354) INFO: {'Role': 'Client #9', 'Round': 40, 'Results_raw': {'train_loss': 30.142349, 'val_loss': 27.678776, 'test_loss': 27.015874}} 2024-11-14 17:19:44,894 (client:354) INFO: {'Role': 'Client #1', 'Round': 40, 'Results_raw': {'train_loss': 17.397593, 'val_loss': 16.177533, 'test_loss': 18.217781}} 2024-11-14 17:20:42,094 (client:354) INFO: {'Role': 'Client #8', 'Round': 40, 'Results_raw': {'train_loss': 20.566317, 'val_loss': 19.93808, 'test_loss': 20.931875}} 2024-11-14 17:21:41,283 (client:354) INFO: {'Role': 'Client #10', 'Round': 40, 'Results_raw': {'train_loss': 22.406214, 'val_loss': 22.190934, 'test_loss': 23.938319}} 2024-11-14 17:21:41,286 (server:615) INFO: {'Role': 'Server #', 'Round': 39, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.001002), 'test_loss': np.float64(155525.193024), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.707857), 'val_loss': np.float64(154005.529266), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.001002), 'test_loss': np.float64(155525.193024), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.707857), 'val_loss': np.float64(154005.529266), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.011581), 'test_avg_loss_bottom_decile': np.float64(24.679396), 'test_avg_loss_top_decile': np.float64(42.391461), 'test_avg_loss_min': np.float64(22.665116), 'test_avg_loss_max': np.float64(42.391461), 'test_avg_loss_bottom10%': np.float64(22.665116), 'test_avg_loss_top10%': np.float64(42.391461), 'test_avg_loss_cos1': np.float64(0.986333), 'test_avg_loss_entropy': np.float64(2.289235), 'test_loss_std': np.float64(25980.033591), 'test_loss_bottom_decile': np.float64(127937.988831), 'test_loss_top_decile': np.float64(219757.332031), 'test_loss_min': np.float64(117495.961853), 'test_loss_max': np.float64(219757.332031), 'test_loss_bottom10%': np.float64(117495.961853), 'test_loss_top10%': np.float64(219757.332031), 'test_loss_cos1': np.float64(0.986333), 'test_loss_entropy': np.float64(2.289235), 'val_avg_loss_std': np.float64(4.068508), 'val_avg_loss_bottom_decile': np.float64(24.159455), 'val_avg_loss_top_decile': np.float64(36.398822), 'val_avg_loss_min': np.float64(22.884388), 'val_avg_loss_max': np.float64(36.398822), 'val_avg_loss_bottom10%': np.float64(22.884388), 'val_avg_loss_top10%': np.float64(36.398822), 'val_avg_loss_cos1': np.float64(0.990752), 'val_avg_loss_entropy': np.float64(2.293091), 'val_loss_std': np.float64(21091.143035), 'val_loss_bottom_decile': np.float64(125242.614929), 'val_loss_top_decile': np.float64(188691.495483), 'val_loss_min': np.float64(118632.668396), 'val_loss_max': np.float64(188691.495483), 'val_loss_bottom10%': np.float64(118632.668396), 'val_loss_top10%': np.float64(188691.495483), 'val_loss_cos1': np.float64(0.990752), 'val_loss_entropy': np.float64(2.293091)}} 2024-11-14 17:21:41,320 (server:353) INFO: Server: Starting evaluation at the end of round 40. 2024-11-14 17:21:41,321 (server:359) INFO: ----------- Starting a new training round (Round #41) ------------- 2024-11-14 17:24:15,217 (client:354) INFO: {'Role': 'Client #6', 'Round': 41, 'Results_raw': {'train_loss': 23.276361, 'val_loss': 21.812308, 'test_loss': 23.320821}} 2024-11-14 17:25:12,789 (client:354) INFO: {'Role': 'Client #4', 'Round': 41, 'Results_raw': {'train_loss': 23.205993, 'val_loss': 21.505085, 'test_loss': 23.001855}} 2024-11-14 17:26:14,182 (client:354) INFO: {'Role': 'Client #9', 'Round': 41, 'Results_raw': {'train_loss': 30.047692, 'val_loss': 27.571151, 'test_loss': 26.631164}} 2024-11-14 17:27:11,492 (client:354) INFO: {'Role': 'Client #8', 'Round': 41, 'Results_raw': {'train_loss': 20.54613, 'val_loss': 20.075185, 'test_loss': 21.192982}} 2024-11-14 17:28:08,473 (client:354) INFO: {'Role': 'Client #7', 'Round': 41, 'Results_raw': {'train_loss': 23.268126, 'val_loss': 22.616238, 'test_loss': 23.048695}} 2024-11-14 17:29:06,402 (client:354) INFO: {'Role': 'Client #1', 'Round': 41, 'Results_raw': {'train_loss': 17.333501, 'val_loss': 16.276029, 'test_loss': 18.549316}} 2024-11-14 17:30:06,218 (client:354) INFO: {'Role': 'Client #10', 'Round': 41, 'Results_raw': {'train_loss': 22.399689, 'val_loss': 22.094865, 'test_loss': 23.655029}} 2024-11-14 17:31:08,122 (client:354) INFO: {'Role': 'Client #3', 'Round': 41, 'Results_raw': {'train_loss': 16.659739, 'val_loss': 16.811232, 'test_loss': 20.011961}} 2024-11-14 17:32:10,264 (client:354) INFO: {'Role': 'Client #5', 'Round': 41, 'Results_raw': {'train_loss': 24.032431, 'val_loss': 25.001431, 'test_loss': 33.07872}} 2024-11-14 17:33:16,369 (client:354) INFO: {'Role': 'Client #2', 'Round': 41, 'Results_raw': {'train_loss': 13.335761, 'val_loss': 12.608374, 'test_loss': 13.233779}} 2024-11-14 17:33:16,372 (server:615) INFO: {'Role': 'Server #', 'Round': 40, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.94318), 'test_loss': np.float64(155225.44505), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.656289), 'val_loss': np.float64(153738.202374), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.94318), 'test_loss': np.float64(155225.44505), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.656289), 'val_loss': np.float64(153738.202374), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.228508), 'test_avg_loss_bottom_decile': np.float64(24.449094), 'test_avg_loss_top_decile': np.float64(42.746474), 'test_avg_loss_min': np.float64(22.106715), 'test_avg_loss_max': np.float64(42.746474), 'test_avg_loss_bottom10%': np.float64(22.106715), 'test_avg_loss_top10%': np.float64(42.746474), 'test_avg_loss_cos1': np.float64(0.985095), 'test_avg_loss_entropy': np.float64(2.287967), 'test_loss_std': np.float64(27104.587022), 'test_loss_bottom_decile': np.float64(126744.101196), 'test_loss_top_decile': np.float64(221597.720459), 'test_loss_min': np.float64(114601.210571), 'test_loss_max': np.float64(221597.720459), 'test_loss_bottom10%': np.float64(114601.210571), 'test_loss_top10%': np.float64(221597.720459), 'test_loss_cos1': np.float64(0.985095), 'test_loss_entropy': np.float64(2.287967), 'val_avg_loss_std': np.float64(4.314476), 'val_avg_loss_bottom_decile': np.float64(23.914879), 'val_avg_loss_top_decile': np.float64(36.820376), 'val_avg_loss_min': np.float64(22.334155), 'val_avg_loss_max': np.float64(36.820376), 'val_avg_loss_bottom10%': np.float64(22.334155), 'val_avg_loss_top10%': np.float64(36.820376), 'val_avg_loss_cos1': np.float64(0.989582), 'val_avg_loss_entropy': np.float64(2.291854), 'val_loss_std': np.float64(22366.241909), 'val_loss_bottom_decile': np.float64(123974.732727), 'val_loss_top_decile': np.float64(190876.830811), 'val_loss_min': np.float64(115780.257874), 'val_loss_max': np.float64(190876.830811), 'val_loss_bottom10%': np.float64(115780.257874), 'val_loss_top10%': np.float64(190876.830811), 'val_loss_cos1': np.float64(0.989582), 'val_loss_entropy': np.float64(2.291854)}} 2024-11-14 17:33:16,415 (server:353) INFO: Server: Starting evaluation at the end of round 41. 2024-11-14 17:33:16,415 (server:359) INFO: ----------- Starting a new training round (Round #42) ------------- 2024-11-14 17:35:57,927 (client:354) INFO: {'Role': 'Client #3', 'Round': 42, 'Results_raw': {'train_loss': 16.653144, 'val_loss': 16.868968, 'test_loss': 19.93686}} 2024-11-14 17:37:06,574 (client:354) INFO: {'Role': 'Client #6', 'Round': 42, 'Results_raw': {'train_loss': 23.254502, 'val_loss': 21.743884, 'test_loss': 23.388723}} 2024-11-14 17:38:12,702 (client:354) INFO: {'Role': 'Client #10', 'Round': 42, 'Results_raw': {'train_loss': 22.404349, 'val_loss': 22.275243, 'test_loss': 23.998079}} 2024-11-14 17:39:20,059 (client:354) INFO: {'Role': 'Client #8', 'Round': 42, 'Results_raw': {'train_loss': 20.538388, 'val_loss': 19.95411, 'test_loss': 20.940336}} 2024-11-14 17:40:27,358 (client:354) INFO: {'Role': 'Client #7', 'Round': 42, 'Results_raw': {'train_loss': 23.225776, 'val_loss': 22.67574, 'test_loss': 23.357485}} 2024-11-14 17:41:33,695 (client:354) INFO: {'Role': 'Client #4', 'Round': 42, 'Results_raw': {'train_loss': 23.146408, 'val_loss': 21.654129, 'test_loss': 23.510109}} 2024-11-14 17:42:40,078 (client:354) INFO: {'Role': 'Client #5', 'Round': 42, 'Results_raw': {'train_loss': 24.025546, 'val_loss': 24.951693, 'test_loss': 33.373274}} 2024-11-14 17:43:47,723 (client:354) INFO: {'Role': 'Client #2', 'Round': 42, 'Results_raw': {'train_loss': 13.323269, 'val_loss': 12.515028, 'test_loss': 13.310411}} 2024-11-14 17:44:51,404 (client:354) INFO: {'Role': 'Client #9', 'Round': 42, 'Results_raw': {'train_loss': 30.056394, 'val_loss': 27.854247, 'test_loss': 27.229588}} 2024-11-14 17:45:49,248 (client:354) INFO: {'Role': 'Client #1', 'Round': 42, 'Results_raw': {'train_loss': 17.310937, 'val_loss': 16.076346, 'test_loss': 18.165093}} 2024-11-14 17:45:49,251 (server:615) INFO: {'Role': 'Server #', 'Round': 41, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.828301), 'test_loss': np.float64(154629.913251), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.531438), 'val_loss': np.float64(153090.9742), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.828301), 'test_loss': np.float64(154629.913251), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.531438), 'val_loss': np.float64(153090.9742), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.177883), 'test_avg_loss_bottom_decile': np.float64(24.351579), 'test_avg_loss_top_decile': np.float64(42.546536), 'test_avg_loss_min': np.float64(22.200003), 'test_avg_loss_max': np.float64(42.546536), 'test_avg_loss_bottom10%': np.float64(22.200003), 'test_avg_loss_top10%': np.float64(42.546536), 'test_avg_loss_cos1': np.float64(0.985266), 'test_avg_loss_entropy': np.float64(2.28816), 'test_loss_std': np.float64(26842.145738), 'test_loss_bottom_decile': np.float64(126238.58374), 'test_loss_top_decile': np.float64(220561.240723), 'test_loss_min': np.float64(115084.81665), 'test_loss_max': np.float64(220561.240723), 'test_loss_bottom10%': np.float64(115084.81665), 'test_loss_top10%': np.float64(220561.240723), 'test_loss_cos1': np.float64(0.985266), 'test_loss_entropy': np.float64(2.28816), 'val_avg_loss_std': np.float64(4.261343), 'val_avg_loss_bottom_decile': np.float64(23.805862), 'val_avg_loss_top_decile': np.float64(36.547234), 'val_avg_loss_min': np.float64(22.378731), 'val_avg_loss_max': np.float64(36.547234), 'val_avg_loss_bottom10%': np.float64(22.378731), 'val_avg_loss_top10%': np.float64(36.547234), 'val_avg_loss_cos1': np.float64(0.989749), 'val_avg_loss_entropy': np.float64(2.292035), 'val_loss_std': np.float64(22090.799634), 'val_loss_bottom_decile': np.float64(123409.588196), 'val_loss_top_decile': np.float64(189460.86084), 'val_loss_min': np.float64(116011.343933), 'val_loss_max': np.float64(189460.86084), 'val_loss_bottom10%': np.float64(116011.343933), 'val_loss_top10%': np.float64(189460.86084), 'val_loss_cos1': np.float64(0.989749), 'val_loss_entropy': np.float64(2.292035)}} 2024-11-14 17:45:49,283 (server:353) INFO: Server: Starting evaluation at the end of round 42. 2024-11-14 17:45:49,283 (server:359) INFO: ----------- Starting a new training round (Round #43) ------------- 2024-11-14 17:48:21,605 (client:354) INFO: {'Role': 'Client #9', 'Round': 43, 'Results_raw': {'train_loss': 30.000723, 'val_loss': 27.809063, 'test_loss': 27.263465}} 2024-11-14 17:49:18,776 (client:354) INFO: {'Role': 'Client #3', 'Round': 43, 'Results_raw': {'train_loss': 16.621577, 'val_loss': 17.052527, 'test_loss': 20.118681}} 2024-11-14 17:50:16,226 (client:354) INFO: {'Role': 'Client #2', 'Round': 43, 'Results_raw': {'train_loss': 13.300183, 'val_loss': 12.549555, 'test_loss': 13.328673}} 2024-11-14 17:51:14,179 (client:354) INFO: {'Role': 'Client #1', 'Round': 43, 'Results_raw': {'train_loss': 17.368399, 'val_loss': 16.134351, 'test_loss': 18.353317}} 2024-11-14 17:52:13,738 (client:354) INFO: {'Role': 'Client #10', 'Round': 43, 'Results_raw': {'train_loss': 22.371633, 'val_loss': 22.320666, 'test_loss': 23.801632}} 2024-11-14 17:53:11,309 (client:354) INFO: {'Role': 'Client #6', 'Round': 43, 'Results_raw': {'train_loss': 23.247885, 'val_loss': 21.659145, 'test_loss': 23.051751}} 2024-11-14 17:54:09,398 (client:354) INFO: {'Role': 'Client #8', 'Round': 43, 'Results_raw': {'train_loss': 20.54181, 'val_loss': 19.901297, 'test_loss': 20.799798}} 2024-11-14 17:55:16,408 (client:354) INFO: {'Role': 'Client #7', 'Round': 43, 'Results_raw': {'train_loss': 23.22924, 'val_loss': 22.604428, 'test_loss': 23.16783}} 2024-11-14 17:56:21,599 (client:354) INFO: {'Role': 'Client #4', 'Round': 43, 'Results_raw': {'train_loss': 23.146295, 'val_loss': 21.456529, 'test_loss': 23.214382}} 2024-11-14 17:57:18,504 (client:354) INFO: {'Role': 'Client #5', 'Round': 43, 'Results_raw': {'train_loss': 24.032282, 'val_loss': 24.994942, 'test_loss': 33.667797}} 2024-11-14 17:57:18,507 (server:615) INFO: {'Role': 'Server #', 'Round': 42, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.815345), 'test_loss': np.float64(154562.749609), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.487223), 'val_loss': np.float64(152861.765466), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.815345), 'test_loss': np.float64(154562.749609), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.487223), 'val_loss': np.float64(152861.765466), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.110521), 'test_avg_loss_bottom_decile': np.float64(24.45736), 'test_avg_loss_top_decile': np.float64(42.417822), 'test_avg_loss_min': np.float64(22.254589), 'test_avg_loss_max': np.float64(42.417822), 'test_avg_loss_bottom10%': np.float64(22.254589), 'test_avg_loss_top10%': np.float64(42.417822), 'test_avg_loss_cos1': np.float64(0.985626), 'test_avg_loss_entropy': np.float64(2.288527), 'test_loss_std': np.float64(26492.939241), 'test_loss_bottom_decile': np.float64(126786.953186), 'test_loss_top_decile': np.float64(219893.98999), 'test_loss_min': np.float64(115367.791199), 'test_loss_max': np.float64(219893.98999), 'test_loss_bottom10%': np.float64(115367.791199), 'test_loss_top10%': np.float64(219893.98999), 'test_loss_cos1': np.float64(0.985626), 'test_loss_entropy': np.float64(2.288527), 'val_avg_loss_std': np.float64(4.160817), 'val_avg_loss_bottom_decile': np.float64(23.931812), 'val_avg_loss_top_decile': np.float64(36.370839), 'val_avg_loss_min': np.float64(22.433238), 'val_avg_loss_max': np.float64(36.370839), 'val_avg_loss_bottom10%': np.float64(22.433238), 'val_avg_loss_top10%': np.float64(36.370839), 'val_avg_loss_cos1': np.float64(0.990191), 'val_avg_loss_entropy': np.float64(2.292499), 'val_loss_std': np.float64(21569.675065), 'val_loss_bottom_decile': np.float64(124062.511047), 'val_loss_top_decile': np.float64(188546.426941), 'val_loss_min': np.float64(116293.906921), 'val_loss_max': np.float64(188546.426941), 'val_loss_bottom10%': np.float64(116293.906921), 'val_loss_top10%': np.float64(188546.426941), 'val_loss_cos1': np.float64(0.990191), 'val_loss_entropy': np.float64(2.292499)}} 2024-11-14 17:57:18,545 (server:353) INFO: Server: Starting evaluation at the end of round 43. 2024-11-14 17:57:18,545 (server:359) INFO: ----------- Starting a new training round (Round #44) ------------- 2024-11-14 17:59:42,755 (client:354) INFO: {'Role': 'Client #1', 'Round': 44, 'Results_raw': {'train_loss': 17.317011, 'val_loss': 16.248941, 'test_loss': 18.552517}} 2024-11-14 18:00:33,575 (client:354) INFO: {'Role': 'Client #8', 'Round': 44, 'Results_raw': {'train_loss': 20.536533, 'val_loss': 19.90539, 'test_loss': 20.755453}} 2024-11-14 18:01:23,920 (client:354) INFO: {'Role': 'Client #4', 'Round': 44, 'Results_raw': {'train_loss': 23.130778, 'val_loss': 21.6649, 'test_loss': 23.399385}} 2024-11-14 18:02:14,200 (client:354) INFO: {'Role': 'Client #7', 'Round': 44, 'Results_raw': {'train_loss': 23.17639, 'val_loss': 22.736139, 'test_loss': 23.276842}} 2024-11-14 18:03:05,808 (client:354) INFO: {'Role': 'Client #6', 'Round': 44, 'Results_raw': {'train_loss': 23.220805, 'val_loss': 21.911414, 'test_loss': 23.812249}} 2024-11-14 18:03:56,577 (client:354) INFO: {'Role': 'Client #5', 'Round': 44, 'Results_raw': {'train_loss': 24.011007, 'val_loss': 24.891751, 'test_loss': 33.702095}} 2024-11-14 18:04:47,035 (client:354) INFO: {'Role': 'Client #10', 'Round': 44, 'Results_raw': {'train_loss': 22.332767, 'val_loss': 22.220179, 'test_loss': 23.914187}} 2024-11-14 18:05:37,007 (client:354) INFO: {'Role': 'Client #9', 'Round': 44, 'Results_raw': {'train_loss': 29.995254, 'val_loss': 28.168549, 'test_loss': 27.85715}} 2024-11-14 18:06:26,475 (client:354) INFO: {'Role': 'Client #2', 'Round': 44, 'Results_raw': {'train_loss': 13.310493, 'val_loss': 12.597504, 'test_loss': 13.322533}} 2024-11-14 18:07:18,869 (client:354) INFO: {'Role': 'Client #3', 'Round': 44, 'Results_raw': {'train_loss': 16.639276, 'val_loss': 17.331153, 'test_loss': 20.612666}} 2024-11-14 18:07:18,872 (server:615) INFO: {'Role': 'Server #', 'Round': 43, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.711125), 'test_loss': np.float64(154022.471735), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.390081), 'val_loss': np.float64(152358.179999), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.711125), 'test_loss': np.float64(154022.471735), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.390081), 'val_loss': np.float64(152358.179999), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.104049), 'test_avg_loss_bottom_decile': np.float64(24.394143), 'test_avg_loss_top_decile': np.float64(42.279602), 'test_avg_loss_min': np.float64(22.17562), 'test_avg_loss_max': np.float64(42.279602), 'test_avg_loss_bottom10%': np.float64(22.17562), 'test_avg_loss_top10%': np.float64(42.279602), 'test_avg_loss_cos1': np.float64(0.985563), 'test_avg_loss_entropy': np.float64(2.288463), 'test_loss_std': np.float64(26459.391274), 'test_loss_bottom_decile': np.float64(126459.237549), 'test_loss_top_decile': np.float64(219177.457886), 'test_loss_min': np.float64(114958.411987), 'test_loss_max': np.float64(219177.457886), 'test_loss_bottom10%': np.float64(114958.411987), 'test_loss_top10%': np.float64(219177.457886), 'test_loss_cos1': np.float64(0.985563), 'test_loss_entropy': np.float64(2.288463), 'val_avg_loss_std': np.float64(4.166071), 'val_avg_loss_bottom_decile': np.float64(23.836655), 'val_avg_loss_top_decile': np.float64(36.205837), 'val_avg_loss_min': np.float64(22.356965), 'val_avg_loss_max': np.float64(36.205837), 'val_avg_loss_bottom10%': np.float64(22.356965), 'val_avg_loss_top10%': np.float64(36.205837), 'val_avg_loss_cos1': np.float64(0.990102), 'val_avg_loss_entropy': np.float64(2.292403), 'val_loss_std': np.float64(21596.91455), 'val_loss_bottom_decile': np.float64(123569.221558), 'val_loss_top_decile': np.float64(187691.059387), 'val_loss_min': np.float64(115898.506226), 'val_loss_max': np.float64(187691.059387), 'val_loss_bottom10%': np.float64(115898.506226), 'val_loss_top10%': np.float64(187691.059387), 'val_loss_cos1': np.float64(0.990102), 'val_loss_entropy': np.float64(2.292403)}} 2024-11-14 18:07:18,905 (server:353) INFO: Server: Starting evaluation at the end of round 44. 2024-11-14 18:07:18,906 (server:359) INFO: ----------- Starting a new training round (Round #45) ------------- 2024-11-14 18:09:38,754 (client:354) INFO: {'Role': 'Client #4', 'Round': 45, 'Results_raw': {'train_loss': 23.116961, 'val_loss': 21.527011, 'test_loss': 23.335895}} 2024-11-14 18:10:30,084 (client:354) INFO: {'Role': 'Client #7', 'Round': 45, 'Results_raw': {'train_loss': 23.138831, 'val_loss': 22.525374, 'test_loss': 23.464044}} 2024-11-14 18:11:21,546 (client:354) INFO: {'Role': 'Client #5', 'Round': 45, 'Results_raw': {'train_loss': 23.950294, 'val_loss': 25.058278, 'test_loss': 33.835384}} 2024-11-14 18:12:12,127 (client:354) INFO: {'Role': 'Client #10', 'Round': 45, 'Results_raw': {'train_loss': 22.265987, 'val_loss': 22.182736, 'test_loss': 24.055045}} 2024-11-14 18:13:03,210 (client:354) INFO: {'Role': 'Client #9', 'Round': 45, 'Results_raw': {'train_loss': 29.947482, 'val_loss': 27.642358, 'test_loss': 27.102147}} 2024-11-14 18:13:53,549 (client:354) INFO: {'Role': 'Client #2', 'Round': 45, 'Results_raw': {'train_loss': 13.305451, 'val_loss': 12.540495, 'test_loss': 13.447045}} 2024-11-14 18:14:43,694 (client:354) INFO: {'Role': 'Client #1', 'Round': 45, 'Results_raw': {'train_loss': 17.270238, 'val_loss': 16.121621, 'test_loss': 18.261427}} 2024-11-14 18:15:33,891 (client:354) INFO: {'Role': 'Client #8', 'Round': 45, 'Results_raw': {'train_loss': 20.51965, 'val_loss': 20.085851, 'test_loss': 20.817084}} 2024-11-14 18:16:24,641 (client:354) INFO: {'Role': 'Client #6', 'Round': 45, 'Results_raw': {'train_loss': 23.205232, 'val_loss': 21.871494, 'test_loss': 23.74903}} 2024-11-14 18:17:14,831 (client:354) INFO: {'Role': 'Client #3', 'Round': 45, 'Results_raw': {'train_loss': 16.588032, 'val_loss': 16.860304, 'test_loss': 19.831142}} 2024-11-14 18:17:14,834 (server:615) INFO: {'Role': 'Server #', 'Round': 44, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.721769), 'test_loss': np.float64(154077.650299), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.384562), 'val_loss': np.float64(152329.567279), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.721769), 'test_loss': np.float64(154077.650299), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.384562), 'val_loss': np.float64(152329.567279), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.018097), 'test_avg_loss_bottom_decile': np.float64(24.5126), 'test_avg_loss_top_decile': np.float64(42.141407), 'test_avg_loss_min': np.float64(22.387408), 'test_avg_loss_max': np.float64(42.141407), 'test_avg_loss_bottom10%': np.float64(22.387408), 'test_avg_loss_top10%': np.float64(42.141407), 'test_avg_loss_cos1': np.float64(0.986045), 'test_avg_loss_entropy': np.float64(2.288962), 'test_loss_std': np.float64(26013.815673), 'test_loss_bottom_decile': np.float64(127073.318481), 'test_loss_top_decile': np.float64(218461.055298), 'test_loss_min': np.float64(116056.322144), 'test_loss_max': np.float64(218461.055298), 'test_loss_bottom10%': np.float64(116056.322144), 'test_loss_top10%': np.float64(218461.055298), 'test_loss_cos1': np.float64(0.986045), 'test_loss_entropy': np.float64(2.288962), 'val_avg_loss_std': np.float64(4.052813), 'val_avg_loss_bottom_decile': np.float64(23.956897), 'val_avg_loss_top_decile': np.float64(36.015368), 'val_avg_loss_min': np.float64(22.555948), 'val_avg_loss_max': np.float64(36.015368), 'val_avg_loss_bottom10%': np.float64(22.555948), 'val_avg_loss_top10%': np.float64(36.015368), 'val_avg_loss_cos1': np.float64(0.990622), 'val_avg_loss_entropy': np.float64(2.292955), 'val_loss_std': np.float64(21009.781026), 'val_loss_bottom_decile': np.float64(124192.552063), 'val_loss_top_decile': np.float64(186703.669861), 'val_loss_min': np.float64(116930.036926), 'val_loss_max': np.float64(186703.669861), 'val_loss_bottom10%': np.float64(116930.036926), 'val_loss_top10%': np.float64(186703.669861), 'val_loss_cos1': np.float64(0.990622), 'val_loss_entropy': np.float64(2.292955)}} 2024-11-14 18:17:14,868 (server:353) INFO: Server: Starting evaluation at the end of round 45. 2024-11-14 18:17:14,869 (server:359) INFO: ----------- Starting a new training round (Round #46) ------------- 2024-11-14 18:19:34,302 (client:354) INFO: {'Role': 'Client #10', 'Round': 46, 'Results_raw': {'train_loss': 22.299085, 'val_loss': 22.05669, 'test_loss': 23.696011}} 2024-11-14 18:20:24,753 (client:354) INFO: {'Role': 'Client #8', 'Round': 46, 'Results_raw': {'train_loss': 20.481189, 'val_loss': 20.027865, 'test_loss': 20.950433}} 2024-11-14 18:21:15,079 (client:354) INFO: {'Role': 'Client #4', 'Round': 46, 'Results_raw': {'train_loss': 23.049483, 'val_loss': 21.547133, 'test_loss': 23.191058}} 2024-11-14 18:22:05,497 (client:354) INFO: {'Role': 'Client #5', 'Round': 46, 'Results_raw': {'train_loss': 23.912754, 'val_loss': 24.871708, 'test_loss': 33.827552}} 2024-11-14 18:22:55,358 (client:354) INFO: {'Role': 'Client #3', 'Round': 46, 'Results_raw': {'train_loss': 16.584791, 'val_loss': 17.139958, 'test_loss': 20.456425}} 2024-11-14 18:23:46,090 (client:354) INFO: {'Role': 'Client #2', 'Round': 46, 'Results_raw': {'train_loss': 13.293293, 'val_loss': 12.495357, 'test_loss': 13.32383}} 2024-11-14 18:24:37,115 (client:354) INFO: {'Role': 'Client #6', 'Round': 46, 'Results_raw': {'train_loss': 23.14487, 'val_loss': 21.701192, 'test_loss': 23.408622}} 2024-11-14 18:25:28,707 (client:354) INFO: {'Role': 'Client #7', 'Round': 46, 'Results_raw': {'train_loss': 23.130469, 'val_loss': 22.611942, 'test_loss': 23.261223}} 2024-11-14 18:26:19,387 (client:354) INFO: {'Role': 'Client #1', 'Round': 46, 'Results_raw': {'train_loss': 17.248809, 'val_loss': 16.200586, 'test_loss': 18.387321}} 2024-11-14 18:27:09,872 (client:354) INFO: {'Role': 'Client #9', 'Round': 46, 'Results_raw': {'train_loss': 29.934684, 'val_loss': 27.958863, 'test_loss': 27.174042}} 2024-11-14 18:27:09,875 (server:615) INFO: {'Role': 'Server #', 'Round': 45, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.660766), 'test_loss': np.float64(153761.410358), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.306147), 'val_loss': np.float64(151923.063611), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.660766), 'test_loss': np.float64(153761.410358), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.306147), 'val_loss': np.float64(151923.063611), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.081434), 'test_avg_loss_bottom_decile': np.float64(24.37484), 'test_avg_loss_top_decile': np.float64(42.263958), 'test_avg_loss_min': np.float64(22.276407), 'test_avg_loss_max': np.float64(42.263958), 'test_avg_loss_bottom10%': np.float64(22.276407), 'test_avg_loss_top10%': np.float64(42.263958), 'test_avg_loss_cos1': np.float64(0.98564), 'test_avg_loss_entropy': np.float64(2.288575), 'test_loss_std': np.float64(26342.154762), 'test_loss_bottom_decile': np.float64(126359.170715), 'test_loss_top_decile': np.float64(219096.359619), 'test_loss_min': np.float64(115480.891968), 'test_loss_max': np.float64(219096.359619), 'test_loss_bottom10%': np.float64(115480.891968), 'test_loss_top10%': np.float64(219096.359619), 'test_loss_cos1': np.float64(0.98564), 'test_loss_entropy': np.float64(2.288575), 'val_avg_loss_std': np.float64(4.111253), 'val_avg_loss_bottom_decile': np.float64(23.791101), 'val_avg_loss_top_decile': np.float64(36.132507), 'val_avg_loss_min': np.float64(22.426204), 'val_avg_loss_max': np.float64(36.132507), 'val_avg_loss_bottom10%': np.float64(22.426204), 'val_avg_loss_top10%': np.float64(36.132507), 'val_avg_loss_cos1': np.float64(0.990303), 'val_avg_loss_entropy': np.float64(2.292627), 'val_loss_std': np.float64(21312.733167), 'val_loss_bottom_decile': np.float64(123333.065002), 'val_loss_top_decile': np.float64(187310.914856), 'val_loss_min': np.float64(116257.441162), 'val_loss_max': np.float64(187310.914856), 'val_loss_bottom10%': np.float64(116257.441162), 'val_loss_top10%': np.float64(187310.914856), 'val_loss_cos1': np.float64(0.990303), 'val_loss_entropy': np.float64(2.292627)}} 2024-11-14 18:27:09,905 (server:353) INFO: Server: Starting evaluation at the end of round 46. 2024-11-14 18:27:09,905 (server:359) INFO: ----------- Starting a new training round (Round #47) ------------- 2024-11-14 18:29:30,195 (client:354) INFO: {'Role': 'Client #7', 'Round': 47, 'Results_raw': {'train_loss': 23.149481, 'val_loss': 22.44922, 'test_loss': 23.065031}} 2024-11-14 18:30:20,593 (client:354) INFO: {'Role': 'Client #5', 'Round': 47, 'Results_raw': {'train_loss': 23.909268, 'val_loss': 25.014521, 'test_loss': 33.538966}} 2024-11-14 18:31:10,916 (client:354) INFO: {'Role': 'Client #3', 'Round': 47, 'Results_raw': {'train_loss': 16.535451, 'val_loss': 17.131743, 'test_loss': 20.578}} 2024-11-14 18:32:01,680 (client:354) INFO: {'Role': 'Client #8', 'Round': 47, 'Results_raw': {'train_loss': 20.453567, 'val_loss': 19.962029, 'test_loss': 20.755515}} 2024-11-14 18:32:52,925 (client:354) INFO: {'Role': 'Client #2', 'Round': 47, 'Results_raw': {'train_loss': 13.245904, 'val_loss': 12.657603, 'test_loss': 13.528926}} 2024-11-14 18:33:43,548 (client:354) INFO: {'Role': 'Client #4', 'Round': 47, 'Results_raw': {'train_loss': 23.083321, 'val_loss': 21.441785, 'test_loss': 23.009414}} 2024-11-14 18:34:34,343 (client:354) INFO: {'Role': 'Client #1', 'Round': 47, 'Results_raw': {'train_loss': 17.248561, 'val_loss': 16.241661, 'test_loss': 18.370631}} 2024-11-14 18:35:27,310 (client:354) INFO: {'Role': 'Client #10', 'Round': 47, 'Results_raw': {'train_loss': 22.242158, 'val_loss': 22.175505, 'test_loss': 23.746032}} 2024-11-14 18:36:18,567 (client:354) INFO: {'Role': 'Client #9', 'Round': 47, 'Results_raw': {'train_loss': 29.906351, 'val_loss': 27.800957, 'test_loss': 27.300456}} 2024-11-14 18:37:09,029 (client:354) INFO: {'Role': 'Client #6', 'Round': 47, 'Results_raw': {'train_loss': 23.141111, 'val_loss': 22.037788, 'test_loss': 23.787177}} 2024-11-14 18:37:09,032 (server:615) INFO: {'Role': 'Server #', 'Round': 46, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.4513), 'test_loss': np.float64(152675.539276), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.104838), 'val_loss': np.float64(150879.481049), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.4513), 'test_loss': np.float64(152675.539276), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.104838), 'val_loss': np.float64(150879.481049), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.113056), 'test_avg_loss_bottom_decile': np.float64(24.145073), 'test_avg_loss_top_decile': np.float64(41.980435), 'test_avg_loss_min': np.float64(21.840103), 'test_avg_loss_max': np.float64(41.980435), 'test_avg_loss_bottom10%': np.float64(21.840103), 'test_avg_loss_top10%': np.float64(41.980435), 'test_avg_loss_cos1': np.float64(0.985262), 'test_avg_loss_entropy': np.float64(2.288145), 'test_loss_std': np.float64(26506.080943), 'test_loss_bottom_decile': np.float64(125168.060425), 'test_loss_top_decile': np.float64(217626.574829), 'test_loss_min': np.float64(113219.096069), 'test_loss_max': np.float64(217626.574829), 'test_loss_bottom10%': np.float64(113219.096069), 'test_loss_top10%': np.float64(217626.574829), 'test_loss_cos1': np.float64(0.985262), 'test_loss_entropy': np.float64(2.288145), 'val_avg_loss_std': np.float64(4.184021), 'val_avg_loss_bottom_decile': np.float64(23.54374), 'val_avg_loss_top_decile': np.float64(35.87375), 'val_avg_loss_min': np.float64(21.977742), 'val_avg_loss_max': np.float64(35.87375), 'val_avg_loss_bottom10%': np.float64(21.977742), 'val_avg_loss_top10%': np.float64(35.87375), 'val_avg_loss_cos1': np.float64(0.989824), 'val_avg_loss_entropy': np.float64(2.292099), 'val_loss_std': np.float64(21689.964837), 'val_loss_bottom_decile': np.float64(122050.750732), 'val_loss_top_decile': np.float64(185969.518677), 'val_loss_min': np.float64(113932.614624), 'val_loss_max': np.float64(185969.518677), 'val_loss_bottom10%': np.float64(113932.614624), 'val_loss_top10%': np.float64(185969.518677), 'val_loss_cos1': np.float64(0.989824), 'val_loss_entropy': np.float64(2.292099)}} 2024-11-14 18:37:09,064 (server:353) INFO: Server: Starting evaluation at the end of round 47. 2024-11-14 18:37:09,064 (server:359) INFO: ----------- Starting a new training round (Round #48) ------------- 2024-11-14 18:39:27,922 (client:354) INFO: {'Role': 'Client #7', 'Round': 48, 'Results_raw': {'train_loss': 23.084523, 'val_loss': 22.529391, 'test_loss': 23.296558}} 2024-11-14 18:40:19,361 (client:354) INFO: {'Role': 'Client #4', 'Round': 48, 'Results_raw': {'train_loss': 23.023094, 'val_loss': 21.570392, 'test_loss': 23.067017}} 2024-11-14 18:41:12,116 (client:354) INFO: {'Role': 'Client #8', 'Round': 48, 'Results_raw': {'train_loss': 20.460275, 'val_loss': 19.955304, 'test_loss': 20.970524}} 2024-11-14 18:42:03,681 (client:354) INFO: {'Role': 'Client #5', 'Round': 48, 'Results_raw': {'train_loss': 23.878304, 'val_loss': 25.118517, 'test_loss': 33.811217}} 2024-11-14 18:43:04,145 (client:354) INFO: {'Role': 'Client #6', 'Round': 48, 'Results_raw': {'train_loss': 23.119557, 'val_loss': 22.001598, 'test_loss': 24.178254}} 2024-11-14 18:44:09,076 (client:354) INFO: {'Role': 'Client #9', 'Round': 48, 'Results_raw': {'train_loss': 29.941427, 'val_loss': 27.862164, 'test_loss': 27.272293}} 2024-11-14 18:45:08,186 (client:354) INFO: {'Role': 'Client #2', 'Round': 48, 'Results_raw': {'train_loss': 13.251917, 'val_loss': 12.630474, 'test_loss': 13.485023}} 2024-11-14 18:46:06,701 (client:354) INFO: {'Role': 'Client #10', 'Round': 48, 'Results_raw': {'train_loss': 22.227, 'val_loss': 22.090703, 'test_loss': 23.628527}} 2024-11-14 18:47:04,898 (client:354) INFO: {'Role': 'Client #3', 'Round': 48, 'Results_raw': {'train_loss': 16.568155, 'val_loss': 16.958618, 'test_loss': 20.16939}} 2024-11-14 18:48:03,280 (client:354) INFO: {'Role': 'Client #1', 'Round': 48, 'Results_raw': {'train_loss': 17.241443, 'val_loss': 16.106934, 'test_loss': 18.333007}} 2024-11-14 18:48:03,283 (server:615) INFO: {'Role': 'Server #', 'Round': 47, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.554032), 'test_loss': np.float64(153208.10061), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.229618), 'val_loss': np.float64(151526.340863), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.554032), 'test_loss': np.float64(153208.10061), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.229618), 'val_loss': np.float64(151526.340863), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.137027), 'test_avg_loss_bottom_decile': np.float64(24.272174), 'test_avg_loss_top_decile': np.float64(42.22227), 'test_avg_loss_min': np.float64(21.931013), 'test_avg_loss_max': np.float64(42.22227), 'test_avg_loss_bottom10%': np.float64(21.931013), 'test_avg_loss_top10%': np.float64(42.22227), 'test_avg_loss_cos1': np.float64(0.985228), 'test_avg_loss_entropy': np.float64(2.288136), 'test_loss_std': np.float64(26630.345603), 'test_loss_bottom_decile': np.float64(125826.949158), 'test_loss_top_decile': np.float64(218880.246155), 'test_loss_min': np.float64(113690.368958), 'test_loss_max': np.float64(218880.246155), 'test_loss_bottom10%': np.float64(113690.368958), 'test_loss_top10%': np.float64(218880.246155), 'test_loss_cos1': np.float64(0.985228), 'test_loss_entropy': np.float64(2.288136), 'val_avg_loss_std': np.float64(4.180958), 'val_avg_loss_bottom_decile': np.float64(23.689678), 'val_avg_loss_top_decile': np.float64(36.089932), 'val_avg_loss_min': np.float64(22.099084), 'val_avg_loss_max': np.float64(36.089932), 'val_avg_loss_bottom10%': np.float64(22.099084), 'val_avg_loss_top10%': np.float64(36.089932), 'val_avg_loss_cos1': np.float64(0.989924), 'val_avg_loss_entropy': np.float64(2.292211), 'val_loss_std': np.float64(21674.088577), 'val_loss_bottom_decile': np.float64(122807.293152), 'val_loss_top_decile': np.float64(187090.208496), 'val_loss_min': np.float64(114561.649292), 'val_loss_max': np.float64(187090.208496), 'val_loss_bottom10%': np.float64(114561.649292), 'val_loss_top10%': np.float64(187090.208496), 'val_loss_cos1': np.float64(0.989924), 'val_loss_entropy': np.float64(2.292211)}} 2024-11-14 18:48:03,317 (server:353) INFO: Server: Starting evaluation at the end of round 48. 2024-11-14 18:48:03,318 (server:359) INFO: ----------- Starting a new training round (Round #49) ------------- 2024-11-14 18:50:31,250 (client:354) INFO: {'Role': 'Client #10', 'Round': 49, 'Results_raw': {'train_loss': 22.226042, 'val_loss': 22.145778, 'test_loss': 23.786385}} 2024-11-14 18:51:30,290 (client:354) INFO: {'Role': 'Client #3', 'Round': 49, 'Results_raw': {'train_loss': 16.496769, 'val_loss': 17.039475, 'test_loss': 20.31592}} 2024-11-14 18:52:25,592 (client:354) INFO: {'Role': 'Client #2', 'Round': 49, 'Results_raw': {'train_loss': 13.241775, 'val_loss': 12.755625, 'test_loss': 13.684871}} 2024-11-14 18:53:15,954 (client:354) INFO: {'Role': 'Client #6', 'Round': 49, 'Results_raw': {'train_loss': 23.087177, 'val_loss': 21.63995, 'test_loss': 23.624573}} 2024-11-14 18:54:14,029 (client:354) INFO: {'Role': 'Client #7', 'Round': 49, 'Results_raw': {'train_loss': 23.046412, 'val_loss': 22.656209, 'test_loss': 23.195357}} 2024-11-14 18:55:12,263 (client:354) INFO: {'Role': 'Client #4', 'Round': 49, 'Results_raw': {'train_loss': 23.038173, 'val_loss': 21.528442, 'test_loss': 23.181235}} 2024-11-14 18:56:11,410 (client:354) INFO: {'Role': 'Client #5', 'Round': 49, 'Results_raw': {'train_loss': 23.830649, 'val_loss': 24.677754, 'test_loss': 33.468219}} 2024-11-14 18:57:13,585 (client:354) INFO: {'Role': 'Client #9', 'Round': 49, 'Results_raw': {'train_loss': 29.89893, 'val_loss': 27.839722, 'test_loss': 27.341814}} 2024-11-14 18:58:19,045 (client:354) INFO: {'Role': 'Client #8', 'Round': 49, 'Results_raw': {'train_loss': 20.451854, 'val_loss': 19.84978, 'test_loss': 20.897593}} 2024-11-14 18:59:24,601 (client:354) INFO: {'Role': 'Client #1', 'Round': 49, 'Results_raw': {'train_loss': 17.203298, 'val_loss': 16.327017, 'test_loss': 18.588961}} 2024-11-14 18:59:24,604 (server:615) INFO: {'Role': 'Server #', 'Round': 48, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.600382), 'test_loss': np.float64(153448.378687), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.250707), 'val_loss': np.float64(151635.666534), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.600382), 'test_loss': np.float64(153448.378687), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.250707), 'val_loss': np.float64(151635.666534), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.06317), 'test_avg_loss_bottom_decile': np.float64(24.336038), 'test_avg_loss_top_decile': np.float64(42.074611), 'test_avg_loss_min': np.float64(22.138319), 'test_avg_loss_max': np.float64(42.074611), 'test_avg_loss_bottom10%': np.float64(22.138319), 'test_avg_loss_top10%': np.float64(42.074611), 'test_avg_loss_cos1': np.float64(0.985684), 'test_avg_loss_entropy': np.float64(2.288587), 'test_loss_std': np.float64(26247.474066), 'test_loss_bottom_decile': np.float64(126158.019043), 'test_loss_top_decile': np.float64(218114.785828), 'test_loss_min': np.float64(114765.046631), 'test_loss_max': np.float64(218114.785828), 'test_loss_bottom10%': np.float64(114765.046631), 'test_loss_top10%': np.float64(218114.785828), 'test_loss_cos1': np.float64(0.985684), 'test_loss_entropy': np.float64(2.288587), 'val_avg_loss_std': np.float64(4.098651), 'val_avg_loss_bottom_decile': np.float64(23.755541), 'val_avg_loss_top_decile': np.float64(35.89283), 'val_avg_loss_min': np.float64(22.297447), 'val_avg_loss_max': np.float64(35.89283), 'val_avg_loss_bottom10%': np.float64(22.297447), 'val_avg_loss_top10%': np.float64(35.89283), 'val_avg_loss_cos1': np.float64(0.990325), 'val_avg_loss_entropy': np.float64(2.292633), 'val_loss_std': np.float64(21247.405595), 'val_loss_bottom_decile': np.float64(123148.724548), 'val_loss_top_decile': np.float64(186068.429077), 'val_loss_min': np.float64(115589.963745), 'val_loss_max': np.float64(186068.429077), 'val_loss_bottom10%': np.float64(115589.963745), 'val_loss_top10%': np.float64(186068.429077), 'val_loss_cos1': np.float64(0.990325), 'val_loss_entropy': np.float64(2.292633)}} 2024-11-14 18:59:24,648 (server:353) INFO: Server: Starting evaluation at the end of round 49. 2024-11-14 18:59:24,649 (server:359) INFO: ----------- Starting a new training round (Round #50) ------------- 2024-11-14 19:02:20,780 (client:354) INFO: {'Role': 'Client #8', 'Round': 50, 'Results_raw': {'train_loss': 20.412973, 'val_loss': 19.905814, 'test_loss': 21.002193}} 2024-11-14 19:03:25,870 (client:354) INFO: {'Role': 'Client #1', 'Round': 50, 'Results_raw': {'train_loss': 17.196583, 'val_loss': 16.070997, 'test_loss': 18.285048}} 2024-11-14 19:04:27,875 (client:354) INFO: {'Role': 'Client #9', 'Round': 50, 'Results_raw': {'train_loss': 29.821434, 'val_loss': 27.679101, 'test_loss': 27.107493}} 2024-11-14 19:05:27,419 (client:354) INFO: {'Role': 'Client #10', 'Round': 50, 'Results_raw': {'train_loss': 22.219332, 'val_loss': 22.174113, 'test_loss': 24.149589}} 2024-11-14 19:06:26,181 (client:354) INFO: {'Role': 'Client #3', 'Round': 50, 'Results_raw': {'train_loss': 16.486653, 'val_loss': 17.053383, 'test_loss': 20.086543}} 2024-11-14 19:07:25,255 (client:354) INFO: {'Role': 'Client #5', 'Round': 50, 'Results_raw': {'train_loss': 23.836901, 'val_loss': 25.100911, 'test_loss': 33.672953}} 2024-11-14 19:08:23,955 (client:354) INFO: {'Role': 'Client #7', 'Round': 50, 'Results_raw': {'train_loss': 23.048086, 'val_loss': 22.563803, 'test_loss': 23.163514}} 2024-11-14 19:09:21,455 (client:354) INFO: {'Role': 'Client #2', 'Round': 50, 'Results_raw': {'train_loss': 13.22116, 'val_loss': 12.613737, 'test_loss': 13.634814}} 2024-11-14 19:10:18,748 (client:354) INFO: {'Role': 'Client #6', 'Round': 50, 'Results_raw': {'train_loss': 23.07007, 'val_loss': 21.722165, 'test_loss': 23.590941}} 2024-11-14 19:11:17,051 (client:354) INFO: {'Role': 'Client #4', 'Round': 50, 'Results_raw': {'train_loss': 22.975524, 'val_loss': 21.464271, 'test_loss': 23.239452}} 2024-11-14 19:11:17,056 (server:615) INFO: {'Role': 'Server #', 'Round': 49, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.403198), 'test_loss': np.float64(152426.178461), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.043163), 'val_loss': np.float64(150559.757428), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.403198), 'test_loss': np.float64(152426.178461), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.043163), 'val_loss': np.float64(150559.757428), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.092187), 'test_avg_loss_bottom_decile': np.float64(24.211022), 'test_avg_loss_top_decile': np.float64(41.99206), 'test_avg_loss_min': np.float64(21.861425), 'test_avg_loss_max': np.float64(41.99206), 'test_avg_loss_bottom10%': np.float64(21.861425), 'test_avg_loss_top10%': np.float64(41.99206), 'test_avg_loss_cos1': np.float64(0.985333), 'test_avg_loss_entropy': np.float64(2.288252), 'test_loss_std': np.float64(26397.899031), 'test_loss_bottom_decile': np.float64(125509.935608), 'test_loss_top_decile': np.float64(217686.841492), 'test_loss_min': np.float64(113329.624817), 'test_loss_max': np.float64(217686.841492), 'test_loss_bottom10%': np.float64(113329.624817), 'test_loss_top10%': np.float64(217686.841492), 'test_loss_cos1': np.float64(0.985333), 'test_loss_entropy': np.float64(2.288252), 'val_avg_loss_std': np.float64(4.12254), 'val_avg_loss_bottom_decile': np.float64(23.608053), 'val_avg_loss_top_decile': np.float64(35.82011), 'val_avg_loss_min': np.float64(21.995928), 'val_avg_loss_max': np.float64(35.82011), 'val_avg_loss_bottom10%': np.float64(21.995928), 'val_avg_loss_top10%': np.float64(35.82011), 'val_avg_loss_cos1': np.float64(0.990075), 'val_avg_loss_entropy': np.float64(2.292373), 'val_loss_std': np.float64(21371.246308), 'val_loss_bottom_decile': np.float64(122384.147095), 'val_loss_top_decile': np.float64(185691.451477), 'val_loss_min': np.float64(114026.889832), 'val_loss_max': np.float64(185691.451477), 'val_loss_bottom10%': np.float64(114026.889832), 'val_loss_top10%': np.float64(185691.451477), 'val_loss_cos1': np.float64(0.990075), 'val_loss_entropy': np.float64(2.292373)}} 2024-11-14 19:11:17,102 (server:353) INFO: Server: Starting evaluation at the end of round 50. 2024-11-14 19:11:17,102 (server:359) INFO: ----------- Starting a new training round (Round #51) ------------- 2024-11-14 19:14:06,284 (client:354) INFO: {'Role': 'Client #8', 'Round': 51, 'Results_raw': {'train_loss': 20.419257, 'val_loss': 20.130636, 'test_loss': 21.239706}} 2024-11-14 19:15:03,232 (client:354) INFO: {'Role': 'Client #1', 'Round': 51, 'Results_raw': {'train_loss': 17.16802, 'val_loss': 16.178218, 'test_loss': 18.478982}} 2024-11-14 19:16:00,528 (client:354) INFO: {'Role': 'Client #4', 'Round': 51, 'Results_raw': {'train_loss': 22.975174, 'val_loss': 21.657118, 'test_loss': 23.50352}} 2024-11-14 19:16:53,938 (client:354) INFO: {'Role': 'Client #10', 'Round': 51, 'Results_raw': {'train_loss': 22.171021, 'val_loss': 22.152633, 'test_loss': 23.489553}} 2024-11-14 19:17:43,578 (client:354) INFO: {'Role': 'Client #3', 'Round': 51, 'Results_raw': {'train_loss': 16.475353, 'val_loss': 16.992547, 'test_loss': 20.082707}} 2024-11-14 19:18:32,891 (client:354) INFO: {'Role': 'Client #2', 'Round': 51, 'Results_raw': {'train_loss': 13.179264, 'val_loss': 12.537672, 'test_loss': 13.446102}} 2024-11-14 19:19:22,200 (client:354) INFO: {'Role': 'Client #7', 'Round': 51, 'Results_raw': {'train_loss': 23.043131, 'val_loss': 22.659602, 'test_loss': 23.12557}} 2024-11-14 19:20:12,026 (client:354) INFO: {'Role': 'Client #5', 'Round': 51, 'Results_raw': {'train_loss': 23.813523, 'val_loss': 24.892494, 'test_loss': 33.638502}} 2024-11-14 19:21:01,976 (client:354) INFO: {'Role': 'Client #9', 'Round': 51, 'Results_raw': {'train_loss': 29.857823, 'val_loss': 27.71874, 'test_loss': 27.104782}} 2024-11-14 19:21:52,703 (client:354) INFO: {'Role': 'Client #6', 'Round': 51, 'Results_raw': {'train_loss': 23.030786, 'val_loss': 21.667285, 'test_loss': 23.587405}} 2024-11-14 19:21:52,706 (server:615) INFO: {'Role': 'Server #', 'Round': 50, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.516258), 'test_loss': np.float64(153012.281769), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.145996), 'val_loss': np.float64(151092.843921), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.516258), 'test_loss': np.float64(153012.281769), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.145996), 'val_loss': np.float64(151092.843921), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.995497), 'test_avg_loss_bottom_decile': np.float64(24.386384), 'test_avg_loss_top_decile': np.float64(41.904839), 'test_avg_loss_min': np.float64(22.208381), 'test_avg_loss_max': np.float64(41.904839), 'test_avg_loss_bottom10%': np.float64(22.208381), 'test_avg_loss_top10%': np.float64(41.904839), 'test_avg_loss_cos1': np.float64(0.985978), 'test_avg_loss_entropy': np.float64(2.288906), 'test_loss_std': np.float64(25896.656313), 'test_loss_bottom_decile': np.float64(126419.013184), 'test_loss_top_decile': np.float64(217234.683777), 'test_loss_min': np.float64(115128.247925), 'test_loss_max': np.float64(217234.683777), 'test_loss_bottom10%': np.float64(115128.247925), 'test_loss_top10%': np.float64(217234.683777), 'test_loss_cos1': np.float64(0.985978), 'test_loss_entropy': np.float64(2.288906), 'val_avg_loss_std': np.float64(4.038086), 'val_avg_loss_bottom_decile': np.float64(23.773032), 'val_avg_loss_top_decile': np.float64(35.78139), 'val_avg_loss_min': np.float64(22.326147), 'val_avg_loss_max': np.float64(35.78139), 'val_avg_loss_bottom10%': np.float64(22.326147), 'val_avg_loss_top10%': np.float64(35.78139), 'val_avg_loss_cos1': np.float64(0.990538), 'val_avg_loss_entropy': np.float64(2.29287), 'val_loss_std': np.float64(20933.438892), 'val_loss_bottom_decile': np.float64(123239.399292), 'val_loss_top_decile': np.float64(185490.723938), 'val_loss_min': np.float64(115738.747192), 'val_loss_max': np.float64(185490.723938), 'val_loss_bottom10%': np.float64(115738.747192), 'val_loss_top10%': np.float64(185490.723938), 'val_loss_cos1': np.float64(0.990538), 'val_loss_entropy': np.float64(2.29287)}} 2024-11-14 19:21:52,740 (server:353) INFO: Server: Starting evaluation at the end of round 51. 2024-11-14 19:21:52,741 (server:359) INFO: ----------- Starting a new training round (Round #52) ------------- 2024-11-14 19:24:10,921 (client:354) INFO: {'Role': 'Client #10', 'Round': 52, 'Results_raw': {'train_loss': 22.166776, 'val_loss': 22.093477, 'test_loss': 24.044274}} 2024-11-14 19:25:01,134 (client:354) INFO: {'Role': 'Client #1', 'Round': 52, 'Results_raw': {'train_loss': 17.156558, 'val_loss': 16.258502, 'test_loss': 18.602365}} 2024-11-14 19:25:51,403 (client:354) INFO: {'Role': 'Client #5', 'Round': 52, 'Results_raw': {'train_loss': 23.790867, 'val_loss': 24.937978, 'test_loss': 33.79634}} 2024-11-14 19:26:41,004 (client:354) INFO: {'Role': 'Client #4', 'Round': 52, 'Results_raw': {'train_loss': 22.924842, 'val_loss': 21.485718, 'test_loss': 23.114771}} 2024-11-14 19:27:30,512 (client:354) INFO: {'Role': 'Client #7', 'Round': 52, 'Results_raw': {'train_loss': 23.0203, 'val_loss': 22.594845, 'test_loss': 23.257381}} 2024-11-14 19:28:20,839 (client:354) INFO: {'Role': 'Client #9', 'Round': 52, 'Results_raw': {'train_loss': 29.777562, 'val_loss': 28.011648, 'test_loss': 27.560812}} 2024-11-14 19:29:11,086 (client:354) INFO: {'Role': 'Client #3', 'Round': 52, 'Results_raw': {'train_loss': 16.487067, 'val_loss': 17.016656, 'test_loss': 20.412915}} 2024-11-14 19:30:01,689 (client:354) INFO: {'Role': 'Client #6', 'Round': 52, 'Results_raw': {'train_loss': 23.041155, 'val_loss': 21.667963, 'test_loss': 23.581826}} 2024-11-14 19:30:53,471 (client:354) INFO: {'Role': 'Client #2', 'Round': 52, 'Results_raw': {'train_loss': 13.217884, 'val_loss': 12.723603, 'test_loss': 13.758356}} 2024-11-14 19:31:42,873 (client:354) INFO: {'Role': 'Client #8', 'Round': 52, 'Results_raw': {'train_loss': 20.383301, 'val_loss': 19.991046, 'test_loss': 20.857413}} 2024-11-14 19:31:42,876 (server:615) INFO: {'Role': 'Server #', 'Round': 51, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.392196), 'test_loss': np.float64(152369.142529), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.029236), 'val_loss': np.float64(150487.559851), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.392196), 'test_loss': np.float64(152369.142529), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.029236), 'val_loss': np.float64(150487.559851), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.041315), 'test_avg_loss_bottom_decile': np.float64(24.212454), 'test_avg_loss_top_decile': np.float64(41.884865), 'test_avg_loss_min': np.float64(21.991876), 'test_avg_loss_max': np.float64(41.884865), 'test_avg_loss_bottom10%': np.float64(21.991876), 'test_avg_loss_top10%': np.float64(41.884865), 'test_avg_loss_cos1': np.float64(0.985607), 'test_avg_loss_entropy': np.float64(2.288535), 'test_loss_std': np.float64(26134.174455), 'test_loss_bottom_decile': np.float64(125517.363647), 'test_loss_top_decile': np.float64(217131.139648), 'test_loss_min': np.float64(114005.883118), 'test_loss_max': np.float64(217131.139648), 'test_loss_bottom10%': np.float64(114005.883118), 'test_loss_top10%': np.float64(217131.139648), 'test_loss_cos1': np.float64(0.985607), 'test_loss_entropy': np.float64(2.288535), 'val_avg_loss_std': np.float64(4.069421), 'val_avg_loss_bottom_decile': np.float64(23.594966), 'val_avg_loss_top_decile': np.float64(35.664662), 'val_avg_loss_min': np.float64(22.137103), 'val_avg_loss_max': np.float64(35.664662), 'val_avg_loss_bottom10%': np.float64(22.137103), 'val_avg_loss_top10%': np.float64(35.664662), 'val_avg_loss_cos1': np.float64(0.990317), 'val_avg_loss_entropy': np.float64(2.292629), 'val_loss_std': np.float64(21095.87685), 'val_loss_bottom_decile': np.float64(122316.305054), 'val_loss_top_decile': np.float64(184885.605286), 'val_loss_min': np.float64(114758.741638), 'val_loss_max': np.float64(184885.605286), 'val_loss_bottom10%': np.float64(114758.741638), 'val_loss_top10%': np.float64(184885.605286), 'val_loss_cos1': np.float64(0.990317), 'val_loss_entropy': np.float64(2.292629)}} 2024-11-14 19:31:42,907 (server:353) INFO: Server: Starting evaluation at the end of round 52. 2024-11-14 19:31:42,908 (server:359) INFO: ----------- Starting a new training round (Round #53) ------------- 2024-11-14 19:34:02,035 (client:354) INFO: {'Role': 'Client #9', 'Round': 53, 'Results_raw': {'train_loss': 29.787006, 'val_loss': 27.55125, 'test_loss': 26.919842}} 2024-11-14 19:34:58,144 (client:354) INFO: {'Role': 'Client #1', 'Round': 53, 'Results_raw': {'train_loss': 17.166289, 'val_loss': 16.235284, 'test_loss': 18.503636}} 2024-11-14 19:35:54,559 (client:354) INFO: {'Role': 'Client #2', 'Round': 53, 'Results_raw': {'train_loss': 13.161857, 'val_loss': 12.534066, 'test_loss': 13.249254}} 2024-11-14 19:36:51,760 (client:354) INFO: {'Role': 'Client #6', 'Round': 53, 'Results_raw': {'train_loss': 23.034166, 'val_loss': 21.762178, 'test_loss': 23.567756}} 2024-11-14 19:37:49,604 (client:354) INFO: {'Role': 'Client #3', 'Round': 53, 'Results_raw': {'train_loss': 16.432743, 'val_loss': 16.924293, 'test_loss': 20.075907}} 2024-11-14 19:38:46,656 (client:354) INFO: {'Role': 'Client #4', 'Round': 53, 'Results_raw': {'train_loss': 22.974232, 'val_loss': 21.554737, 'test_loss': 23.340896}} 2024-11-14 19:39:43,798 (client:354) INFO: {'Role': 'Client #10', 'Round': 53, 'Results_raw': {'train_loss': 22.180761, 'val_loss': 22.219926, 'test_loss': 23.955021}} 2024-11-14 19:40:41,103 (client:354) INFO: {'Role': 'Client #8', 'Round': 53, 'Results_raw': {'train_loss': 20.348985, 'val_loss': 19.94633, 'test_loss': 20.903495}} 2024-11-14 19:41:38,306 (client:354) INFO: {'Role': 'Client #7', 'Round': 53, 'Results_raw': {'train_loss': 23.043964, 'val_loss': 22.638677, 'test_loss': 23.357721}} 2024-11-14 19:42:35,553 (client:354) INFO: {'Role': 'Client #5', 'Round': 53, 'Results_raw': {'train_loss': 23.789714, 'val_loss': 24.863643, 'test_loss': 33.616537}} 2024-11-14 19:42:35,557 (server:615) INFO: {'Role': 'Server #', 'Round': 52, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.517442), 'test_loss': np.float64(153018.42171), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.132693), 'val_loss': np.float64(151023.879572), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.517442), 'test_loss': np.float64(153018.42171), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(29.132693), 'val_loss': np.float64(151023.879572), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.014481), 'test_avg_loss_bottom_decile': np.float64(24.357164), 'test_avg_loss_top_decile': np.float64(41.997555), 'test_avg_loss_min': np.float64(22.224197), 'test_avg_loss_max': np.float64(41.997555), 'test_avg_loss_bottom10%': np.float64(22.224197), 'test_avg_loss_top10%': np.float64(41.997555), 'test_avg_loss_cos1': np.float64(0.985875), 'test_avg_loss_entropy': np.float64(2.288818), 'test_loss_std': np.float64(25995.067366), 'test_loss_bottom_decile': np.float64(126267.537781), 'test_loss_top_decile': np.float64(217715.325684), 'test_loss_min': np.float64(115210.238647), 'test_loss_max': np.float64(217715.325684), 'test_loss_bottom10%': np.float64(115210.238647), 'test_loss_top10%': np.float64(217715.325684), 'test_loss_cos1': np.float64(0.985875), 'test_loss_entropy': np.float64(2.288818), 'val_avg_loss_std': np.float64(4.022372), 'val_avg_loss_bottom_decile': np.float64(23.739339), 'val_avg_loss_top_decile': np.float64(35.771045), 'val_avg_loss_min': np.float64(22.338727), 'val_avg_loss_max': np.float64(35.771045), 'val_avg_loss_bottom10%': np.float64(22.338727), 'val_avg_loss_top10%': np.float64(35.771045), 'val_avg_loss_cos1': np.float64(0.990602), 'val_avg_loss_entropy': np.float64(2.292935), 'val_loss_std': np.float64(20851.976273), 'val_loss_bottom_decile': np.float64(123064.7323), 'val_loss_top_decile': np.float64(185437.095154), 'val_loss_min': np.float64(115803.961731), 'val_loss_max': np.float64(185437.095154), 'val_loss_bottom10%': np.float64(115803.961731), 'val_loss_top10%': np.float64(185437.095154), 'val_loss_cos1': np.float64(0.990602), 'val_loss_entropy': np.float64(2.292935)}} 2024-11-14 19:42:35,596 (server:353) INFO: Server: Starting evaluation at the end of round 53. 2024-11-14 19:42:35,597 (server:359) INFO: ----------- Starting a new training round (Round #54) ------------- 2024-11-14 19:45:13,236 (client:354) INFO: {'Role': 'Client #5', 'Round': 54, 'Results_raw': {'train_loss': 23.736419, 'val_loss': 25.144015, 'test_loss': 34.001183}} 2024-11-14 19:46:16,137 (client:354) INFO: {'Role': 'Client #4', 'Round': 54, 'Results_raw': {'train_loss': 22.908413, 'val_loss': 21.640539, 'test_loss': 23.482404}} 2024-11-14 19:47:19,478 (client:354) INFO: {'Role': 'Client #2', 'Round': 54, 'Results_raw': {'train_loss': 13.159807, 'val_loss': 12.508771, 'test_loss': 13.447694}} 2024-11-14 19:48:21,809 (client:354) INFO: {'Role': 'Client #9', 'Round': 54, 'Results_raw': {'train_loss': 29.749503, 'val_loss': 27.694568, 'test_loss': 27.135152}} 2024-11-14 19:49:23,069 (client:354) INFO: {'Role': 'Client #7', 'Round': 54, 'Results_raw': {'train_loss': 22.9912, 'val_loss': 22.645138, 'test_loss': 23.234357}} 2024-11-14 19:50:19,577 (client:354) INFO: {'Role': 'Client #10', 'Round': 54, 'Results_raw': {'train_loss': 22.15672, 'val_loss': 22.039063, 'test_loss': 23.820328}} 2024-11-14 19:51:16,293 (client:354) INFO: {'Role': 'Client #3', 'Round': 54, 'Results_raw': {'train_loss': 16.44189, 'val_loss': 17.131894, 'test_loss': 20.602664}} 2024-11-14 19:52:13,065 (client:354) INFO: {'Role': 'Client #8', 'Round': 54, 'Results_raw': {'train_loss': 20.342088, 'val_loss': 19.950828, 'test_loss': 21.054494}} 2024-11-14 19:53:10,602 (client:354) INFO: {'Role': 'Client #1', 'Round': 54, 'Results_raw': {'train_loss': 17.160018, 'val_loss': 16.277785, 'test_loss': 18.513292}} 2024-11-14 19:54:08,786 (client:354) INFO: {'Role': 'Client #6', 'Round': 54, 'Results_raw': {'train_loss': 23.043137, 'val_loss': 21.922476, 'test_loss': 23.927722}} 2024-11-14 19:54:08,792 (server:615) INFO: {'Role': 'Server #', 'Round': 53, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.264381), 'test_loss': np.float64(151706.550934), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.883434), 'val_loss': np.float64(149731.72243), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.264381), 'test_loss': np.float64(151706.550934), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.883434), 'val_loss': np.float64(149731.72243), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.10237), 'test_avg_loss_bottom_decile': np.float64(24.043917), 'test_avg_loss_top_decile': np.float64(41.866252), 'test_avg_loss_min': np.float64(21.743606), 'test_avg_loss_max': np.float64(41.866252), 'test_avg_loss_bottom10%': np.float64(21.743606), 'test_avg_loss_top10%': np.float64(41.866252), 'test_avg_loss_cos1': np.float64(0.985138), 'test_avg_loss_entropy': np.float64(2.288062), 'test_loss_std': np.float64(26450.685136), 'test_loss_bottom_decile': np.float64(124643.664246), 'test_loss_top_decile': np.float64(217034.649719), 'test_loss_min': np.float64(112718.853516), 'test_loss_max': np.float64(217034.649719), 'test_loss_bottom10%': np.float64(112718.853516), 'test_loss_top10%': np.float64(217034.649719), 'test_loss_cos1': np.float64(0.985138), 'test_loss_entropy': np.float64(2.288062), 'val_avg_loss_std': np.float64(4.143838), 'val_avg_loss_bottom_decile': np.float64(23.385926), 'val_avg_loss_top_decile': np.float64(35.621631), 'val_avg_loss_min': np.float64(21.855477), 'val_avg_loss_max': np.float64(35.621631), 'val_avg_loss_bottom10%': np.float64(21.855477), 'val_avg_loss_top10%': np.float64(35.621631), 'val_avg_loss_cos1': np.float64(0.989865), 'val_avg_loss_entropy': np.float64(2.292153), 'val_loss_std': np.float64(21481.657442), 'val_loss_bottom_decile': np.float64(121232.638), 'val_loss_top_decile': np.float64(184662.536621), 'val_loss_min': np.float64(113298.793152), 'val_loss_max': np.float64(184662.536621), 'val_loss_bottom10%': np.float64(113298.793152), 'val_loss_top10%': np.float64(184662.536621), 'val_loss_cos1': np.float64(0.989865), 'val_loss_entropy': np.float64(2.292153)}} 2024-11-14 19:54:08,834 (server:353) INFO: Server: Starting evaluation at the end of round 54. 2024-11-14 19:54:08,834 (server:359) INFO: ----------- Starting a new training round (Round #55) ------------- 2024-11-14 19:56:39,804 (client:354) INFO: {'Role': 'Client #5', 'Round': 55, 'Results_raw': {'train_loss': 23.720094, 'val_loss': 25.074783, 'test_loss': 33.966092}} 2024-11-14 19:57:36,524 (client:354) INFO: {'Role': 'Client #2', 'Round': 55, 'Results_raw': {'train_loss': 13.165442, 'val_loss': 12.64068, 'test_loss': 13.417397}} 2024-11-14 19:58:33,352 (client:354) INFO: {'Role': 'Client #3', 'Round': 55, 'Results_raw': {'train_loss': 16.425742, 'val_loss': 16.932225, 'test_loss': 20.090038}} 2024-11-14 19:59:30,801 (client:354) INFO: {'Role': 'Client #7', 'Round': 55, 'Results_raw': {'train_loss': 22.951853, 'val_loss': 22.479548, 'test_loss': 23.177357}} 2024-11-14 20:00:27,669 (client:354) INFO: {'Role': 'Client #6', 'Round': 55, 'Results_raw': {'train_loss': 22.968902, 'val_loss': 21.805846, 'test_loss': 23.923154}} 2024-11-14 20:01:24,530 (client:354) INFO: {'Role': 'Client #9', 'Round': 55, 'Results_raw': {'train_loss': 29.747976, 'val_loss': 27.828823, 'test_loss': 27.277319}} 2024-11-14 20:02:21,873 (client:354) INFO: {'Role': 'Client #1', 'Round': 55, 'Results_raw': {'train_loss': 17.108535, 'val_loss': 16.200007, 'test_loss': 18.441576}} 2024-11-14 20:03:19,058 (client:354) INFO: {'Role': 'Client #10', 'Round': 55, 'Results_raw': {'train_loss': 22.088346, 'val_loss': 22.129899, 'test_loss': 23.599676}} 2024-11-14 20:04:16,993 (client:354) INFO: {'Role': 'Client #4', 'Round': 55, 'Results_raw': {'train_loss': 22.900546, 'val_loss': 21.497762, 'test_loss': 23.522324}} 2024-11-14 20:05:14,668 (client:354) INFO: {'Role': 'Client #8', 'Round': 55, 'Results_raw': {'train_loss': 20.345822, 'val_loss': 20.061608, 'test_loss': 21.213484}} 2024-11-14 20:05:14,671 (server:615) INFO: {'Role': 'Server #', 'Round': 54, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.358859), 'test_loss': np.float64(152196.326831), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.960881), 'val_loss': np.float64(150133.208984), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.358859), 'test_loss': np.float64(152196.326831), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.960881), 'val_loss': np.float64(150133.208984), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.010301), 'test_avg_loss_bottom_decile': np.float64(24.270675), 'test_avg_loss_top_decile': np.float64(41.831168), 'test_avg_loss_min': np.float64(22.056625), 'test_avg_loss_max': np.float64(41.831168), 'test_avg_loss_bottom10%': np.float64(22.056625), 'test_avg_loss_top10%': np.float64(41.831168), 'test_avg_loss_cos1': np.float64(0.985749), 'test_avg_loss_entropy': np.float64(2.288697), 'test_loss_std': np.float64(25973.400723), 'test_loss_bottom_decile': np.float64(125819.178406), 'test_loss_top_decile': np.float64(216852.773132), 'test_loss_min': np.float64(114341.545776), 'test_loss_max': np.float64(216852.773132), 'test_loss_bottom10%': np.float64(114341.545776), 'test_loss_top10%': np.float64(216852.773132), 'test_loss_cos1': np.float64(0.985749), 'test_loss_entropy': np.float64(2.288697), 'val_avg_loss_std': np.float64(4.029237), 'val_avg_loss_bottom_decile': np.float64(23.609668), 'val_avg_loss_top_decile': np.float64(35.628255), 'val_avg_loss_min': np.float64(22.149994), 'val_avg_loss_max': np.float64(35.628255), 'val_avg_loss_bottom10%': np.float64(22.149994), 'val_avg_loss_top10%': np.float64(35.628255), 'val_avg_loss_cos1': np.float64(0.99046), 'val_avg_loss_entropy': np.float64(2.292789), 'val_loss_std': np.float64(20887.563108), 'val_loss_bottom_decile': np.float64(122392.516418), 'val_loss_top_decile': np.float64(184696.873535), 'val_loss_min': np.float64(114825.569824), 'val_loss_max': np.float64(184696.873535), 'val_loss_bottom10%': np.float64(114825.569824), 'val_loss_top10%': np.float64(184696.873535), 'val_loss_cos1': np.float64(0.99046), 'val_loss_entropy': np.float64(2.292789)}} 2024-11-14 20:05:14,715 (server:353) INFO: Server: Starting evaluation at the end of round 55. 2024-11-14 20:05:14,715 (server:359) INFO: ----------- Starting a new training round (Round #56) ------------- 2024-11-14 20:07:53,704 (client:354) INFO: {'Role': 'Client #5', 'Round': 56, 'Results_raw': {'train_loss': 23.686053, 'val_loss': 24.80933, 'test_loss': 33.7444}} 2024-11-14 20:08:51,631 (client:354) INFO: {'Role': 'Client #10', 'Round': 56, 'Results_raw': {'train_loss': 22.138089, 'val_loss': 22.050522, 'test_loss': 23.598532}} 2024-11-14 20:09:49,210 (client:354) INFO: {'Role': 'Client #7', 'Round': 56, 'Results_raw': {'train_loss': 22.961446, 'val_loss': 22.635817, 'test_loss': 23.351196}} 2024-11-14 20:10:46,153 (client:354) INFO: {'Role': 'Client #8', 'Round': 56, 'Results_raw': {'train_loss': 20.340955, 'val_loss': 19.911577, 'test_loss': 20.830306}} 2024-11-14 20:11:42,642 (client:354) INFO: {'Role': 'Client #9', 'Round': 56, 'Results_raw': {'train_loss': 29.698908, 'val_loss': 27.626329, 'test_loss': 27.008703}} 2024-11-14 20:12:40,370 (client:354) INFO: {'Role': 'Client #4', 'Round': 56, 'Results_raw': {'train_loss': 22.872775, 'val_loss': 21.379061, 'test_loss': 23.377195}} 2024-11-14 20:13:37,552 (client:354) INFO: {'Role': 'Client #3', 'Round': 56, 'Results_raw': {'train_loss': 16.419702, 'val_loss': 17.253628, 'test_loss': 20.759323}} 2024-11-14 20:14:34,441 (client:354) INFO: {'Role': 'Client #1', 'Round': 56, 'Results_raw': {'train_loss': 17.093756, 'val_loss': 16.115548, 'test_loss': 18.394552}} 2024-11-14 20:15:32,215 (client:354) INFO: {'Role': 'Client #2', 'Round': 56, 'Results_raw': {'train_loss': 13.141609, 'val_loss': 12.586872, 'test_loss': 13.406267}} 2024-11-14 20:16:29,211 (client:354) INFO: {'Role': 'Client #6', 'Round': 56, 'Results_raw': {'train_loss': 22.981604, 'val_loss': 21.643468, 'test_loss': 23.836729}} 2024-11-14 20:16:29,215 (server:615) INFO: {'Role': 'Server #', 'Round': 55, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.150226), 'test_loss': np.float64(151114.770538), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.730006), 'val_loss': np.float64(148936.349298), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.150226), 'test_loss': np.float64(151114.770538), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.730006), 'val_loss': np.float64(148936.349298), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.978601), 'test_avg_loss_bottom_decile': np.float64(24.065199), 'test_avg_loss_top_decile': np.float64(41.481787), 'test_avg_loss_min': np.float64(21.848062), 'test_avg_loss_max': np.float64(41.481787), 'test_avg_loss_bottom10%': np.float64(21.848062), 'test_avg_loss_top10%': np.float64(41.481787), 'test_avg_loss_cos1': np.float64(0.985727), 'test_avg_loss_entropy': np.float64(2.288654), 'test_loss_std': np.float64(25809.06795), 'test_loss_bottom_decile': np.float64(124753.99231), 'test_loss_top_decile': np.float64(215041.584534), 'test_loss_min': np.float64(113260.352173), 'test_loss_max': np.float64(215041.584534), 'test_loss_bottom10%': np.float64(113260.352173), 'test_loss_top10%': np.float64(215041.584534), 'test_loss_cos1': np.float64(0.985727), 'test_loss_entropy': np.float64(2.288654), 'val_avg_loss_std': np.float64(3.996536), 'val_avg_loss_bottom_decile': np.float64(23.387803), 'val_avg_loss_top_decile': np.float64(35.171672), 'val_avg_loss_min': np.float64(21.946487), 'val_avg_loss_max': np.float64(35.171672), 'val_avg_loss_bottom10%': np.float64(21.946487), 'val_avg_loss_top10%': np.float64(35.171672), 'val_avg_loss_cos1': np.float64(0.990463), 'val_avg_loss_entropy': np.float64(2.292779), 'val_loss_std': np.float64(20718.044084), 'val_loss_bottom_decile': np.float64(121242.371765), 'val_loss_top_decile': np.float64(182329.948547), 'val_loss_min': np.float64(113770.587646), 'val_loss_max': np.float64(182329.948547), 'val_loss_bottom10%': np.float64(113770.587646), 'val_loss_top10%': np.float64(182329.948547), 'val_loss_cos1': np.float64(0.990463), 'val_loss_entropy': np.float64(2.292779)}} 2024-11-14 20:16:29,259 (server:353) INFO: Server: Starting evaluation at the end of round 56. 2024-11-14 20:16:29,259 (server:359) INFO: ----------- Starting a new training round (Round #57) ------------- 2024-11-14 20:19:00,768 (client:354) INFO: {'Role': 'Client #2', 'Round': 57, 'Results_raw': {'train_loss': 13.150569, 'val_loss': 12.610688, 'test_loss': 13.626305}} 2024-11-14 20:19:58,886 (client:354) INFO: {'Role': 'Client #10', 'Round': 57, 'Results_raw': {'train_loss': 22.073373, 'val_loss': 22.047957, 'test_loss': 23.629407}} 2024-11-14 20:20:57,479 (client:354) INFO: {'Role': 'Client #5', 'Round': 57, 'Results_raw': {'train_loss': 23.695995, 'val_loss': 24.79631, 'test_loss': 33.392518}} 2024-11-14 20:21:55,212 (client:354) INFO: {'Role': 'Client #7', 'Round': 57, 'Results_raw': {'train_loss': 22.95454, 'val_loss': 22.521967, 'test_loss': 23.161581}} 2024-11-14 20:22:57,313 (client:354) INFO: {'Role': 'Client #4', 'Round': 57, 'Results_raw': {'train_loss': 22.868969, 'val_loss': 21.403467, 'test_loss': 23.320965}} 2024-11-14 20:23:59,213 (client:354) INFO: {'Role': 'Client #6', 'Round': 57, 'Results_raw': {'train_loss': 22.948147, 'val_loss': 21.842318, 'test_loss': 23.712061}} 2024-11-14 20:25:01,520 (client:354) INFO: {'Role': 'Client #8', 'Round': 57, 'Results_raw': {'train_loss': 20.305573, 'val_loss': 19.916928, 'test_loss': 20.996257}} 2024-11-14 20:26:03,541 (client:354) INFO: {'Role': 'Client #1', 'Round': 57, 'Results_raw': {'train_loss': 17.083911, 'val_loss': 16.09957, 'test_loss': 18.395806}} 2024-11-14 20:27:05,296 (client:354) INFO: {'Role': 'Client #9', 'Round': 57, 'Results_raw': {'train_loss': 29.721966, 'val_loss': 27.561433, 'test_loss': 26.934049}} 2024-11-14 20:28:06,808 (client:354) INFO: {'Role': 'Client #3', 'Round': 57, 'Results_raw': {'train_loss': 16.36915, 'val_loss': 17.001314, 'test_loss': 20.239517}} 2024-11-14 20:28:06,811 (server:615) INFO: {'Role': 'Server #', 'Round': 56, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.243177), 'test_loss': np.float64(151596.631134), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.854282), 'val_loss': np.float64(149580.59762), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.243177), 'test_loss': np.float64(151596.631134), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.854282), 'val_loss': np.float64(149580.59762), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.07651), 'test_avg_loss_bottom_decile': np.float64(24.028765), 'test_avg_loss_top_decile': np.float64(41.801363), 'test_avg_loss_min': np.float64(21.814326), 'test_avg_loss_max': np.float64(41.801363), 'test_avg_loss_bottom10%': np.float64(21.814326), 'test_avg_loss_top10%': np.float64(41.801363), 'test_avg_loss_cos1': np.float64(0.985264), 'test_avg_loss_entropy': np.float64(2.288196), 'test_loss_std': np.float64(26316.627389), 'test_loss_bottom_decile': np.float64(124565.118164), 'test_loss_top_decile': np.float64(216698.26709), 'test_loss_min': np.float64(113085.466919), 'test_loss_max': np.float64(216698.26709), 'test_loss_bottom10%': np.float64(113085.466919), 'test_loss_top10%': np.float64(216698.26709), 'test_loss_cos1': np.float64(0.985264), 'test_loss_entropy': np.float64(2.288196), 'val_avg_loss_std': np.float64(4.113162), 'val_avg_loss_bottom_decile': np.float64(23.36688), 'val_avg_loss_top_decile': np.float64(35.561652), 'val_avg_loss_min': np.float64(21.911033), 'val_avg_loss_max': np.float64(35.561652), 'val_avg_loss_bottom10%': np.float64(21.911033), 'val_avg_loss_top10%': np.float64(35.561652), 'val_avg_loss_cos1': np.float64(0.989992), 'val_avg_loss_entropy': np.float64(2.29229), 'val_loss_std': np.float64(21322.630792), 'val_loss_bottom_decile': np.float64(121133.903748), 'val_loss_top_decile': np.float64(184351.604553), 'val_loss_min': np.float64(113586.795532), 'val_loss_max': np.float64(184351.604553), 'val_loss_bottom10%': np.float64(113586.795532), 'val_loss_top10%': np.float64(184351.604553), 'val_loss_cos1': np.float64(0.989992), 'val_loss_entropy': np.float64(2.29229)}} 2024-11-14 20:28:06,848 (server:353) INFO: Server: Starting evaluation at the end of round 57. 2024-11-14 20:28:06,849 (server:359) INFO: ----------- Starting a new training round (Round #58) ------------- 2024-11-14 20:30:33,041 (client:354) INFO: {'Role': 'Client #6', 'Round': 58, 'Results_raw': {'train_loss': 22.94396, 'val_loss': 21.722713, 'test_loss': 23.732882}} 2024-11-14 20:31:27,711 (client:354) INFO: {'Role': 'Client #8', 'Round': 58, 'Results_raw': {'train_loss': 20.311654, 'val_loss': 19.906823, 'test_loss': 20.851086}} 2024-11-14 20:32:22,255 (client:354) INFO: {'Role': 'Client #10', 'Round': 58, 'Results_raw': {'train_loss': 22.089945, 'val_loss': 22.173154, 'test_loss': 23.991264}} 2024-11-14 20:33:17,375 (client:354) INFO: {'Role': 'Client #9', 'Round': 58, 'Results_raw': {'train_loss': 29.666824, 'val_loss': 27.769768, 'test_loss': 27.286791}} 2024-11-14 20:34:12,073 (client:354) INFO: {'Role': 'Client #4', 'Round': 58, 'Results_raw': {'train_loss': 22.873858, 'val_loss': 21.471516, 'test_loss': 23.029245}} 2024-11-14 20:35:06,602 (client:354) INFO: {'Role': 'Client #2', 'Round': 58, 'Results_raw': {'train_loss': 13.147296, 'val_loss': 12.572944, 'test_loss': 13.697114}} 2024-11-14 20:36:01,970 (client:354) INFO: {'Role': 'Client #3', 'Round': 58, 'Results_raw': {'train_loss': 16.356742, 'val_loss': 16.927833, 'test_loss': 20.305444}} 2024-11-14 20:36:56,268 (client:354) INFO: {'Role': 'Client #5', 'Round': 58, 'Results_raw': {'train_loss': 23.702928, 'val_loss': 25.174538, 'test_loss': 34.122022}} 2024-11-14 20:37:51,356 (client:354) INFO: {'Role': 'Client #7', 'Round': 58, 'Results_raw': {'train_loss': 22.9042, 'val_loss': 22.582781, 'test_loss': 23.308936}} 2024-11-14 20:38:46,599 (client:354) INFO: {'Role': 'Client #1', 'Round': 58, 'Results_raw': {'train_loss': 17.045081, 'val_loss': 16.171516, 'test_loss': 18.580664}} 2024-11-14 20:38:46,602 (server:615) INFO: {'Role': 'Server #', 'Round': 57, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.190917), 'test_loss': np.float64(151325.712567), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.801749), 'val_loss': np.float64(149308.264337), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.190917), 'test_loss': np.float64(151325.712567), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.801749), 'val_loss': np.float64(149308.264337), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.026452), 'test_avg_loss_bottom_decile': np.float64(24.028127), 'test_avg_loss_top_decile': np.float64(41.648721), 'test_avg_loss_min': np.float64(21.906032), 'test_avg_loss_max': np.float64(41.648721), 'test_avg_loss_bottom10%': np.float64(21.906032), 'test_avg_loss_top10%': np.float64(41.648721), 'test_avg_loss_cos1': np.float64(0.985497), 'test_avg_loss_entropy': np.float64(2.28844), 'test_loss_std': np.float64(26057.129061), 'test_loss_bottom_decile': np.float64(124561.812805), 'test_loss_top_decile': np.float64(215906.970154), 'test_loss_min': np.float64(113560.869934), 'test_loss_max': np.float64(215906.970154), 'test_loss_bottom10%': np.float64(113560.869934), 'test_loss_top10%': np.float64(215906.970154), 'test_loss_cos1': np.float64(0.985497), 'test_loss_entropy': np.float64(2.28844), 'val_avg_loss_std': np.float64(4.070666), 'val_avg_loss_bottom_decile': np.float64(23.351441), 'val_avg_loss_top_decile': np.float64(35.406425), 'val_avg_loss_min': np.float64(21.982702), 'val_avg_loss_max': np.float64(35.406425), 'val_avg_loss_bottom10%': np.float64(21.982702), 'val_avg_loss_top10%': np.float64(35.406425), 'val_avg_loss_cos1': np.float64(0.99016), 'val_avg_loss_entropy': np.float64(2.292474), 'val_loss_std': np.float64(21102.33248), 'val_loss_bottom_decile': np.float64(121053.872131), 'val_loss_top_decile': np.float64(183546.908569), 'val_loss_min': np.float64(113958.326599), 'val_loss_max': np.float64(183546.908569), 'val_loss_bottom10%': np.float64(113958.326599), 'val_loss_top10%': np.float64(183546.908569), 'val_loss_cos1': np.float64(0.99016), 'val_loss_entropy': np.float64(2.292474)}} 2024-11-14 20:38:46,645 (server:353) INFO: Server: Starting evaluation at the end of round 58. 2024-11-14 20:38:46,646 (server:359) INFO: ----------- Starting a new training round (Round #59) ------------- 2024-11-14 20:41:15,068 (client:354) INFO: {'Role': 'Client #1', 'Round': 59, 'Results_raw': {'train_loss': 17.072565, 'val_loss': 16.050701, 'test_loss': 18.468341}} 2024-11-14 20:42:08,822 (client:354) INFO: {'Role': 'Client #10', 'Round': 59, 'Results_raw': {'train_loss': 22.061709, 'val_loss': 22.099411, 'test_loss': 23.804121}} 2024-11-14 20:43:02,763 (client:354) INFO: {'Role': 'Client #5', 'Round': 59, 'Results_raw': {'train_loss': 23.622659, 'val_loss': 24.933143, 'test_loss': 33.924613}} 2024-11-14 20:43:57,786 (client:354) INFO: {'Role': 'Client #3', 'Round': 59, 'Results_raw': {'train_loss': 16.362112, 'val_loss': 16.86312, 'test_loss': 20.230536}} 2024-11-14 20:44:52,799 (client:354) INFO: {'Role': 'Client #8', 'Round': 59, 'Results_raw': {'train_loss': 20.245443, 'val_loss': 20.035724, 'test_loss': 21.11823}} 2024-11-14 20:45:48,973 (client:354) INFO: {'Role': 'Client #2', 'Round': 59, 'Results_raw': {'train_loss': 13.122272, 'val_loss': 12.492454, 'test_loss': 13.479029}} 2024-11-14 20:46:44,474 (client:354) INFO: {'Role': 'Client #6', 'Round': 59, 'Results_raw': {'train_loss': 22.913767, 'val_loss': 21.686989, 'test_loss': 23.628749}} 2024-11-14 20:47:39,033 (client:354) INFO: {'Role': 'Client #7', 'Round': 59, 'Results_raw': {'train_loss': 22.932078, 'val_loss': 22.54302, 'test_loss': 23.122302}} 2024-11-14 20:48:33,770 (client:354) INFO: {'Role': 'Client #9', 'Round': 59, 'Results_raw': {'train_loss': 29.651371, 'val_loss': 27.814239, 'test_loss': 27.053581}} 2024-11-14 20:49:28,731 (client:354) INFO: {'Role': 'Client #4', 'Round': 59, 'Results_raw': {'train_loss': 22.845144, 'val_loss': 21.568193, 'test_loss': 23.336213}} 2024-11-14 20:49:28,742 (server:615) INFO: {'Role': 'Server #', 'Round': 58, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.205487), 'test_loss': np.float64(151401.246985), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.799532), 'val_loss': np.float64(149296.77135), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.205487), 'test_loss': np.float64(151401.246985), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.799532), 'val_loss': np.float64(149296.77135), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.027242), 'test_avg_loss_bottom_decile': np.float64(24.053722), 'test_avg_loss_top_decile': np.float64(41.605694), 'test_avg_loss_min': np.float64(21.839741), 'test_avg_loss_max': np.float64(41.605694), 'test_avg_loss_bottom10%': np.float64(21.839741), 'test_avg_loss_top10%': np.float64(41.605694), 'test_avg_loss_cos1': np.float64(0.985506), 'test_avg_loss_entropy': np.float64(2.288425), 'test_loss_std': np.float64(26061.223318), 'test_loss_bottom_decile': np.float64(124694.495789), 'test_loss_top_decile': np.float64(215683.916809), 'test_loss_min': np.float64(113217.21991), 'test_loss_max': np.float64(215683.916809), 'test_loss_bottom10%': np.float64(113217.21991), 'test_loss_top10%': np.float64(215683.916809), 'test_loss_cos1': np.float64(0.985506), 'test_loss_entropy': np.float64(2.288425), 'val_avg_loss_std': np.float64(4.090176), 'val_avg_loss_bottom_decile': np.float64(23.354692), 'val_avg_loss_top_decile': np.float64(35.338308), 'val_avg_loss_min': np.float64(21.890487), 'val_avg_loss_max': np.float64(35.338308), 'val_avg_loss_bottom10%': np.float64(21.890487), 'val_avg_loss_top10%': np.float64(35.338308), 'val_avg_loss_cos1': np.float64(0.990065), 'val_avg_loss_entropy': np.float64(2.292361), 'val_loss_std': np.float64(21203.469853), 'val_loss_bottom_decile': np.float64(121070.722229), 'val_loss_top_decile': np.float64(183193.78656), 'val_loss_min': np.float64(113480.286621), 'val_loss_max': np.float64(183193.78656), 'val_loss_bottom10%': np.float64(113480.286621), 'val_loss_top10%': np.float64(183193.78656), 'val_loss_cos1': np.float64(0.990065), 'val_loss_entropy': np.float64(2.292361)}} 2024-11-14 20:49:28,771 (server:353) INFO: Server: Starting evaluation at the end of round 59. 2024-11-14 20:49:28,771 (server:359) INFO: ----------- Starting a new training round (Round #60) ------------- 2024-11-14 20:51:55,603 (client:354) INFO: {'Role': 'Client #4', 'Round': 60, 'Results_raw': {'train_loss': 22.809624, 'val_loss': 21.392093, 'test_loss': 23.210605}} 2024-11-14 20:52:49,872 (client:354) INFO: {'Role': 'Client #10', 'Round': 60, 'Results_raw': {'train_loss': 22.042971, 'val_loss': 22.075139, 'test_loss': 23.871936}} 2024-11-14 20:53:43,939 (client:354) INFO: {'Role': 'Client #1', 'Round': 60, 'Results_raw': {'train_loss': 17.058291, 'val_loss': 16.231608, 'test_loss': 18.622999}} 2024-11-14 20:54:44,448 (client:354) INFO: {'Role': 'Client #9', 'Round': 60, 'Results_raw': {'train_loss': 29.623562, 'val_loss': 27.833138, 'test_loss': 27.222596}} 2024-11-14 20:55:45,725 (client:354) INFO: {'Role': 'Client #5', 'Round': 60, 'Results_raw': {'train_loss': 23.617518, 'val_loss': 24.864718, 'test_loss': 33.884807}} 2024-11-14 20:56:43,950 (client:354) INFO: {'Role': 'Client #3', 'Round': 60, 'Results_raw': {'train_loss': 16.353773, 'val_loss': 16.947267, 'test_loss': 20.064015}} 2024-11-14 20:57:43,306 (client:354) INFO: {'Role': 'Client #7', 'Round': 60, 'Results_raw': {'train_loss': 22.897574, 'val_loss': 22.520659, 'test_loss': 23.451857}} 2024-11-14 20:58:41,051 (client:354) INFO: {'Role': 'Client #2', 'Round': 60, 'Results_raw': {'train_loss': 13.129979, 'val_loss': 12.602042, 'test_loss': 13.697247}} 2024-11-14 20:59:37,534 (client:354) INFO: {'Role': 'Client #6', 'Round': 60, 'Results_raw': {'train_loss': 22.900236, 'val_loss': 21.997267, 'test_loss': 24.474632}} 2024-11-14 21:00:31,631 (client:354) INFO: {'Role': 'Client #8', 'Round': 60, 'Results_raw': {'train_loss': 20.289221, 'val_loss': 19.88275, 'test_loss': 21.104604}} 2024-11-14 21:00:31,634 (server:615) INFO: {'Role': 'Server #', 'Round': 59, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.214013), 'test_loss': np.float64(151445.445874), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.803081), 'val_loss': np.float64(149315.173267), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.214013), 'test_loss': np.float64(151445.445874), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.803081), 'val_loss': np.float64(149315.173267), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.081129), 'test_avg_loss_bottom_decile': np.float64(24.040092), 'test_avg_loss_top_decile': np.float64(41.692545), 'test_avg_loss_min': np.float64(21.653031), 'test_avg_loss_max': np.float64(41.692545), 'test_avg_loss_bottom10%': np.float64(21.653031), 'test_avg_loss_top10%': np.float64(41.692545), 'test_avg_loss_cos1': np.float64(0.985209), 'test_avg_loss_entropy': np.float64(2.288106), 'test_loss_std': np.float64(26340.574885), 'test_loss_bottom_decile': np.float64(124623.837891), 'test_loss_top_decile': np.float64(216134.15332), 'test_loss_min': np.float64(112249.314697), 'test_loss_max': np.float64(216134.15332), 'test_loss_bottom10%': np.float64(112249.314697), 'test_loss_top10%': np.float64(216134.15332), 'test_loss_cos1': np.float64(0.985209), 'test_loss_entropy': np.float64(2.288106), 'val_avg_loss_std': np.float64(4.149685), 'val_avg_loss_bottom_decile': np.float64(23.320119), 'val_avg_loss_top_decile': np.float64(35.433614), 'val_avg_loss_min': np.float64(21.701788), 'val_avg_loss_max': np.float64(35.433614), 'val_avg_loss_bottom10%': np.float64(21.701788), 'val_avg_loss_top10%': np.float64(35.433614), 'val_avg_loss_cos1': np.float64(0.989781), 'val_avg_loss_entropy': np.float64(2.292048), 'val_loss_std': np.float64(21511.968322), 'val_loss_bottom_decile': np.float64(120891.49646), 'val_loss_top_decile': np.float64(183687.856934), 'val_loss_min': np.float64(112502.068054), 'val_loss_max': np.float64(183687.856934), 'val_loss_bottom10%': np.float64(112502.068054), 'val_loss_top10%': np.float64(183687.856934), 'val_loss_cos1': np.float64(0.989781), 'val_loss_entropy': np.float64(2.292048)}} 2024-11-14 21:00:31,679 (server:353) INFO: Server: Starting evaluation at the end of round 60. 2024-11-14 21:00:31,680 (server:359) INFO: ----------- Starting a new training round (Round #61) ------------- 2024-11-14 21:03:16,385 (client:354) INFO: {'Role': 'Client #6', 'Round': 61, 'Results_raw': {'train_loss': 22.905579, 'val_loss': 21.90536, 'test_loss': 23.551847}} 2024-11-14 21:04:17,384 (client:354) INFO: {'Role': 'Client #2', 'Round': 61, 'Results_raw': {'train_loss': 13.101125, 'val_loss': 12.505323, 'test_loss': 13.508622}} 2024-11-14 21:05:15,710 (client:354) INFO: {'Role': 'Client #8', 'Round': 61, 'Results_raw': {'train_loss': 20.284085, 'val_loss': 20.060062, 'test_loss': 21.213497}} 2024-11-14 21:06:13,502 (client:354) INFO: {'Role': 'Client #4', 'Round': 61, 'Results_raw': {'train_loss': 22.793176, 'val_loss': 21.527312, 'test_loss': 23.441077}} 2024-11-14 21:07:11,477 (client:354) INFO: {'Role': 'Client #5', 'Round': 61, 'Results_raw': {'train_loss': 23.574098, 'val_loss': 24.87723, 'test_loss': 33.781097}} 2024-11-14 21:08:05,452 (client:354) INFO: {'Role': 'Client #7', 'Round': 61, 'Results_raw': {'train_loss': 22.871797, 'val_loss': 22.46215, 'test_loss': 23.449694}} 2024-11-14 21:08:59,762 (client:354) INFO: {'Role': 'Client #9', 'Round': 61, 'Results_raw': {'train_loss': 29.63351, 'val_loss': 27.995341, 'test_loss': 27.584785}} 2024-11-14 21:09:53,938 (client:354) INFO: {'Role': 'Client #10', 'Round': 61, 'Results_raw': {'train_loss': 22.027388, 'val_loss': 22.158752, 'test_loss': 23.880038}} 2024-11-14 21:10:48,767 (client:354) INFO: {'Role': 'Client #1', 'Round': 61, 'Results_raw': {'train_loss': 17.058317, 'val_loss': 16.12847, 'test_loss': 18.400608}} 2024-11-14 21:11:42,788 (client:354) INFO: {'Role': 'Client #3', 'Round': 61, 'Results_raw': {'train_loss': 16.340248, 'val_loss': 17.054832, 'test_loss': 20.674278}} 2024-11-14 21:11:42,791 (server:615) INFO: {'Role': 'Server #', 'Round': 60, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.189603), 'test_loss': np.float64(151318.902527), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.752826), 'val_loss': np.float64(149054.64986), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.189603), 'test_loss': np.float64(151318.902527), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.752826), 'val_loss': np.float64(149054.64986), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.992514), 'test_avg_loss_bottom_decile': np.float64(24.055109), 'test_avg_loss_top_decile': np.float64(41.58567), 'test_avg_loss_min': np.float64(21.97684), 'test_avg_loss_max': np.float64(41.58567), 'test_avg_loss_bottom10%': np.float64(21.97684), 'test_avg_loss_top10%': np.float64(41.58567), 'test_avg_loss_cos1': np.float64(0.985686), 'test_avg_loss_entropy': np.float64(2.288633), 'test_loss_std': np.float64(25881.190155), 'test_loss_bottom_decile': np.float64(124701.685669), 'test_loss_top_decile': np.float64(215580.114319), 'test_loss_min': np.float64(113927.940796), 'test_loss_max': np.float64(215580.114319), 'test_loss_bottom10%': np.float64(113927.940796), 'test_loss_top10%': np.float64(215580.114319), 'test_loss_cos1': np.float64(0.985686), 'test_loss_entropy': np.float64(2.288633), 'val_avg_loss_std': np.float64(4.027181), 'val_avg_loss_bottom_decile': np.float64(23.331981), 'val_avg_loss_top_decile': np.float64(35.276221), 'val_avg_loss_min': np.float64(22.016609), 'val_avg_loss_max': np.float64(35.276221), 'val_avg_loss_bottom10%': np.float64(22.016609), 'val_avg_loss_top10%': np.float64(35.276221), 'val_avg_loss_cos1': np.float64(0.990333), 'val_avg_loss_entropy': np.float64(2.292651), 'val_loss_std': np.float64(20876.906969), 'val_loss_bottom_decile': np.float64(120952.987671), 'val_loss_top_decile': np.float64(182871.928589), 'val_loss_min': np.float64(114134.102295), 'val_loss_max': np.float64(182871.928589), 'val_loss_bottom10%': np.float64(114134.102295), 'val_loss_top10%': np.float64(182871.928589), 'val_loss_cos1': np.float64(0.990333), 'val_loss_entropy': np.float64(2.292651)}} 2024-11-14 21:11:42,825 (server:353) INFO: Server: Starting evaluation at the end of round 61. 2024-11-14 21:11:42,826 (server:359) INFO: ----------- Starting a new training round (Round #62) ------------- 2024-11-14 21:14:09,298 (client:354) INFO: {'Role': 'Client #4', 'Round': 62, 'Results_raw': {'train_loss': 22.818131, 'val_loss': 21.460799, 'test_loss': 23.364102}} 2024-11-14 21:15:03,942 (client:354) INFO: {'Role': 'Client #8', 'Round': 62, 'Results_raw': {'train_loss': 20.247378, 'val_loss': 19.934626, 'test_loss': 21.04126}} 2024-11-14 21:15:58,568 (client:354) INFO: {'Role': 'Client #3', 'Round': 62, 'Results_raw': {'train_loss': 16.320373, 'val_loss': 16.871307, 'test_loss': 20.232165}} 2024-11-14 21:16:52,532 (client:354) INFO: {'Role': 'Client #10', 'Round': 62, 'Results_raw': {'train_loss': 22.031059, 'val_loss': 22.030438, 'test_loss': 23.804195}} 2024-11-14 21:17:46,920 (client:354) INFO: {'Role': 'Client #2', 'Round': 62, 'Results_raw': {'train_loss': 13.104265, 'val_loss': 12.511931, 'test_loss': 13.49809}} 2024-11-14 21:18:41,225 (client:354) INFO: {'Role': 'Client #6', 'Round': 62, 'Results_raw': {'train_loss': 22.873863, 'val_loss': 21.706108, 'test_loss': 23.476605}} 2024-11-14 21:19:35,313 (client:354) INFO: {'Role': 'Client #9', 'Round': 62, 'Results_raw': {'train_loss': 29.583752, 'val_loss': 27.638311, 'test_loss': 27.070948}} 2024-11-14 21:20:29,445 (client:354) INFO: {'Role': 'Client #5', 'Round': 62, 'Results_raw': {'train_loss': 23.60009, 'val_loss': 24.92737, 'test_loss': 33.806105}} 2024-11-14 21:21:25,616 (client:354) INFO: {'Role': 'Client #7', 'Round': 62, 'Results_raw': {'train_loss': 22.925316, 'val_loss': 22.43558, 'test_loss': 23.113729}} 2024-11-14 21:22:20,126 (client:354) INFO: {'Role': 'Client #1', 'Round': 62, 'Results_raw': {'train_loss': 17.015541, 'val_loss': 16.03447, 'test_loss': 18.287069}} 2024-11-14 21:22:20,129 (server:615) INFO: {'Role': 'Server #', 'Round': 61, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.097506), 'test_loss': np.float64(150841.472308), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.6691), 'val_loss': np.float64(148620.614221), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.097506), 'test_loss': np.float64(150841.472308), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.6691), 'val_loss': np.float64(148620.614221), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.999399), 'test_avg_loss_bottom_decile': np.float64(23.940151), 'test_avg_loss_top_decile': np.float64(41.447367), 'test_avg_loss_min': np.float64(21.791472), 'test_avg_loss_max': np.float64(41.447367), 'test_avg_loss_bottom10%': np.float64(21.791472), 'test_avg_loss_top10%': np.float64(41.447367), 'test_avg_loss_cos1': np.float64(0.985559), 'test_avg_loss_entropy': np.float64(2.288482), 'test_loss_std': np.float64(25916.884893), 'test_loss_bottom_decile': np.float64(124105.743225), 'test_loss_top_decile': np.float64(214863.151794), 'test_loss_min': np.float64(112966.991028), 'test_loss_max': np.float64(214863.151794), 'test_loss_bottom10%': np.float64(112966.991028), 'test_loss_top10%': np.float64(214863.151794), 'test_loss_cos1': np.float64(0.985559), 'test_loss_entropy': np.float64(2.288482), 'val_avg_loss_std': np.float64(4.052856), 'val_avg_loss_bottom_decile': np.float64(23.22812), 'val_avg_loss_top_decile': np.float64(35.171357), 'val_avg_loss_min': np.float64(21.815523), 'val_avg_loss_max': np.float64(35.171357), 'val_avg_loss_bottom10%': np.float64(21.815523), 'val_avg_loss_top10%': np.float64(35.171357), 'val_avg_loss_cos1': np.float64(0.990155), 'val_avg_loss_entropy': np.float64(2.292456), 'val_loss_std': np.float64(21010.005751), 'val_loss_bottom_decile': np.float64(120414.575745), 'val_loss_top_decile': np.float64(182328.313965), 'val_loss_min': np.float64(113091.67157), 'val_loss_max': np.float64(182328.313965), 'val_loss_bottom10%': np.float64(113091.67157), 'val_loss_top10%': np.float64(182328.313965), 'val_loss_cos1': np.float64(0.990155), 'val_loss_entropy': np.float64(2.292456)}} 2024-11-14 21:22:20,162 (server:353) INFO: Server: Starting evaluation at the end of round 62. 2024-11-14 21:22:20,163 (server:359) INFO: ----------- Starting a new training round (Round #63) ------------- 2024-11-14 21:24:46,979 (client:354) INFO: {'Role': 'Client #8', 'Round': 63, 'Results_raw': {'train_loss': 20.223381, 'val_loss': 20.095993, 'test_loss': 21.205154}} 2024-11-14 21:25:41,075 (client:354) INFO: {'Role': 'Client #3', 'Round': 63, 'Results_raw': {'train_loss': 16.305397, 'val_loss': 16.772403, 'test_loss': 19.752272}} 2024-11-14 21:26:36,332 (client:354) INFO: {'Role': 'Client #10', 'Round': 63, 'Results_raw': {'train_loss': 21.989414, 'val_loss': 22.048953, 'test_loss': 23.82914}} 2024-11-14 21:27:30,417 (client:354) INFO: {'Role': 'Client #1', 'Round': 63, 'Results_raw': {'train_loss': 17.026709, 'val_loss': 16.130507, 'test_loss': 18.300111}} 2024-11-14 21:28:24,675 (client:354) INFO: {'Role': 'Client #9', 'Round': 63, 'Results_raw': {'train_loss': 29.566415, 'val_loss': 27.62807, 'test_loss': 27.097884}} 2024-11-14 21:29:18,418 (client:354) INFO: {'Role': 'Client #7', 'Round': 63, 'Results_raw': {'train_loss': 22.870147, 'val_loss': 22.772501, 'test_loss': 23.311194}} 2024-11-14 21:30:14,534 (client:354) INFO: {'Role': 'Client #4', 'Round': 63, 'Results_raw': {'train_loss': 22.781271, 'val_loss': 21.594654, 'test_loss': 23.501078}} 2024-11-14 21:31:08,233 (client:354) INFO: {'Role': 'Client #5', 'Round': 63, 'Results_raw': {'train_loss': 23.596429, 'val_loss': 25.029341, 'test_loss': 34.113183}} 2024-11-14 21:32:02,784 (client:354) INFO: {'Role': 'Client #2', 'Round': 63, 'Results_raw': {'train_loss': 13.107004, 'val_loss': 12.499319, 'test_loss': 13.337547}} 2024-11-14 21:32:57,808 (client:354) INFO: {'Role': 'Client #6', 'Round': 63, 'Results_raw': {'train_loss': 22.850753, 'val_loss': 21.653211, 'test_loss': 23.719749}} 2024-11-14 21:32:57,811 (server:615) INFO: {'Role': 'Server #', 'Round': 62, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.051828), 'test_loss': np.float64(150604.676001), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.627511), 'val_loss': np.float64(148405.018658), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.051828), 'test_loss': np.float64(150604.676001), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.627511), 'val_loss': np.float64(148405.018658), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.051493), 'test_avg_loss_bottom_decile': np.float64(23.849021), 'test_avg_loss_top_decile': np.float64(41.53333), 'test_avg_loss_min': np.float64(21.691203), 'test_avg_loss_max': np.float64(41.53333), 'test_avg_loss_bottom10%': np.float64(21.691203), 'test_avg_loss_top10%': np.float64(41.53333), 'test_avg_loss_cos1': np.float64(0.985217), 'test_avg_loss_entropy': np.float64(2.288151), 'test_loss_std': np.float64(26186.940441), 'test_loss_bottom_decile': np.float64(123633.327454), 'test_loss_top_decile': np.float64(215308.783081), 'test_loss_min': np.float64(112447.196655), 'test_loss_max': np.float64(215308.783081), 'test_loss_bottom10%': np.float64(112447.196655), 'test_loss_top10%': np.float64(215308.783081), 'test_loss_cos1': np.float64(0.985217), 'test_loss_entropy': np.float64(2.288151), 'val_avg_loss_std': np.float64(4.104057), 'val_avg_loss_bottom_decile': np.float64(23.109817), 'val_avg_loss_top_decile': np.float64(35.206109), 'val_avg_loss_min': np.float64(21.729452), 'val_avg_loss_max': np.float64(35.206109), 'val_avg_loss_bottom10%': np.float64(21.729452), 'val_avg_loss_top10%': np.float64(35.206109), 'val_avg_loss_cos1': np.float64(0.98988), 'val_avg_loss_entropy': np.float64(2.29217), 'val_loss_std': np.float64(21275.430688), 'val_loss_bottom_decile': np.float64(119801.292297), 'val_loss_top_decile': np.float64(182508.468506), 'val_loss_min': np.float64(112645.4776), 'val_loss_max': np.float64(182508.468506), 'val_loss_bottom10%': np.float64(112645.4776), 'val_loss_top10%': np.float64(182508.468506), 'val_loss_cos1': np.float64(0.98988), 'val_loss_entropy': np.float64(2.29217)}} 2024-11-14 21:32:57,843 (server:353) INFO: Server: Starting evaluation at the end of round 63. 2024-11-14 21:32:57,843 (server:359) INFO: ----------- Starting a new training round (Round #64) ------------- 2024-11-14 21:35:30,252 (client:354) INFO: {'Role': 'Client #10', 'Round': 64, 'Results_raw': {'train_loss': 21.965729, 'val_loss': 22.151341, 'test_loss': 24.073895}} 2024-11-14 21:36:26,921 (client:354) INFO: {'Role': 'Client #9', 'Round': 64, 'Results_raw': {'train_loss': 29.535337, 'val_loss': 27.918051, 'test_loss': 27.339378}} 2024-11-14 21:37:23,444 (client:354) INFO: {'Role': 'Client #1', 'Round': 64, 'Results_raw': {'train_loss': 17.001554, 'val_loss': 16.253907, 'test_loss': 18.602544}} 2024-11-14 21:38:20,711 (client:354) INFO: {'Role': 'Client #5', 'Round': 64, 'Results_raw': {'train_loss': 23.538797, 'val_loss': 24.917994, 'test_loss': 33.99458}} 2024-11-14 21:39:17,594 (client:354) INFO: {'Role': 'Client #2', 'Round': 64, 'Results_raw': {'train_loss': 13.110748, 'val_loss': 12.382524, 'test_loss': 13.292743}} 2024-11-14 21:40:15,676 (client:354) INFO: {'Role': 'Client #7', 'Round': 64, 'Results_raw': {'train_loss': 22.840201, 'val_loss': 22.498972, 'test_loss': 23.284746}} 2024-11-14 21:41:17,840 (client:354) INFO: {'Role': 'Client #6', 'Round': 64, 'Results_raw': {'train_loss': 22.824683, 'val_loss': 21.765606, 'test_loss': 23.71009}} 2024-11-14 21:42:20,097 (client:354) INFO: {'Role': 'Client #8', 'Round': 64, 'Results_raw': {'train_loss': 20.225464, 'val_loss': 19.90112, 'test_loss': 21.003403}} 2024-11-14 21:43:18,239 (client:354) INFO: {'Role': 'Client #3', 'Round': 64, 'Results_raw': {'train_loss': 16.299614, 'val_loss': 17.068378, 'test_loss': 20.197576}} 2024-11-14 21:44:16,289 (client:354) INFO: {'Role': 'Client #4', 'Round': 64, 'Results_raw': {'train_loss': 22.759115, 'val_loss': 21.418134, 'test_loss': 23.029438}} 2024-11-14 21:44:16,294 (server:615) INFO: {'Role': 'Server #', 'Round': 63, 'Results_weighted_avg': {'test_avg_loss': np.float64(28.926932), 'test_loss': np.float64(149957.213831), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.500204), 'val_loss': np.float64(147745.058276), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(28.926932), 'test_loss': np.float64(149957.213831), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.500204), 'val_loss': np.float64(147745.058276), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.04893), 'test_avg_loss_bottom_decile': np.float64(23.778394), 'test_avg_loss_top_decile': np.float64(41.313481), 'test_avg_loss_min': np.float64(21.469563), 'test_avg_loss_max': np.float64(41.313481), 'test_avg_loss_bottom10%': np.float64(21.469563), 'test_avg_loss_top10%': np.float64(41.313481), 'test_avg_loss_cos1': np.float64(0.985107), 'test_avg_loss_entropy': np.float64(2.28801), 'test_loss_std': np.float64(26173.651266), 'test_loss_bottom_decile': np.float64(123267.196655), 'test_loss_top_decile': np.float64(214169.086548), 'test_loss_min': np.float64(111298.214661), 'test_loss_max': np.float64(214169.086548), 'test_loss_bottom10%': np.float64(111298.214661), 'test_loss_top10%': np.float64(214169.086548), 'test_loss_cos1': np.float64(0.985107), 'test_loss_entropy': np.float64(2.28801), 'val_avg_loss_std': np.float64(4.109199), 'val_avg_loss_bottom_decile': np.float64(23.080048), 'val_avg_loss_top_decile': np.float64(34.94553), 'val_avg_loss_min': np.float64(21.52009), 'val_avg_loss_max': np.float64(34.94553), 'val_avg_loss_bottom10%': np.float64(21.52009), 'val_avg_loss_top10%': np.float64(34.94553), 'val_avg_loss_cos1': np.float64(0.989765), 'val_avg_loss_entropy': np.float64(2.292035), 'val_loss_std': np.float64(21302.088288), 'val_loss_bottom_decile': np.float64(119646.970093), 'val_loss_top_decile': np.float64(181157.628174), 'val_loss_min': np.float64(111560.148804), 'val_loss_max': np.float64(181157.628174), 'val_loss_bottom10%': np.float64(111560.148804), 'val_loss_top10%': np.float64(181157.628174), 'val_loss_cos1': np.float64(0.989765), 'val_loss_entropy': np.float64(2.292035)}} 2024-11-14 21:44:16,339 (server:353) INFO: Server: Starting evaluation at the end of round 64. 2024-11-14 21:44:16,339 (server:359) INFO: ----------- Starting a new training round (Round #65) ------------- 2024-11-14 21:46:40,183 (client:354) INFO: {'Role': 'Client #2', 'Round': 65, 'Results_raw': {'train_loss': 13.059127, 'val_loss': 12.603493, 'test_loss': 13.530367}} 2024-11-14 21:47:33,736 (client:354) INFO: {'Role': 'Client #5', 'Round': 65, 'Results_raw': {'train_loss': 23.522461, 'val_loss': 24.927934, 'test_loss': 33.800032}} 2024-11-14 21:48:26,109 (client:354) INFO: {'Role': 'Client #10', 'Round': 65, 'Results_raw': {'train_loss': 21.932941, 'val_loss': 22.02224, 'test_loss': 24.074643}} 2024-11-14 21:49:19,328 (client:354) INFO: {'Role': 'Client #1', 'Round': 65, 'Results_raw': {'train_loss': 16.955581, 'val_loss': 16.047751, 'test_loss': 18.45793}} 2024-11-14 21:50:11,656 (client:354) INFO: {'Role': 'Client #7', 'Round': 65, 'Results_raw': {'train_loss': 22.822764, 'val_loss': 22.658901, 'test_loss': 23.357956}} 2024-11-14 21:51:04,351 (client:354) INFO: {'Role': 'Client #3', 'Round': 65, 'Results_raw': {'train_loss': 16.29542, 'val_loss': 17.0426, 'test_loss': 20.386503}} 2024-11-14 21:51:56,675 (client:354) INFO: {'Role': 'Client #9', 'Round': 65, 'Results_raw': {'train_loss': 29.533178, 'val_loss': 28.019149, 'test_loss': 27.619974}} 2024-11-14 21:52:48,914 (client:354) INFO: {'Role': 'Client #8', 'Round': 65, 'Results_raw': {'train_loss': 20.223671, 'val_loss': 19.895853, 'test_loss': 21.004428}} 2024-11-14 21:53:40,608 (client:354) INFO: {'Role': 'Client #4', 'Round': 65, 'Results_raw': {'train_loss': 22.744734, 'val_loss': 21.550922, 'test_loss': 23.401443}} 2024-11-14 21:54:32,361 (client:354) INFO: {'Role': 'Client #6', 'Round': 65, 'Results_raw': {'train_loss': 22.806784, 'val_loss': 21.658428, 'test_loss': 23.831095}} 2024-11-14 21:54:32,365 (server:615) INFO: {'Role': 'Server #', 'Round': 64, 'Results_weighted_avg': {'test_avg_loss': np.float64(28.993404), 'test_loss': np.float64(150301.805585), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.538774), 'val_loss': np.float64(147945.004028), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(28.993404), 'test_loss': np.float64(150301.805585), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.538774), 'val_loss': np.float64(147945.004028), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.025027), 'test_avg_loss_bottom_decile': np.float64(23.85018), 'test_avg_loss_top_decile': np.float64(41.40698), 'test_avg_loss_min': np.float64(21.667653), 'test_avg_loss_max': np.float64(41.40698), 'test_avg_loss_bottom10%': np.float64(21.667653), 'test_avg_loss_top10%': np.float64(41.40698), 'test_avg_loss_cos1': np.float64(0.985311), 'test_avg_loss_entropy': np.float64(2.288244), 'test_loss_std': np.float64(26049.742247), 'test_loss_bottom_decile': np.float64(123639.333496), 'test_loss_top_decile': np.float64(214653.784546), 'test_loss_min': np.float64(112325.115234), 'test_loss_max': np.float64(214653.784546), 'test_loss_bottom10%': np.float64(112325.115234), 'test_loss_top10%': np.float64(214653.784546), 'test_loss_cos1': np.float64(0.985311), 'test_loss_entropy': np.float64(2.288244), 'val_avg_loss_std': np.float64(4.067102), 'val_avg_loss_bottom_decile': np.float64(23.102666), 'val_avg_loss_top_decile': np.float64(35.0385), 'val_avg_loss_min': np.float64(21.686743), 'val_avg_loss_max': np.float64(35.0385), 'val_avg_loss_bottom10%': np.float64(21.686743), 'val_avg_loss_top10%': np.float64(35.0385), 'val_avg_loss_cos1': np.float64(0.989997), 'val_avg_loss_entropy': np.float64(2.292295), 'val_loss_std': np.float64(21083.85683), 'val_loss_bottom_decile': np.float64(119764.223083), 'val_loss_top_decile': np.float64(181639.584106), 'val_loss_min': np.float64(112424.077393), 'val_loss_max': np.float64(181639.584106), 'val_loss_bottom10%': np.float64(112424.077393), 'val_loss_top10%': np.float64(181639.584106), 'val_loss_cos1': np.float64(0.989997), 'val_loss_entropy': np.float64(2.292295)}} 2024-11-14 21:54:32,404 (server:353) INFO: Server: Starting evaluation at the end of round 65. 2024-11-14 21:54:32,405 (server:359) INFO: ----------- Starting a new training round (Round #66) ------------- 2024-11-14 21:56:55,821 (client:354) INFO: {'Role': 'Client #4', 'Round': 66, 'Results_raw': {'train_loss': 22.731308, 'val_loss': 21.553732, 'test_loss': 23.521851}} 2024-11-14 21:57:49,411 (client:354) INFO: {'Role': 'Client #6', 'Round': 66, 'Results_raw': {'train_loss': 22.795952, 'val_loss': 21.735357, 'test_loss': 23.858565}} 2024-11-14 21:58:42,641 (client:354) INFO: {'Role': 'Client #9', 'Round': 66, 'Results_raw': {'train_loss': 29.500934, 'val_loss': 27.67566, 'test_loss': 27.310389}} 2024-11-14 21:59:36,333 (client:354) INFO: {'Role': 'Client #5', 'Round': 66, 'Results_raw': {'train_loss': 23.508941, 'val_loss': 24.932517, 'test_loss': 34.141676}} 2024-11-14 22:00:29,284 (client:354) INFO: {'Role': 'Client #2', 'Round': 66, 'Results_raw': {'train_loss': 13.080792, 'val_loss': 12.591426, 'test_loss': 13.526658}} 2024-11-14 22:01:21,239 (client:354) INFO: {'Role': 'Client #10', 'Round': 66, 'Results_raw': {'train_loss': 22.012828, 'val_loss': 22.073353, 'test_loss': 23.829582}} 2024-11-14 22:02:13,539 (client:354) INFO: {'Role': 'Client #8', 'Round': 66, 'Results_raw': {'train_loss': 20.213533, 'val_loss': 19.879725, 'test_loss': 20.745377}} 2024-11-14 22:03:07,933 (client:354) INFO: {'Role': 'Client #1', 'Round': 66, 'Results_raw': {'train_loss': 16.942995, 'val_loss': 16.255574, 'test_loss': 18.526086}} 2024-11-14 22:04:01,717 (client:354) INFO: {'Role': 'Client #7', 'Round': 66, 'Results_raw': {'train_loss': 22.773518, 'val_loss': 22.540091, 'test_loss': 23.099352}} 2024-11-14 22:04:54,935 (client:354) INFO: {'Role': 'Client #3', 'Round': 66, 'Results_raw': {'train_loss': 16.254129, 'val_loss': 16.861607, 'test_loss': 20.198204}} 2024-11-14 22:04:54,945 (server:615) INFO: {'Role': 'Server #', 'Round': 65, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.107358), 'test_loss': np.float64(150892.545514), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.643643), 'val_loss': np.float64(148488.645605), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.107358), 'test_loss': np.float64(150892.545514), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.643643), 'val_loss': np.float64(148488.645605), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.050871), 'test_avg_loss_bottom_decile': np.float64(23.945121), 'test_avg_loss_top_decile': np.float64(41.604944), 'test_avg_loss_min': np.float64(21.709375), 'test_avg_loss_max': np.float64(41.604944), 'test_avg_loss_bottom10%': np.float64(21.709375), 'test_avg_loss_top10%': np.float64(41.604944), 'test_avg_loss_cos1': np.float64(0.985276), 'test_avg_loss_entropy': np.float64(2.288209), 'test_loss_std': np.float64(26183.714918), 'test_loss_bottom_decile': np.float64(124131.506165), 'test_loss_top_decile': np.float64(215680.02771), 'test_loss_min': np.float64(112541.397766), 'test_loss_max': np.float64(215680.02771), 'test_loss_bottom10%': np.float64(112541.397766), 'test_loss_top10%': np.float64(215680.02771), 'test_loss_cos1': np.float64(0.985276), 'test_loss_entropy': np.float64(2.288209), 'val_avg_loss_std': np.float64(4.090591), 'val_avg_loss_bottom_decile': np.float64(23.183536), 'val_avg_loss_top_decile': np.float64(35.18403), 'val_avg_loss_min': np.float64(21.683968), 'val_avg_loss_max': np.float64(35.18403), 'val_avg_loss_bottom10%': np.float64(21.683968), 'val_avg_loss_top10%': np.float64(35.18403), 'val_avg_loss_cos1': np.float64(0.989956), 'val_avg_loss_entropy': np.float64(2.292236), 'val_loss_std': np.float64(21205.621302), 'val_loss_bottom_decile': np.float64(120183.450195), 'val_loss_top_decile': np.float64(182394.009827), 'val_loss_min': np.float64(112409.692505), 'val_loss_max': np.float64(182394.009827), 'val_loss_bottom10%': np.float64(112409.692505), 'val_loss_top10%': np.float64(182394.009827), 'val_loss_cos1': np.float64(0.989956), 'val_loss_entropy': np.float64(2.292236)}} 2024-11-14 22:04:54,985 (server:353) INFO: Server: Starting evaluation at the end of round 66. 2024-11-14 22:04:54,986 (server:359) INFO: ----------- Starting a new training round (Round #67) ------------- 2024-11-14 22:07:37,537 (client:354) INFO: {'Role': 'Client #3', 'Round': 67, 'Results_raw': {'train_loss': 16.238235, 'val_loss': 16.948748, 'test_loss': 20.098347}} 2024-11-14 22:08:28,037 (client:354) INFO: {'Role': 'Client #9', 'Round': 67, 'Results_raw': {'train_loss': 29.523692, 'val_loss': 27.671054, 'test_loss': 27.0728}} 2024-11-14 22:09:17,853 (client:354) INFO: {'Role': 'Client #5', 'Round': 67, 'Results_raw': {'train_loss': 23.502793, 'val_loss': 25.107993, 'test_loss': 33.985098}} 2024-11-14 22:10:15,169 (client:354) INFO: {'Role': 'Client #7', 'Round': 67, 'Results_raw': {'train_loss': 22.777162, 'val_loss': 22.519435, 'test_loss': 23.391973}} 2024-11-14 22:11:06,938 (client:354) INFO: {'Role': 'Client #4', 'Round': 67, 'Results_raw': {'train_loss': 22.695368, 'val_loss': 21.36903, 'test_loss': 23.288827}} 2024-11-14 22:12:04,696 (client:354) INFO: {'Role': 'Client #1', 'Round': 67, 'Results_raw': {'train_loss': 16.955425, 'val_loss': 16.121074, 'test_loss': 18.46967}} 2024-11-14 22:12:54,276 (client:354) INFO: {'Role': 'Client #6', 'Round': 67, 'Results_raw': {'train_loss': 22.816023, 'val_loss': 21.768731, 'test_loss': 23.660392}} 2024-11-14 22:13:45,581 (client:354) INFO: {'Role': 'Client #10', 'Round': 67, 'Results_raw': {'train_loss': 21.950793, 'val_loss': 22.059535, 'test_loss': 24.043513}} 2024-11-14 22:14:37,785 (client:354) INFO: {'Role': 'Client #2', 'Round': 67, 'Results_raw': {'train_loss': 13.055571, 'val_loss': 12.447914, 'test_loss': 13.46356}} 2024-11-14 22:15:26,136 (client:354) INFO: {'Role': 'Client #8', 'Round': 67, 'Results_raw': {'train_loss': 20.185639, 'val_loss': 20.035193, 'test_loss': 21.303351}} 2024-11-14 22:15:26,139 (server:615) INFO: {'Role': 'Server #', 'Round': 66, 'Results_weighted_avg': {'test_avg_loss': np.float64(28.952001), 'test_loss': np.float64(150087.171112), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.505181), 'val_loss': np.float64(147770.857959), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(28.952001), 'test_loss': np.float64(150087.171112), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.505181), 'val_loss': np.float64(147770.857959), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.020863), 'test_avg_loss_bottom_decile': np.float64(23.851355), 'test_avg_loss_top_decile': np.float64(41.391984), 'test_avg_loss_min': np.float64(21.576), 'test_avg_loss_max': np.float64(41.391984), 'test_avg_loss_bottom10%': np.float64(21.576), 'test_avg_loss_top10%': np.float64(41.391984), 'test_avg_loss_cos1': np.float64(0.985294), 'test_avg_loss_entropy': np.float64(2.28823), 'test_loss_std': np.float64(26028.154862), 'test_loss_bottom_decile': np.float64(123645.42627), 'test_loss_top_decile': np.float64(214576.045471), 'test_loss_min': np.float64(111849.984253), 'test_loss_max': np.float64(214576.045471), 'test_loss_bottom10%': np.float64(111849.984253), 'test_loss_top10%': np.float64(214576.045471), 'test_loss_cos1': np.float64(0.985294), 'test_loss_entropy': np.float64(2.28823), 'val_avg_loss_std': np.float64(4.043828), 'val_avg_loss_bottom_decile': np.float64(23.109635), 'val_avg_loss_top_decile': np.float64(34.993043), 'val_avg_loss_min': np.float64(21.59814), 'val_avg_loss_max': np.float64(34.993043), 'val_avg_loss_bottom10%': np.float64(21.59814), 'val_avg_loss_top10%': np.float64(34.993043), 'val_avg_loss_cos1': np.float64(0.990087), 'val_avg_loss_entropy': np.float64(2.292379), 'val_loss_std': np.float64(20963.202468), 'val_loss_bottom_decile': np.float64(119800.350098), 'val_loss_top_decile': np.float64(181403.937012), 'val_loss_min': np.float64(111964.756714), 'val_loss_max': np.float64(181403.937012), 'val_loss_bottom10%': np.float64(111964.756714), 'val_loss_top10%': np.float64(181403.937012), 'val_loss_cos1': np.float64(0.990087), 'val_loss_entropy': np.float64(2.292379)}} 2024-11-14 22:15:26,175 (server:353) INFO: Server: Starting evaluation at the end of round 67. 2024-11-14 22:15:26,175 (server:359) INFO: ----------- Starting a new training round (Round #68) ------------- 2024-11-14 22:17:41,891 (client:354) INFO: {'Role': 'Client #9', 'Round': 68, 'Results_raw': {'train_loss': 29.48073, 'val_loss': 27.817508, 'test_loss': 27.227798}} 2024-11-14 22:18:34,546 (client:354) INFO: {'Role': 'Client #2', 'Round': 68, 'Results_raw': {'train_loss': 13.027337, 'val_loss': 12.635089, 'test_loss': 13.566422}} 2024-11-14 22:19:26,962 (client:354) INFO: {'Role': 'Client #5', 'Round': 68, 'Results_raw': {'train_loss': 23.464825, 'val_loss': 24.791099, 'test_loss': 33.792021}} 2024-11-14 22:20:14,501 (client:354) INFO: {'Role': 'Client #1', 'Round': 68, 'Results_raw': {'train_loss': 16.932759, 'val_loss': 16.033993, 'test_loss': 18.364162}} 2024-11-14 22:21:02,618 (client:354) INFO: {'Role': 'Client #10', 'Round': 68, 'Results_raw': {'train_loss': 21.941517, 'val_loss': 22.081256, 'test_loss': 24.001044}} 2024-11-14 22:21:51,219 (client:354) INFO: {'Role': 'Client #4', 'Round': 68, 'Results_raw': {'train_loss': 22.72842, 'val_loss': 21.504656, 'test_loss': 23.356978}} 2024-11-14 22:22:39,799 (client:354) INFO: {'Role': 'Client #3', 'Round': 68, 'Results_raw': {'train_loss': 16.243364, 'val_loss': 16.982761, 'test_loss': 20.181387}} 2024-11-14 22:23:28,155 (client:354) INFO: {'Role': 'Client #7', 'Round': 68, 'Results_raw': {'train_loss': 22.781672, 'val_loss': 22.563501, 'test_loss': 23.233546}} 2024-11-14 22:24:16,421 (client:354) INFO: {'Role': 'Client #8', 'Round': 68, 'Results_raw': {'train_loss': 20.170732, 'val_loss': 19.932405, 'test_loss': 20.945717}} 2024-11-14 22:25:04,258 (client:354) INFO: {'Role': 'Client #6', 'Round': 68, 'Results_raw': {'train_loss': 22.789919, 'val_loss': 21.817265, 'test_loss': 23.968966}} 2024-11-14 22:25:04,261 (server:615) INFO: {'Role': 'Server #', 'Round': 67, 'Results_weighted_avg': {'test_avg_loss': np.float64(28.862146), 'test_loss': np.float64(149621.363239), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.413402), 'val_loss': np.float64(147295.077911), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(28.862146), 'test_loss': np.float64(149621.363239), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.413402), 'val_loss': np.float64(147295.077911), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.983363), 'test_avg_loss_bottom_decile': np.float64(23.737117), 'test_avg_loss_top_decile': np.float64(41.198022), 'test_avg_loss_min': np.float64(21.598828), 'test_avg_loss_max': np.float64(41.198022), 'test_avg_loss_bottom10%': np.float64(21.598828), 'test_avg_loss_top10%': np.float64(41.198022), 'test_avg_loss_cos1': np.float64(0.985419), 'test_avg_loss_entropy': np.float64(2.288355), 'test_loss_std': np.float64(25833.751497), 'test_loss_bottom_decile': np.float64(123053.216858), 'test_loss_top_decile': np.float64(213570.546631), 'test_loss_min': np.float64(111968.326416), 'test_loss_max': np.float64(213570.546631), 'test_loss_bottom10%': np.float64(111968.326416), 'test_loss_top10%': np.float64(213570.546631), 'test_loss_cos1': np.float64(0.985419), 'test_loss_entropy': np.float64(2.288355), 'val_avg_loss_std': np.float64(4.015736), 'val_avg_loss_bottom_decile': np.float64(22.981196), 'val_avg_loss_top_decile': np.float64(34.800088), 'val_avg_loss_min': np.float64(21.637979), 'val_avg_loss_max': np.float64(34.800088), 'val_avg_loss_bottom10%': np.float64(21.637979), 'val_avg_loss_top10%': np.float64(34.800088), 'val_avg_loss_cos1': np.float64(0.99016), 'val_avg_loss_entropy': np.float64(2.292458), 'val_loss_std': np.float64(20817.574739), 'val_loss_bottom_decile': np.float64(119134.519165), 'val_loss_top_decile': np.float64(180403.654175), 'val_loss_min': np.float64(112171.2854), 'val_loss_max': np.float64(180403.654175), 'val_loss_bottom10%': np.float64(112171.2854), 'val_loss_top10%': np.float64(180403.654175), 'val_loss_cos1': np.float64(0.99016), 'val_loss_entropy': np.float64(2.292458)}} 2024-11-14 22:25:04,293 (server:353) INFO: Server: Starting evaluation at the end of round 68. 2024-11-14 22:25:04,294 (server:359) INFO: ----------- Starting a new training round (Round #69) ------------- 2024-11-14 22:27:27,213 (client:354) INFO: {'Role': 'Client #10', 'Round': 69, 'Results_raw': {'train_loss': 21.961225, 'val_loss': 21.971239, 'test_loss': 24.109989}} 2024-11-14 22:28:14,271 (client:354) INFO: {'Role': 'Client #8', 'Round': 69, 'Results_raw': {'train_loss': 20.178334, 'val_loss': 20.030332, 'test_loss': 21.082472}} 2024-11-14 22:29:00,962 (client:354) INFO: {'Role': 'Client #6', 'Round': 69, 'Results_raw': {'train_loss': 22.76547, 'val_loss': 21.76215, 'test_loss': 23.66514}} 2024-11-14 22:29:47,330 (client:354) INFO: {'Role': 'Client #5', 'Round': 69, 'Results_raw': {'train_loss': 23.509825, 'val_loss': 24.803956, 'test_loss': 33.705015}} 2024-11-14 22:30:33,822 (client:354) INFO: {'Role': 'Client #9', 'Round': 69, 'Results_raw': {'train_loss': 29.458036, 'val_loss': 27.813754, 'test_loss': 27.208765}} 2024-11-14 22:31:20,622 (client:354) INFO: {'Role': 'Client #7', 'Round': 69, 'Results_raw': {'train_loss': 22.765319, 'val_loss': 22.536235, 'test_loss': 23.247318}} 2024-11-14 22:32:07,147 (client:354) INFO: {'Role': 'Client #2', 'Round': 69, 'Results_raw': {'train_loss': 13.052241, 'val_loss': 12.565755, 'test_loss': 13.466569}} 2024-11-14 22:32:54,234 (client:354) INFO: {'Role': 'Client #4', 'Round': 69, 'Results_raw': {'train_loss': 22.673666, 'val_loss': 21.458603, 'test_loss': 23.369245}} 2024-11-14 22:33:48,106 (client:354) INFO: {'Role': 'Client #1', 'Round': 69, 'Results_raw': {'train_loss': 16.938486, 'val_loss': 16.123924, 'test_loss': 18.271405}} 2024-11-14 22:34:35,612 (client:354) INFO: {'Role': 'Client #3', 'Round': 69, 'Results_raw': {'train_loss': 16.208804, 'val_loss': 16.930585, 'test_loss': 20.300157}} 2024-11-14 22:34:35,615 (server:615) INFO: {'Role': 'Server #', 'Round': 68, 'Results_weighted_avg': {'test_avg_loss': np.float64(28.889946), 'test_loss': np.float64(149765.477484), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.4569), 'val_loss': np.float64(147520.567358), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(28.889946), 'test_loss': np.float64(149765.477484), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.4569), 'val_loss': np.float64(147520.567358), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(4.991389), 'test_avg_loss_bottom_decile': np.float64(23.708398), 'test_avg_loss_top_decile': np.float64(41.245936), 'test_avg_loss_min': np.float64(21.665692), 'test_avg_loss_max': np.float64(41.245936), 'test_avg_loss_bottom10%': np.float64(21.665692), 'test_avg_loss_top10%': np.float64(41.245936), 'test_avg_loss_cos1': np.float64(0.985401), 'test_avg_loss_entropy': np.float64(2.288341), 'test_loss_std': np.float64(25875.360919), 'test_loss_bottom_decile': np.float64(122904.333618), 'test_loss_top_decile': np.float64(213818.934326), 'test_loss_min': np.float64(112314.949341), 'test_loss_max': np.float64(213818.934326), 'test_loss_bottom10%': np.float64(112314.949341), 'test_loss_top10%': np.float64(213818.934326), 'test_loss_cos1': np.float64(0.985401), 'test_loss_entropy': np.float64(2.288341), 'val_avg_loss_std': np.float64(4.044718), 'val_avg_loss_bottom_decile': np.float64(22.939586), 'val_avg_loss_top_decile': np.float64(34.917856), 'val_avg_loss_min': np.float64(21.674891), 'val_avg_loss_max': np.float64(34.917856), 'val_avg_loss_bottom10%': np.float64(21.674891), 'val_avg_loss_top10%': np.float64(34.917856), 'val_avg_loss_cos1': np.float64(0.990049), 'val_avg_loss_entropy': np.float64(2.292348), 'val_loss_std': np.float64(20967.820304), 'val_loss_bottom_decile': np.float64(118918.813538), 'val_loss_top_decile': np.float64(181014.16687), 'val_loss_min': np.float64(112362.635376), 'val_loss_max': np.float64(181014.16687), 'val_loss_bottom10%': np.float64(112362.635376), 'val_loss_top10%': np.float64(181014.16687), 'val_loss_cos1': np.float64(0.990049), 'val_loss_entropy': np.float64(2.292348)}} 2024-11-14 22:34:35,647 (server:370) INFO: Server: Training is finished! Starting evaluation. 2024-11-14 22:36:02,441 (server:615) INFO: {'Role': 'Server #', 'Round': 69, 'Results_weighted_avg': {'test_avg_loss': np.float64(28.853475), 'test_loss': np.float64(149576.416656), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.403533), 'val_loss': np.float64(147243.914642), 'val_total': np.float64(5184.0)}, 'Results_avg': {'test_avg_loss': np.float64(28.853475), 'test_loss': np.float64(149576.416656), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.403533), 'val_loss': np.float64(147243.914642), 'val_total': np.float64(5184.0)}, 'Results_fairness': {'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(5.04174), 'test_avg_loss_bottom_decile': np.float64(23.700661), 'test_avg_loss_top_decile': np.float64(41.267734), 'test_avg_loss_min': np.float64(21.465702), 'test_avg_loss_max': np.float64(41.267734), 'test_avg_loss_bottom10%': np.float64(21.465702), 'test_avg_loss_top10%': np.float64(41.267734), 'test_avg_loss_cos1': np.float64(0.985075), 'test_avg_loss_entropy': np.float64(2.287996), 'test_loss_std': np.float64(26136.378812), 'test_loss_bottom_decile': np.float64(122864.227661), 'test_loss_top_decile': np.float64(213931.930786), 'test_loss_min': np.float64(111278.20166), 'test_loss_max': np.float64(213931.930786), 'test_loss_bottom10%': np.float64(111278.20166), 'test_loss_top10%': np.float64(213931.930786), 'test_loss_cos1': np.float64(0.985075), 'test_loss_entropy': np.float64(2.287996), 'val_avg_loss_std': np.float64(4.10837), 'val_avg_loss_bottom_decile': np.float64(22.937223), 'val_avg_loss_top_decile': np.float64(34.884771), 'val_avg_loss_min': np.float64(21.456825), 'val_avg_loss_max': np.float64(34.884771), 'val_avg_loss_bottom10%': np.float64(21.456825), 'val_avg_loss_top10%': np.float64(34.884771), 'val_avg_loss_cos1': np.float64(0.989701), 'val_avg_loss_entropy': np.float64(2.291971), 'val_loss_std': np.float64(21297.79043), 'val_loss_bottom_decile': np.float64(118906.566101), 'val_loss_top_decile': np.float64(180842.652649), 'val_loss_min': np.float64(111232.181213), 'val_loss_max': np.float64(180842.652649), 'val_loss_bottom10%': np.float64(111232.181213), 'val_loss_top10%': np.float64(180842.652649), 'val_loss_cos1': np.float64(0.989701), 'val_loss_entropy': np.float64(2.291971)}} 2024-11-14 22:36:02,444 (server:420) INFO: Server: Final evaluation is finished! Starting merging results. 2024-11-14 22:36:02,444 (server:546) INFO: {'Role': 'Server #', 'Round': 'Final', 'Results_raw': {'client_best_individual': {'val_loss': 111232.181213, 'test_avg_loss': 21.465702, 'test_loss': 111278.20166, 'test_total': 5184.0, 'val_avg_loss': 21.456825, 'val_total': 5184.0}, 'client_summarized_weighted_avg': {'val_loss': np.float64(147243.914642), 'test_avg_loss': np.float64(28.853475), 'test_loss': np.float64(149576.416656), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.403533), 'val_total': np.float64(5184.0)}, 'client_summarized_avg': {'val_loss': np.float64(147243.914642), 'test_avg_loss': np.float64(28.853475), 'test_loss': np.float64(149576.416656), 'test_total': np.float64(5184.0), 'val_avg_loss': np.float64(28.403533), 'val_total': np.float64(5184.0)}, 'client_summarized_fairness': {'val_loss_entropy': np.float64(2.290285), 'val_loss_cos1': np.float64(0.988565), 'val_loss_top10%': np.float64(281832.150513), 'val_loss_bottom10%': np.float64(158174.575928), 'val_loss_max': np.float64(281832.150513), 'val_loss_min': np.float64(158174.575928), 'val_loss_top_decile': np.float64(281832.150513), 'val_loss_bottom_decile': np.float64(202973.908569), 'val_loss_std': np.float64(36146.281782), 'test_total': np.float64(5184.0), 'val_total': np.float64(5184.0), 'test_avg_loss_std': np.float64(6.89224), 'test_avg_loss_bottom_decile': np.float64(38.8195), 'test_avg_loss_top_decile': np.float64(56.793405), 'test_avg_loss_min': np.float64(30.10601), 'test_avg_loss_max': np.float64(56.793405), 'test_avg_loss_bottom10%': np.float64(30.10601), 'test_avg_loss_top10%': np.float64(56.793405), 'test_avg_loss_cos1': np.float64(0.988428), 'test_avg_loss_entropy': np.float64(2.290334), 'test_loss_std': np.float64(35729.370987), 'test_loss_bottom_decile': np.float64(201240.28833), 'test_loss_top_decile': np.float64(294417.012695), 'test_loss_min': np.float64(156069.553711), 'test_loss_max': np.float64(294417.012695), 'test_loss_bottom10%': np.float64(156069.553711), 'test_loss_top10%': np.float64(294417.012695), 'test_loss_cos1': np.float64(0.988428), 'test_loss_entropy': np.float64(2.290334), 'val_avg_loss_std': np.float64(6.972662), 'val_avg_loss_bottom_decile': np.float64(39.153918), 'val_avg_loss_top_decile': np.float64(54.36577), 'val_avg_loss_min': np.float64(30.512071), 'val_avg_loss_max': np.float64(54.36577), 'val_avg_loss_bottom10%': np.float64(30.512071), 'val_avg_loss_top10%': np.float64(54.36577), 'val_avg_loss_cos1': np.float64(0.988565), 'val_avg_loss_entropy': np.float64(2.290285)}}} 2024-11-14 22:36:02,446 (server:565) INFO: {'Role': 'Client #1', 'Round': 70, 'Results_raw': {'test_avg_loss': 23.700661, 'test_loss': 122864.227661, 'test_total': 5184, 'val_avg_loss': 22.937223, 'val_loss': 118906.566101, 'val_total': 5184}} 2024-11-14 22:36:02,447 (server:565) INFO: {'Role': 'Client #2', 'Round': 70, 'Results_raw': {'test_avg_loss': 21.465702, 'test_loss': 111278.20166, 'test_total': 5184, 'val_avg_loss': 21.456825, 'val_loss': 111232.181213, 'val_total': 5184}} 2024-11-14 22:36:02,447 (server:565) INFO: {'Role': 'Client #3', 'Round': 70, 'Results_raw': {'test_avg_loss': 26.659772, 'test_loss': 138204.259888, 'test_total': 5184, 'val_avg_loss': 25.112968, 'val_loss': 130185.626953, 'val_total': 5184}} 2024-11-14 22:36:02,447 (server:565) INFO: {'Role': 'Client #4', 'Round': 70, 'Results_raw': {'test_avg_loss': 27.788015, 'test_loss': 144053.068176, 'test_total': 5184, 'val_avg_loss': 27.952635, 'val_loss': 144906.459473, 'val_total': 5184}} 2024-11-14 22:36:02,448 (server:565) INFO: {'Role': 'Client #5', 'Round': 70, 'Results_raw': {'test_avg_loss': 41.267734, 'test_loss': 213931.930786, 'test_total': 5184, 'val_avg_loss': 34.884771, 'val_loss': 180842.652649, 'val_total': 5184}} 2024-11-14 22:36:02,448 (server:565) INFO: {'Role': 'Client #6', 'Round': 70, 'Results_raw': {'test_avg_loss': 29.233554, 'test_loss': 151546.742859, 'test_total': 5184, 'val_avg_loss': 29.80463, 'val_loss': 154507.199402, 'val_total': 5184}} 2024-11-14 22:36:02,448 (server:565) INFO: {'Role': 'Client #7', 'Round': 70, 'Results_raw': {'test_avg_loss': 29.858281, 'test_loss': 154785.330627, 'test_total': 5184, 'val_avg_loss': 31.017042, 'val_loss': 160792.347961, 'val_total': 5184}} 2024-11-14 22:36:02,449 (server:565) INFO: {'Role': 'Client #8', 'Round': 70, 'Results_raw': {'test_avg_loss': 27.154904, 'test_loss': 140771.024841, 'test_total': 5184, 'val_avg_loss': 27.611642, 'val_loss': 143138.751648, 'val_total': 5184}} 2024-11-14 22:36:02,449 (server:565) INFO: {'Role': 'Client #9', 'Round': 70, 'Results_raw': {'test_avg_loss': 31.732183, 'test_loss': 164499.634583, 'test_total': 5184, 'val_avg_loss': 33.550637, 'val_loss': 173926.500854, 'val_total': 5184}} 2024-11-14 22:36:02,449 (server:565) INFO: {'Role': 'Client #10', 'Round': 70, 'Results_raw': {'test_avg_loss': 29.673948, 'test_loss': 153829.745483, 'test_total': 5184, 'val_avg_loss': 29.706956, 'val_loss': 154000.860168, 'val_total': 5184}} 2024-11-14 22:36:02,455 (monitor:173) INFO: In worker #0, the system-related metrics are: {'id': 0, 'fl_end_time_minutes': 770.218917, '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} 2024-11-14 22:36:02,456 (client:582) INFO: ================= client 1 received finish message ================= 2024-11-14 22:36:02,459 (monitor:173) INFO: In worker #1, the system-related metrics are: {'id': 1, 'fl_end_time_minutes': 770.218716, 'total_model_size': 563814, 'total_flops': 42290006640000.0, '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} 2024-11-14 22:36:02,459 (client:582) INFO: ================= client 2 received finish message ================= 2024-11-14 22:36:02,462 (monitor:173) INFO: In worker #2, the system-related metrics are: {'id': 2, 'fl_end_time_minutes': 770.21831, 'total_model_size': 563814, 'total_flops': 42290006640000.0, '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} 2024-11-14 22:36:02,463 (client:582) INFO: ================= client 3 received finish message ================= 2024-11-14 22:36:02,465 (monitor:173) INFO: In worker #3, the system-related metrics are: {'id': 3, 'fl_end_time_minutes': 770.217954, 'total_model_size': 563814, 'total_flops': 42290006640000.0, '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} 2024-11-14 22:36:02,465 (client:582) INFO: ================= client 4 received finish message ================= 2024-11-14 22:36:02,468 (monitor:173) INFO: In worker #4, the system-related metrics are: {'id': 4, 'fl_end_time_minutes': 770.217686, 'total_model_size': 563814, 'total_flops': 42290006640000.0, '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} 2024-11-14 22:36:02,468 (client:582) INFO: ================= client 5 received finish message ================= 2024-11-14 22:36:02,472 (monitor:173) INFO: In worker #5, the system-related metrics are: {'id': 5, 'fl_end_time_minutes': 770.217447, 'total_model_size': 563814, 'total_flops': 42290006640000.0, '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} 2024-11-14 22:36:02,472 (client:582) INFO: ================= client 6 received finish message ================= 2024-11-14 22:36:02,477 (monitor:173) INFO: In worker #6, the system-related metrics are: {'id': 6, 'fl_end_time_minutes': 770.2172, 'total_model_size': 563814, 'total_flops': 42290006640000.0, '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} 2024-11-14 22:36:02,477 (client:582) INFO: ================= client 7 received finish message ================= 2024-11-14 22:36:02,481 (monitor:173) INFO: In worker #7, the system-related metrics are: {'id': 7, 'fl_end_time_minutes': 770.216918, 'total_model_size': 563814, 'total_flops': 42290006640000.0, '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} 2024-11-14 22:36:02,481 (client:582) INFO: ================= client 8 received finish message ================= 2024-11-14 22:36:02,486 (monitor:173) INFO: In worker #8, the system-related metrics are: {'id': 8, 'fl_end_time_minutes': 770.216694, 'total_model_size': 563814, 'total_flops': 42290006640000.0, '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} 2024-11-14 22:36:02,486 (client:582) INFO: ================= client 9 received finish message ================= 2024-11-14 22:36:02,490 (monitor:173) INFO: In worker #9, the system-related metrics are: {'id': 9, 'fl_end_time_minutes': 770.21645, 'total_model_size': 563814, 'total_flops': 42290006640000.0, '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} 2024-11-14 22:36:02,491 (client:582) INFO: ================= client 10 received finish message ================= 2024-11-14 22:36:02,494 (monitor:173) INFO: In worker #10, the system-related metrics are: {'id': 10, 'fl_end_time_minutes': 770.216229, 'total_model_size': 563814, 'total_flops': 42290006640000.0, '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} 2024-11-14 22:36:02,495 (monitor:338) INFO: We will compress the file eval_results.raw into a .gz file, and delete the old one 2024-11-14 22:36:02,528 (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(770.217502), 'sys_avg/total_model_size': '500.55K', 'sys_avg/total_flops': '34.97T', '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)}) 2024-11-14 22:36:02,529 (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(0.000862), 'sys_std/total_model_size': '158.29K', 'sys_std/total_flops': '11.06T', '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)})