2024-11-14 11:58:46,662 (logging:124) INFO: the current machine is at 127.0.1.1 2024-11-14 11:58:46,663 (logging:126) INFO: the current dir is /home/czzhangheng/code/FederatedScope 2024-11-14 11:58:46,663 (logging:127) INFO: the output dir is exp/FedAvg_DDGCRN_on_trafficflow_lr0.01_lstep1/sub_exp_20241114115846 2024-11-14 11:59:04,230 (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: 170 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/PeMS08 save_data: False scaler: [229.843136, 145.625531] 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: 0 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_DDGCRN_on_trafficflow_lr0.01_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: 17 num_of_trees: 10 num_user: 0 out_channels: 1 output_dim: 1 pretrain_tasks: [] rnn_units: 64 stage: task: TrafficFlowPrediction type: DDGCRN use_bias: True use_contrastive_loss: False use_day: True use_week: True nbafl: use: False outdir: exp/FedAvg_DDGCRN_on_trafficflow_lr0.01_lstep1/sub_exp_20241114115846 personalization: K: 5 beta: 1.0 epoch_feature: 1 epoch_linear: 2 local_param: [] local_update_steps: 1 lr: 0.01 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: 0.01 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 11:59:04,406 (utils:147) INFO: The device information file is not provided 2024-11-14 11:59:04,465 (fed_runner:173) INFO: Server has been set up ... 2024-11-14 11:59:04,489 (fed_runner:225) INFO: Client 1 has been set up ... 2024-11-14 11:59:04,511 (fed_runner:225) INFO: Client 2 has been set up ... 2024-11-14 11:59:04,527 (fed_runner:225) INFO: Client 3 has been set up ... 2024-11-14 11:59:04,544 (fed_runner:225) INFO: Client 4 has been set up ... 2024-11-14 11:59:04,562 (fed_runner:225) INFO: Client 5 has been set up ... 2024-11-14 11:59:04,582 (fed_runner:225) INFO: Client 6 has been set up ... 2024-11-14 11:59:04,601 (fed_runner:225) INFO: Client 7 has been set up ... 2024-11-14 11:59:04,617 (fed_runner:225) INFO: Client 8 has been set up ... 2024-11-14 11:59:04,637 (fed_runner:225) INFO: Client 9 has been set up ... 2024-11-14 11:59:04,655 (fed_runner:225) INFO: Client 10 has been set up ... 2024-11-14 11:59:04,655 (trainer:345) INFO: Model meta-info: . 2024-11-14 11:59:04,656 (trainer:353) INFO: Num of original para names: 50. 2024-11-14 11:59:04,656 (trainer:354) INFO: Num of original trainable para names: 50. 2024-11-14 11:59:04,656 (trainer:356) INFO: Num of preserved para names in local update: 50. Preserved para names in local update: {'encoder1.DGCRM_cells.0.update.bias_pool', 'encoder2.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder2.DGCRM_cells.0.update.bias_pool', 'node_embeddings1', 'end_conv2.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc2.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc3.weight', 'encoder1.DGCRM_cells.0.update.fc.fc1.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc3.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc2.weight', 'end_conv1.bias', 'encoder1.DGCRM_cells.0.gate.fc.fc3.bias', 'encoder1.DGCRM_cells.0.gate.bias', 'encoder2.DGCRM_cells.0.update.weights', 'encoder2.DGCRM_cells.0.gate.bias', 'encoder2.DGCRM_cells.0.update.fc.fc3.bias', 'encoder2.DGCRM_cells.0.update.fc.fc2.bias', 'T_i_D_emb', 'encoder1.DGCRM_cells.0.update.bias', 'end_conv3.weight', 'encoder2.DGCRM_cells.0.update.fc.fc3.weight', 'end_conv2.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc1.weight', 'encoder1.DGCRM_cells.0.update.weights_pool', 'encoder2.DGCRM_cells.0.gate.fc.fc3.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc2.bias', 'encoder2.DGCRM_cells.0.update.bias', 'node_embeddings2', 'D_i_W_emb', 'end_conv3.bias', 'encoder2.DGCRM_cells.0.gate.weights', 'encoder2.DGCRM_cells.0.gate.weights_pool', 'encoder2.DGCRM_cells.0.gate.bias_pool', 'encoder1.DGCRM_cells.0.gate.weights', 'encoder2.DGCRM_cells.0.update.fc.fc1.weight', 'end_conv1.weight', 'encoder1.DGCRM_cells.0.update.fc.fc1.bias', 'encoder1.DGCRM_cells.0.update.fc.fc2.weight', 'encoder1.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder1.DGCRM_cells.0.gate.bias_pool', 'encoder1.DGCRM_cells.0.update.fc.fc3.weight', 'encoder2.DGCRM_cells.0.gate.fc.fc1.bias', 'encoder1.DGCRM_cells.0.gate.weights_pool', 'encoder2.DGCRM_cells.0.update.fc.fc1.bias', 'encoder2.DGCRM_cells.0.update.fc.fc2.weight', 'encoder1.DGCRM_cells.0.update.fc.fc3.bias', 'encoder1.DGCRM_cells.0.update.weights', 'encoder2.DGCRM_cells.0.update.weights_pool', 'encoder1.DGCRM_cells.0.update.fc.fc2.bias', 'encoder2.DGCRM_cells.0.gate.fc.fc2.weight'}. 2024-11-14 11:59:04,656 (trainer:360) INFO: Num of filtered para names in local update: 0. Filtered para names in local update: set(). 2024-11-14 11:59:04,657 (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 11:59:04,670 (server:843) INFO: ----------- Starting training (Round #0) ------------- 2024-11-14 11:59:40,258 (client:354) INFO: {'Role': 'Client #9', 'Round': 0, 'Results_raw': {'train_loss': 37.754987, 'val_loss': 31.277188, 'test_loss': 27.259351}} 2024-11-14 12:00:15,149 (client:354) INFO: {'Role': 'Client #3', 'Round': 0, 'Results_raw': {'train_loss': 38.20077, 'val_loss': 28.959635, 'test_loss': 29.028561}} 2024-11-14 12:00:53,727 (client:354) INFO: {'Role': 'Client #6', 'Round': 0, 'Results_raw': {'train_loss': 27.761092, 'val_loss': 21.274523, 'test_loss': 21.062306}} 2024-11-14 12:01:29,282 (client:354) INFO: {'Role': 'Client #7', 'Round': 0, 'Results_raw': {'train_loss': 28.602545, 'val_loss': 21.313748, 'test_loss': 20.003168}} 2024-11-14 12:02:04,829 (client:354) INFO: {'Role': 'Client #4', 'Round': 0, 'Results_raw': {'train_loss': 36.765281, 'val_loss': 24.431613, 'test_loss': 24.572145}} 2024-11-14 12:02:41,057 (client:354) INFO: {'Role': 'Client #2', 'Round': 0, 'Results_raw': {'train_loss': 37.033324, 'val_loss': 28.455185, 'test_loss': 27.427502}} 2024-11-14 12:03:16,870 (client:354) INFO: {'Role': 'Client #1', 'Round': 0, 'Results_raw': {'train_loss': 47.10884, 'val_loss': 33.164957, 'test_loss': 32.882762}} 2024-11-14 12:03:52,897 (client:354) INFO: {'Role': 'Client #8', 'Round': 0, 'Results_raw': {'train_loss': 37.606052, 'val_loss': 27.821956, 'test_loss': 24.550049}} 2024-11-14 12:04:33,385 (client:354) INFO: {'Role': 'Client #5', 'Round': 0, 'Results_raw': {'train_loss': 32.851018, 'val_loss': 23.237654, 'test_loss': 23.298016}} 2024-11-14 12:05:19,981 (client:354) INFO: {'Role': 'Client #10', 'Round': 0, 'Results_raw': {'train_loss': 34.482289, 'val_loss': 25.449568, 'test_loss': 24.514494}} 2024-11-14 12:05:20,027 (server:353) INFO: Server: Starting evaluation at the end of round 0. 2024-11-14 12:05:20,028 (server:359) INFO: ----------- Starting a new training round (Round #1) ------------- 2024-11-14 12:07:30,300 (client:354) INFO: {'Role': 'Client #6', 'Round': 1, 'Results_raw': {'train_loss': 23.008816, 'val_loss': 20.956763, 'test_loss': 20.496651}} 2024-11-14 12:08:15,104 (client:354) INFO: {'Role': 'Client #4', 'Round': 1, 'Results_raw': {'train_loss': 30.42118, 'val_loss': 25.370114, 'test_loss': 25.410394}} 2024-11-14 12:09:00,631 (client:354) INFO: {'Role': 'Client #5', 'Round': 1, 'Results_raw': {'train_loss': 26.300336, 'val_loss': 22.677527, 'test_loss': 23.086853}} 2024-11-14 12:09:46,099 (client:354) INFO: {'Role': 'Client #8', 'Round': 1, 'Results_raw': {'train_loss': 30.307526, 'val_loss': 26.662689, 'test_loss': 23.555709}} 2024-11-14 12:10:31,140 (client:354) INFO: {'Role': 'Client #7', 'Round': 1, 'Results_raw': {'train_loss': 23.178433, 'val_loss': 21.657309, 'test_loss': 20.30633}} 2024-11-14 12:11:14,152 (client:354) INFO: {'Role': 'Client #9', 'Round': 1, 'Results_raw': {'train_loss': 30.77934, 'val_loss': 30.755806, 'test_loss': 26.943449}} 2024-11-14 12:11:55,987 (client:354) INFO: {'Role': 'Client #10', 'Round': 1, 'Results_raw': {'train_loss': 28.252238, 'val_loss': 23.939038, 'test_loss': 23.58182}} 2024-11-14 12:12:39,479 (client:354) INFO: {'Role': 'Client #3', 'Round': 1, 'Results_raw': {'train_loss': 32.714533, 'val_loss': 28.529471, 'test_loss': 28.499942}} 2024-11-14 12:13:21,742 (client:354) INFO: {'Role': 'Client #2', 'Round': 1, 'Results_raw': {'train_loss': 29.294133, 'val_loss': 27.144464, 'test_loss': 26.392934}} 2024-11-14 12:14:12,179 (client:354) INFO: {'Role': 'Client #1', 'Round': 1, 'Results_raw': {'train_loss': 38.971623, 'val_loss': 32.917443, 'test_loss': 32.818176}} 2024-11-14 12:14:12,185 (server:615) INFO: {'Role': 'Server #', 'Round': 0, 'Results_weighted_avg': {'test_avg_loss': np.float64(55.792733), 'test_loss': np.float64(196390.421204), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(57.008905), 'val_loss': np.float64(200671.346606), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(55.792733), 'test_loss': np.float64(196390.421204), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(57.008905), 'val_loss': np.float64(200671.346606), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(8.197565), 'test_avg_loss_bottom_decile': np.float64(43.900183), 'test_avg_loss_top_decile': np.float64(68.756876), 'test_avg_loss_min': np.float64(40.996824), 'test_avg_loss_max': np.float64(68.756876), 'test_avg_loss_bottom10%': np.float64(40.996824), 'test_avg_loss_top10%': np.float64(68.756876), 'test_avg_loss_cos1': np.float64(0.989378), 'test_avg_loss_entropy': np.float64(2.29148), 'test_loss_std': np.float64(28855.428296), 'test_loss_bottom_decile': np.float64(154528.644287), 'test_loss_top_decile': np.float64(242024.202637), 'test_loss_min': np.float64(144308.821655), 'test_loss_max': np.float64(242024.202637), 'test_loss_bottom10%': np.float64(144308.821655), 'test_loss_top10%': np.float64(242024.202637), 'test_loss_cos1': np.float64(0.989378), 'test_loss_entropy': np.float64(2.29148), 'val_avg_loss_std': np.float64(8.455585), 'val_avg_loss_bottom_decile': np.float64(44.60222), 'val_avg_loss_top_decile': np.float64(70.227201), 'val_avg_loss_min': np.float64(41.777075), 'val_avg_loss_max': np.float64(70.227201), 'val_avg_loss_bottom10%': np.float64(41.777075), 'val_avg_loss_top10%': np.float64(70.227201), 'val_avg_loss_cos1': np.float64(0.989179), 'val_avg_loss_entropy': np.float64(2.291259), 'val_loss_std': np.float64(29763.657552), 'val_loss_bottom_decile': np.float64(156999.813477), 'val_loss_top_decile': np.float64(247199.748535), 'val_loss_min': np.float64(147055.30542), 'val_loss_max': np.float64(247199.748535), 'val_loss_bottom10%': np.float64(147055.30542), 'val_loss_top10%': np.float64(247199.748535), 'val_loss_cos1': np.float64(0.989179), 'val_loss_entropy': np.float64(2.291259)}} 2024-11-14 12:14:12,235 (server:353) INFO: Server: Starting evaluation at the end of round 1. 2024-11-14 12:14:12,235 (server:359) INFO: ----------- Starting a new training round (Round #2) ------------- 2024-11-14 12:16:28,454 (client:354) INFO: {'Role': 'Client #2', 'Round': 2, 'Results_raw': {'train_loss': 27.992742, 'val_loss': 26.414716, 'test_loss': 25.727694}} 2024-11-14 12:17:16,496 (client:354) INFO: {'Role': 'Client #7', 'Round': 2, 'Results_raw': {'train_loss': 22.764272, 'val_loss': 21.233536, 'test_loss': 19.824816}} 2024-11-14 12:18:00,767 (client:354) INFO: {'Role': 'Client #3', 'Round': 2, 'Results_raw': {'train_loss': 31.384578, 'val_loss': 28.605666, 'test_loss': 28.712598}} 2024-11-14 12:18:53,089 (client:354) INFO: {'Role': 'Client #6', 'Round': 2, 'Results_raw': {'train_loss': 21.758786, 'val_loss': 21.013881, 'test_loss': 20.537339}} 2024-11-14 12:19:45,257 (client:354) INFO: {'Role': 'Client #8', 'Round': 2, 'Results_raw': {'train_loss': 28.166505, 'val_loss': 27.244818, 'test_loss': 23.917385}} 2024-11-14 12:20:37,957 (client:354) INFO: {'Role': 'Client #9', 'Round': 2, 'Results_raw': {'train_loss': 29.710395, 'val_loss': 30.794981, 'test_loss': 26.91014}} 2024-11-14 12:21:23,432 (client:354) INFO: {'Role': 'Client #10', 'Round': 2, 'Results_raw': {'train_loss': 27.008938, 'val_loss': 23.64333, 'test_loss': 23.63831}} 2024-11-14 12:22:06,993 (client:354) INFO: {'Role': 'Client #1', 'Round': 2, 'Results_raw': {'train_loss': 37.639326, 'val_loss': 32.891631, 'test_loss': 32.800899}} 2024-11-14 12:22:51,551 (client:354) INFO: {'Role': 'Client #4', 'Round': 2, 'Results_raw': {'train_loss': 28.846128, 'val_loss': 23.571068, 'test_loss': 23.595174}} 2024-11-14 12:23:34,639 (client:354) INFO: {'Role': 'Client #5', 'Round': 2, 'Results_raw': {'train_loss': 25.095614, 'val_loss': 22.407904, 'test_loss': 22.614379}} 2024-11-14 12:23:34,643 (server:615) INFO: {'Role': 'Server #', 'Round': 1, 'Results_weighted_avg': {'test_avg_loss': np.float64(36.814272), 'test_loss': np.float64(129586.238873), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(38.468391), 'val_loss': np.float64(135408.734961), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(36.814272), 'test_loss': np.float64(129586.238873), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(38.468391), 'val_loss': np.float64(135408.734961), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(5.824384), 'test_avg_loss_bottom_decile': np.float64(28.079359), 'test_avg_loss_top_decile': np.float64(48.245355), 'test_avg_loss_min': np.float64(27.516944), 'test_avg_loss_max': np.float64(48.245355), 'test_avg_loss_bottom10%': np.float64(27.516944), 'test_avg_loss_top10%': np.float64(48.245355), 'test_avg_loss_cos1': np.float64(0.987715), 'test_avg_loss_entropy': np.float64(2.289943), 'test_loss_std': np.float64(20501.830224), 'test_loss_bottom_decile': np.float64(98839.344238), 'test_loss_top_decile': np.float64(169823.648682), 'test_loss_min': np.float64(96859.641174), 'test_loss_max': np.float64(169823.648682), 'test_loss_bottom10%': np.float64(96859.641174), 'test_loss_top10%': np.float64(169823.648682), 'test_loss_cos1': np.float64(0.987715), 'test_loss_entropy': np.float64(2.289943), 'val_avg_loss_std': np.float64(6.020271), 'val_avg_loss_bottom_decile': np.float64(29.310515), 'val_avg_loss_top_decile': np.float64(49.80633), 'val_avg_loss_min': np.float64(28.875106), 'val_avg_loss_max': np.float64(49.80633), 'val_avg_loss_bottom10%': np.float64(28.875106), 'val_avg_loss_top10%': np.float64(49.80633), 'val_avg_loss_cos1': np.float64(0.987974), 'val_avg_loss_entropy': np.float64(2.290176), 'val_loss_std': np.float64(21191.35416), 'val_loss_bottom_decile': np.float64(103173.011597), 'val_loss_top_decile': np.float64(175318.280151), 'val_loss_min': np.float64(101640.37207), 'val_loss_max': np.float64(175318.280151), 'val_loss_bottom10%': np.float64(101640.37207), 'val_loss_top10%': np.float64(175318.280151), 'val_loss_cos1': np.float64(0.987974), 'val_loss_entropy': np.float64(2.290176)}} 2024-11-14 12:23:34,680 (server:353) INFO: Server: Starting evaluation at the end of round 2. 2024-11-14 12:23:34,680 (server:359) INFO: ----------- Starting a new training round (Round #3) ------------- 2024-11-14 12:25:49,751 (client:354) INFO: {'Role': 'Client #1', 'Round': 3, 'Results_raw': {'train_loss': 35.920596, 'val_loss': 31.62552, 'test_loss': 31.522727}} 2024-11-14 12:26:33,752 (client:354) INFO: {'Role': 'Client #8', 'Round': 3, 'Results_raw': {'train_loss': 27.362219, 'val_loss': 26.778338, 'test_loss': 22.98922}} 2024-11-14 12:27:17,439 (client:354) INFO: {'Role': 'Client #6', 'Round': 3, 'Results_raw': {'train_loss': 21.101348, 'val_loss': 20.670752, 'test_loss': 20.278511}} 2024-11-14 12:28:01,322 (client:354) INFO: {'Role': 'Client #3', 'Round': 3, 'Results_raw': {'train_loss': 30.252349, 'val_loss': 27.511719, 'test_loss': 27.601006}} 2024-11-14 12:28:45,922 (client:354) INFO: {'Role': 'Client #4', 'Round': 3, 'Results_raw': {'train_loss': 28.287415, 'val_loss': 23.975174, 'test_loss': 24.161463}} 2024-11-14 12:29:29,664 (client:354) INFO: {'Role': 'Client #10', 'Round': 3, 'Results_raw': {'train_loss': 26.142945, 'val_loss': 23.444383, 'test_loss': 23.57403}} 2024-11-14 12:30:13,242 (client:354) INFO: {'Role': 'Client #7', 'Round': 3, 'Results_raw': {'train_loss': 21.952835, 'val_loss': 20.844809, 'test_loss': 19.344029}} 2024-11-14 12:30:57,938 (client:354) INFO: {'Role': 'Client #5', 'Round': 3, 'Results_raw': {'train_loss': 24.221887, 'val_loss': 21.864915, 'test_loss': 23.351057}} 2024-11-14 12:31:43,709 (client:354) INFO: {'Role': 'Client #9', 'Round': 3, 'Results_raw': {'train_loss': 29.029379, 'val_loss': 30.116529, 'test_loss': 26.161036}} 2024-11-14 12:32:30,483 (client:354) INFO: {'Role': 'Client #2', 'Round': 3, 'Results_raw': {'train_loss': 27.318059, 'val_loss': 26.220114, 'test_loss': 25.376754}} 2024-11-14 12:32:30,486 (server:615) INFO: {'Role': 'Server #', 'Round': 2, 'Results_weighted_avg': {'test_avg_loss': np.float64(33.881854), 'test_loss': np.float64(119264.126733), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(35.573771), 'val_loss': np.float64(125219.6745), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(33.881854), 'test_loss': np.float64(119264.126733), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(35.573771), 'val_loss': np.float64(125219.6745), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.968005), 'test_avg_loss_bottom_decile': np.float64(28.162665), 'test_avg_loss_top_decile': np.float64(44.468807), 'test_avg_loss_min': np.float64(26.160831), 'test_avg_loss_max': np.float64(44.468807), 'test_avg_loss_bottom10%': np.float64(26.160831), 'test_avg_loss_top10%': np.float64(44.468807), 'test_avg_loss_cos1': np.float64(0.989421), 'test_avg_loss_entropy': np.float64(2.291943), 'test_loss_std': np.float64(17487.376669), 'test_loss_bottom_decile': np.float64(99132.581482), 'test_loss_top_decile': np.float64(156530.202148), 'test_loss_min': np.float64(92086.125183), 'test_loss_max': np.float64(156530.202148), 'test_loss_bottom10%': np.float64(92086.125183), 'test_loss_top10%': np.float64(156530.202148), 'test_loss_cos1': np.float64(0.989421), 'test_loss_entropy': np.float64(2.291943), 'val_avg_loss_std': np.float64(5.072947), 'val_avg_loss_bottom_decile': np.float64(29.690184), 'val_avg_loss_top_decile': np.float64(45.89722), 'val_avg_loss_min': np.float64(27.721801), 'val_avg_loss_max': np.float64(45.89722), 'val_avg_loss_bottom10%': np.float64(27.721801), 'val_avg_loss_top10%': np.float64(45.89722), 'val_avg_loss_cos1': np.float64(0.989985), 'val_avg_loss_entropy': np.float64(2.292484), 'val_loss_std': np.float64(17856.772658), 'val_loss_bottom_decile': np.float64(104509.446045), 'val_loss_top_decile': np.float64(161558.21582), 'val_loss_min': np.float64(97580.738037), 'val_loss_max': np.float64(161558.21582), 'val_loss_bottom10%': np.float64(97580.738037), 'val_loss_top10%': np.float64(161558.21582), 'val_loss_cos1': np.float64(0.989985), 'val_loss_entropy': np.float64(2.292484)}} 2024-11-14 12:32:30,533 (server:353) INFO: Server: Starting evaluation at the end of round 3. 2024-11-14 12:32:30,534 (server:359) INFO: ----------- Starting a new training round (Round #4) ------------- 2024-11-14 12:35:05,208 (client:354) INFO: {'Role': 'Client #3', 'Round': 4, 'Results_raw': {'train_loss': 29.717607, 'val_loss': 27.145555, 'test_loss': 27.300884}} 2024-11-14 12:35:54,657 (client:354) INFO: {'Role': 'Client #1', 'Round': 4, 'Results_raw': {'train_loss': 35.342194, 'val_loss': 31.392954, 'test_loss': 31.141257}} 2024-11-14 12:36:41,650 (client:354) INFO: {'Role': 'Client #10', 'Round': 4, 'Results_raw': {'train_loss': 25.59979, 'val_loss': 23.337572, 'test_loss': 23.304249}} 2024-11-14 12:37:26,243 (client:354) INFO: {'Role': 'Client #6', 'Round': 4, 'Results_raw': {'train_loss': 20.565821, 'val_loss': 20.024792, 'test_loss': 19.702125}} 2024-11-14 12:38:10,200 (client:354) INFO: {'Role': 'Client #5', 'Round': 4, 'Results_raw': {'train_loss': 23.453052, 'val_loss': 21.609013, 'test_loss': 22.377411}} 2024-11-14 12:38:56,730 (client:354) INFO: {'Role': 'Client #2', 'Round': 4, 'Results_raw': {'train_loss': 26.676039, 'val_loss': 27.01668, 'test_loss': 26.009338}} 2024-11-14 12:39:41,773 (client:354) INFO: {'Role': 'Client #8', 'Round': 4, 'Results_raw': {'train_loss': 26.835458, 'val_loss': 26.212647, 'test_loss': 22.933851}} 2024-11-14 12:40:25,128 (client:354) INFO: {'Role': 'Client #9', 'Round': 4, 'Results_raw': {'train_loss': 28.472025, 'val_loss': 29.801351, 'test_loss': 25.828042}} 2024-11-14 12:41:09,027 (client:354) INFO: {'Role': 'Client #7', 'Round': 4, 'Results_raw': {'train_loss': 21.637559, 'val_loss': 20.514521, 'test_loss': 19.220469}} 2024-11-14 12:41:55,838 (client:354) INFO: {'Role': 'Client #4', 'Round': 4, 'Results_raw': {'train_loss': 27.581951, 'val_loss': 23.199512, 'test_loss': 23.379927}} 2024-11-14 12:41:55,842 (server:615) INFO: {'Role': 'Server #', 'Round': 3, 'Results_weighted_avg': {'test_avg_loss': np.float64(32.959046), 'test_loss': np.float64(116015.843573), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(34.673675), 'val_loss': np.float64(122051.335547), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(32.959046), 'test_loss': np.float64(116015.843573), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(34.673675), 'val_loss': np.float64(122051.335547), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.526977), 'test_avg_loss_bottom_decile': np.float64(28.408184), 'test_avg_loss_top_decile': np.float64(42.980015), 'test_avg_loss_min': np.float64(26.078174), 'test_avg_loss_max': np.float64(42.980015), 'test_avg_loss_bottom10%': np.float64(26.078174), 'test_avg_loss_top10%': np.float64(42.980015), 'test_avg_loss_cos1': np.float64(0.990699), 'test_avg_loss_entropy': np.float64(2.293315), 'test_loss_std': np.float64(15934.960559), 'test_loss_bottom_decile': np.float64(99996.808594), 'test_loss_top_decile': np.float64(151289.652832), 'test_loss_min': np.float64(91795.172363), 'test_loss_max': np.float64(151289.652832), 'test_loss_bottom10%': np.float64(91795.172363), 'test_loss_top10%': np.float64(151289.652832), 'test_loss_cos1': np.float64(0.990699), 'test_loss_entropy': np.float64(2.293315), 'val_avg_loss_std': np.float64(4.629165), 'val_avg_loss_bottom_decile': np.float64(30.02102), 'val_avg_loss_top_decile': np.float64(44.370188), 'val_avg_loss_min': np.float64(27.654602), 'val_avg_loss_max': np.float64(44.370188), 'val_avg_loss_bottom10%': np.float64(27.654602), 'val_avg_loss_top10%': np.float64(44.370188), 'val_avg_loss_cos1': np.float64(0.991205), 'val_avg_loss_entropy': np.float64(2.293788), 'val_loss_std': np.float64(16294.662055), 'val_loss_bottom_decile': np.float64(105673.99115), 'val_loss_top_decile': np.float64(156183.062622), 'val_loss_min': np.float64(97344.199646), 'val_loss_max': np.float64(156183.062622), 'val_loss_bottom10%': np.float64(97344.199646), 'val_loss_top10%': np.float64(156183.062622), 'val_loss_cos1': np.float64(0.991205), 'val_loss_entropy': np.float64(2.293788)}} 2024-11-14 12:41:55,889 (server:353) INFO: Server: Starting evaluation at the end of round 4. 2024-11-14 12:41:55,889 (server:359) INFO: ----------- Starting a new training round (Round #5) ------------- 2024-11-14 12:44:13,914 (client:354) INFO: {'Role': 'Client #2', 'Round': 5, 'Results_raw': {'train_loss': 26.260025, 'val_loss': 25.947358, 'test_loss': 25.080846}} 2024-11-14 12:45:01,304 (client:354) INFO: {'Role': 'Client #5', 'Round': 5, 'Results_raw': {'train_loss': 22.900463, 'val_loss': 21.464681, 'test_loss': 22.339245}} 2024-11-14 12:45:48,856 (client:354) INFO: {'Role': 'Client #10', 'Round': 5, 'Results_raw': {'train_loss': 25.240836, 'val_loss': 23.562682, 'test_loss': 23.31644}} 2024-11-14 12:46:40,555 (client:354) INFO: {'Role': 'Client #6', 'Round': 5, 'Results_raw': {'train_loss': 20.128033, 'val_loss': 20.273112, 'test_loss': 19.824403}} 2024-11-14 12:47:33,561 (client:354) INFO: {'Role': 'Client #4', 'Round': 5, 'Results_raw': {'train_loss': 27.256156, 'val_loss': 22.900135, 'test_loss': 23.115885}} 2024-11-14 12:48:21,872 (client:354) INFO: {'Role': 'Client #1', 'Round': 5, 'Results_raw': {'train_loss': 34.959771, 'val_loss': 31.191612, 'test_loss': 31.133445}} 2024-11-14 12:49:07,393 (client:354) INFO: {'Role': 'Client #9', 'Round': 5, 'Results_raw': {'train_loss': 28.208971, 'val_loss': 29.782414, 'test_loss': 25.811816}} 2024-11-14 12:49:52,157 (client:354) INFO: {'Role': 'Client #8', 'Round': 5, 'Results_raw': {'train_loss': 26.439903, 'val_loss': 25.451843, 'test_loss': 22.524234}} 2024-11-14 12:50:36,752 (client:354) INFO: {'Role': 'Client #7', 'Round': 5, 'Results_raw': {'train_loss': 21.491676, 'val_loss': 20.62548, 'test_loss': 19.284751}} 2024-11-14 12:51:20,740 (client:354) INFO: {'Role': 'Client #3', 'Round': 5, 'Results_raw': {'train_loss': 29.296946, 'val_loss': 27.220986, 'test_loss': 27.466089}} 2024-11-14 12:51:20,743 (server:615) INFO: {'Role': 'Server #', 'Round': 4, 'Results_weighted_avg': {'test_avg_loss': np.float64(32.239247), 'test_loss': np.float64(113482.15036), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.976819), 'val_loss': np.float64(119598.402771), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(32.239247), 'test_loss': np.float64(113482.15036), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.976819), 'val_loss': np.float64(119598.402771), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.259897), 'test_avg_loss_bottom_decile': np.float64(27.794011), 'test_avg_loss_top_decile': np.float64(41.932066), 'test_avg_loss_min': np.float64(26.013769), 'test_avg_loss_max': np.float64(41.932066), 'test_avg_loss_bottom10%': np.float64(26.013769), 'test_avg_loss_top10%': np.float64(41.932066), 'test_avg_loss_cos1': np.float64(0.991383), 'test_avg_loss_entropy': np.float64(2.294051), 'test_loss_std': np.float64(14994.839184), 'test_loss_bottom_decile': np.float64(97834.917358), 'test_loss_top_decile': np.float64(147600.873779), 'test_loss_min': np.float64(91568.46759), 'test_loss_max': np.float64(147600.873779), 'test_loss_bottom10%': np.float64(91568.46759), 'test_loss_top10%': np.float64(147600.873779), 'test_loss_cos1': np.float64(0.991383), 'test_loss_entropy': np.float64(2.294051), 'val_avg_loss_std': np.float64(4.338811), 'val_avg_loss_bottom_decile': np.float64(29.625645), 'val_avg_loss_top_decile': np.float64(43.241414), 'val_avg_loss_min': np.float64(27.593469), 'val_avg_loss_max': np.float64(43.241414), 'val_avg_loss_bottom10%': np.float64(27.593469), 'val_avg_loss_top10%': np.float64(43.241414), 'val_avg_loss_cos1': np.float64(0.991945), 'val_avg_loss_entropy': np.float64(2.294568), 'val_loss_std': np.float64(15272.61517), 'val_loss_bottom_decile': np.float64(104282.269836), 'val_loss_top_decile': np.float64(152209.776611), 'val_loss_min': np.float64(97129.011353), 'val_loss_max': np.float64(152209.776611), 'val_loss_bottom10%': np.float64(97129.011353), 'val_loss_top10%': np.float64(152209.776611), 'val_loss_cos1': np.float64(0.991945), 'val_loss_entropy': np.float64(2.294568)}} 2024-11-14 12:51:20,789 (server:353) INFO: Server: Starting evaluation at the end of round 5. 2024-11-14 12:51:20,790 (server:359) INFO: ----------- Starting a new training round (Round #6) ------------- 2024-11-14 12:53:35,539 (client:354) INFO: {'Role': 'Client #6', 'Round': 6, 'Results_raw': {'train_loss': 19.910432, 'val_loss': 19.974151, 'test_loss': 19.987546}} 2024-11-14 12:54:19,832 (client:354) INFO: {'Role': 'Client #5', 'Round': 6, 'Results_raw': {'train_loss': 22.688112, 'val_loss': 21.167496, 'test_loss': 22.234538}} 2024-11-14 12:55:03,104 (client:354) INFO: {'Role': 'Client #9', 'Round': 6, 'Results_raw': {'train_loss': 27.909882, 'val_loss': 29.719649, 'test_loss': 25.653687}} 2024-11-14 12:55:46,335 (client:354) INFO: {'Role': 'Client #2', 'Round': 6, 'Results_raw': {'train_loss': 25.979194, 'val_loss': 26.318805, 'test_loss': 25.354385}} 2024-11-14 12:56:29,369 (client:354) INFO: {'Role': 'Client #4', 'Round': 6, 'Results_raw': {'train_loss': 27.008444, 'val_loss': 23.02394, 'test_loss': 22.886376}} 2024-11-14 12:57:13,768 (client:354) INFO: {'Role': 'Client #8', 'Round': 6, 'Results_raw': {'train_loss': 26.078369, 'val_loss': 25.042473, 'test_loss': 21.73271}} 2024-11-14 12:57:56,893 (client:354) INFO: {'Role': 'Client #7', 'Round': 6, 'Results_raw': {'train_loss': 21.297976, 'val_loss': 20.335339, 'test_loss': 18.983776}} 2024-11-14 12:58:41,297 (client:354) INFO: {'Role': 'Client #1', 'Round': 6, 'Results_raw': {'train_loss': 34.598117, 'val_loss': 31.720662, 'test_loss': 31.542041}} 2024-11-14 12:59:25,738 (client:354) INFO: {'Role': 'Client #3', 'Round': 6, 'Results_raw': {'train_loss': 29.042702, 'val_loss': 26.779528, 'test_loss': 26.83231}} 2024-11-14 13:00:10,102 (client:354) INFO: {'Role': 'Client #10', 'Round': 6, 'Results_raw': {'train_loss': 24.910427, 'val_loss': 22.675124, 'test_loss': 22.691497}} 2024-11-14 13:00:10,105 (server:615) INFO: {'Role': 'Server #', 'Round': 5, 'Results_weighted_avg': {'test_avg_loss': np.float64(32.321569), 'test_loss': np.float64(113771.923322), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(34.048562), 'val_loss': np.float64(119850.938757), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(32.321569), 'test_loss': np.float64(113771.923322), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(34.048562), 'val_loss': np.float64(119850.938757), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.392883), 'test_avg_loss_bottom_decile': np.float64(27.92263), 'test_avg_loss_top_decile': np.float64(42.406978), 'test_avg_loss_min': np.float64(25.972854), 'test_avg_loss_max': np.float64(42.406978), 'test_avg_loss_bottom10%': np.float64(25.972854), 'test_avg_loss_top10%': np.float64(42.406978), 'test_avg_loss_cos1': np.float64(0.99089), 'test_avg_loss_entropy': np.float64(2.293578), 'test_loss_std': np.float64(15462.948791), 'test_loss_bottom_decile': np.float64(98287.659241), 'test_loss_top_decile': np.float64(149272.560913), 'test_loss_min': np.float64(91424.447083), 'test_loss_max': np.float64(149272.560913), 'test_loss_bottom10%': np.float64(91424.447083), 'test_loss_top10%': np.float64(149272.560913), 'test_loss_cos1': np.float64(0.99089), 'test_loss_entropy': np.float64(2.293578), 'val_avg_loss_std': np.float64(4.481855), 'val_avg_loss_bottom_decile': np.float64(29.715606), 'val_avg_loss_top_decile': np.float64(43.751033), 'val_avg_loss_min': np.float64(27.533167), 'val_avg_loss_max': np.float64(43.751033), 'val_avg_loss_bottom10%': np.float64(27.533167), 'val_avg_loss_top10%': np.float64(43.751033), 'val_avg_loss_cos1': np.float64(0.991448), 'val_avg_loss_entropy': np.float64(2.294086), 'val_loss_std': np.float64(15776.131021), 'val_loss_bottom_decile': np.float64(104598.934692), 'val_loss_top_decile': np.float64(154003.634766), 'val_loss_min': np.float64(96916.748413), 'val_loss_max': np.float64(154003.634766), 'val_loss_bottom10%': np.float64(96916.748413), 'val_loss_top10%': np.float64(154003.634766), 'val_loss_cos1': np.float64(0.991448), 'val_loss_entropy': np.float64(2.294086)}} 2024-11-14 13:00:10,139 (server:353) INFO: Server: Starting evaluation at the end of round 6. 2024-11-14 13:00:10,140 (server:359) INFO: ----------- Starting a new training round (Round #7) ------------- 2024-11-14 13:02:25,256 (client:354) INFO: {'Role': 'Client #8', 'Round': 7, 'Results_raw': {'train_loss': 25.717339, 'val_loss': 25.132875, 'test_loss': 22.263104}} 2024-11-14 13:03:09,607 (client:354) INFO: {'Role': 'Client #4', 'Round': 7, 'Results_raw': {'train_loss': 26.759175, 'val_loss': 22.826867, 'test_loss': 23.248556}} 2024-11-14 13:03:52,806 (client:354) INFO: {'Role': 'Client #6', 'Round': 7, 'Results_raw': {'train_loss': 19.713536, 'val_loss': 20.025901, 'test_loss': 20.090601}} 2024-11-14 13:04:36,674 (client:354) INFO: {'Role': 'Client #10', 'Round': 7, 'Results_raw': {'train_loss': 24.58894, 'val_loss': 22.692808, 'test_loss': 22.712608}} 2024-11-14 13:05:20,771 (client:354) INFO: {'Role': 'Client #9', 'Round': 7, 'Results_raw': {'train_loss': 27.705625, 'val_loss': 29.853354, 'test_loss': 25.770121}} 2024-11-14 13:06:05,624 (client:354) INFO: {'Role': 'Client #7', 'Round': 7, 'Results_raw': {'train_loss': 21.170572, 'val_loss': 20.537002, 'test_loss': 19.297059}} 2024-11-14 13:06:53,677 (client:354) INFO: {'Role': 'Client #1', 'Round': 7, 'Results_raw': {'train_loss': 34.482996, 'val_loss': 30.997812, 'test_loss': 30.838957}} 2024-11-14 13:07:38,926 (client:354) INFO: {'Role': 'Client #3', 'Round': 7, 'Results_raw': {'train_loss': 28.806146, 'val_loss': 26.891987, 'test_loss': 26.844444}} 2024-11-14 13:08:25,016 (client:354) INFO: {'Role': 'Client #5', 'Round': 7, 'Results_raw': {'train_loss': 22.43127, 'val_loss': 20.83713, 'test_loss': 21.904507}} 2024-11-14 13:09:13,735 (client:354) INFO: {'Role': 'Client #2', 'Round': 7, 'Results_raw': {'train_loss': 25.898953, 'val_loss': 25.635027, 'test_loss': 24.904697}} 2024-11-14 13:09:13,740 (server:615) INFO: {'Role': 'Server #', 'Round': 6, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.975808), 'test_loss': np.float64(112554.842407), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.693292), 'val_loss': np.float64(118600.38653), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.975808), 'test_loss': np.float64(112554.842407), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.693292), 'val_loss': np.float64(118600.38653), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.277448), 'test_avg_loss_bottom_decile': np.float64(27.68125), 'test_avg_loss_top_decile': np.float64(41.914178), 'test_avg_loss_min': np.float64(25.998589), 'test_avg_loss_max': np.float64(41.914178), 'test_avg_loss_bottom10%': np.float64(25.998589), 'test_avg_loss_top10%': np.float64(41.914178), 'test_avg_loss_cos1': np.float64(0.991171), 'test_avg_loss_entropy': np.float64(2.293882), 'test_loss_std': np.float64(15056.615936), 'test_loss_bottom_decile': np.float64(97438.001648), 'test_loss_top_decile': np.float64(147537.907227), 'test_loss_min': np.float64(91515.03186), 'test_loss_max': np.float64(147537.907227), 'test_loss_bottom10%': np.float64(91515.03186), 'test_loss_top10%': np.float64(147537.907227), 'test_loss_cos1': np.float64(0.991171), 'test_loss_entropy': np.float64(2.293882), 'val_avg_loss_std': np.float64(4.355932), 'val_avg_loss_bottom_decile': np.float64(29.287311), 'val_avg_loss_top_decile': np.float64(43.22131), 'val_avg_loss_min': np.float64(27.564226), 'val_avg_loss_max': np.float64(43.22131), 'val_avg_loss_bottom10%': np.float64(27.564226), 'val_avg_loss_top10%': np.float64(43.22131), 'val_avg_loss_cos1': np.float64(0.991746), 'val_avg_loss_entropy': np.float64(2.294401), 'val_loss_std': np.float64(15332.881994), 'val_loss_bottom_decile': np.float64(103091.336243), 'val_loss_top_decile': np.float64(152139.010864), 'val_loss_min': np.float64(97026.076416), 'val_loss_max': np.float64(152139.010864), 'val_loss_bottom10%': np.float64(97026.076416), 'val_loss_top10%': np.float64(152139.010864), 'val_loss_cos1': np.float64(0.991746), 'val_loss_entropy': np.float64(2.294401)}} 2024-11-14 13:09:13,790 (server:353) INFO: Server: Starting evaluation at the end of round 7. 2024-11-14 13:09:13,791 (server:359) INFO: ----------- Starting a new training round (Round #8) ------------- 2024-11-14 13:11:28,937 (client:354) INFO: {'Role': 'Client #6', 'Round': 8, 'Results_raw': {'train_loss': 19.577883, 'val_loss': 19.734451, 'test_loss': 19.806565}} 2024-11-14 13:12:15,086 (client:354) INFO: {'Role': 'Client #7', 'Round': 8, 'Results_raw': {'train_loss': 20.987395, 'val_loss': 20.001685, 'test_loss': 18.644757}} 2024-11-14 13:13:00,713 (client:354) INFO: {'Role': 'Client #2', 'Round': 8, 'Results_raw': {'train_loss': 25.710456, 'val_loss': 26.027041, 'test_loss': 25.218526}} 2024-11-14 13:13:46,679 (client:354) INFO: {'Role': 'Client #4', 'Round': 8, 'Results_raw': {'train_loss': 26.700209, 'val_loss': 23.023352, 'test_loss': 23.306554}} 2024-11-14 13:14:33,551 (client:354) INFO: {'Role': 'Client #1', 'Round': 8, 'Results_raw': {'train_loss': 34.237575, 'val_loss': 31.062976, 'test_loss': 30.940215}} 2024-11-14 13:15:19,744 (client:354) INFO: {'Role': 'Client #8', 'Round': 8, 'Results_raw': {'train_loss': 25.502903, 'val_loss': 25.397121, 'test_loss': 22.567029}} 2024-11-14 13:16:08,081 (client:354) INFO: {'Role': 'Client #5', 'Round': 8, 'Results_raw': {'train_loss': 22.366983, 'val_loss': 20.810674, 'test_loss': 21.563564}} 2024-11-14 13:16:54,254 (client:354) INFO: {'Role': 'Client #3', 'Round': 8, 'Results_raw': {'train_loss': 28.554601, 'val_loss': 26.764607, 'test_loss': 26.849371}} 2024-11-14 13:17:37,174 (client:354) INFO: {'Role': 'Client #10', 'Round': 8, 'Results_raw': {'train_loss': 24.519136, 'val_loss': 22.592259, 'test_loss': 22.861611}} 2024-11-14 13:18:21,310 (client:354) INFO: {'Role': 'Client #9', 'Round': 8, 'Results_raw': {'train_loss': 27.422473, 'val_loss': 29.472225, 'test_loss': 25.623155}} 2024-11-14 13:18:21,313 (server:615) INFO: {'Role': 'Server #', 'Round': 7, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.766602), 'test_loss': np.float64(111818.439801), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.440617), 'val_loss': np.float64(117710.971796), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.766602), 'test_loss': np.float64(111818.439801), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.440617), 'val_loss': np.float64(117710.971796), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.32419), 'test_avg_loss_bottom_decile': np.float64(27.442177), 'test_avg_loss_top_decile': np.float64(41.75416), 'test_avg_loss_min': np.float64(25.822141), 'test_avg_loss_max': np.float64(41.75416), 'test_avg_loss_bottom10%': np.float64(25.822141), 'test_avg_loss_top10%': np.float64(41.75416), 'test_avg_loss_cos1': np.float64(0.990862), 'test_avg_loss_entropy': np.float64(2.293573), 'test_loss_std': np.float64(15221.150287), 'test_loss_bottom_decile': np.float64(96596.462585), 'test_loss_top_decile': np.float64(146974.642944), 'test_loss_min': np.float64(90893.935669), 'test_loss_max': np.float64(146974.642944), 'test_loss_bottom10%': np.float64(90893.935669), 'test_loss_top10%': np.float64(146974.642944), 'test_loss_cos1': np.float64(0.990862), 'test_loss_entropy': np.float64(2.293573), 'val_avg_loss_std': np.float64(4.430663), 'val_avg_loss_bottom_decile': np.float64(28.767624), 'val_avg_loss_top_decile': np.float64(43.029253), 'val_avg_loss_min': np.float64(27.372771), 'val_avg_loss_max': np.float64(43.029253), 'val_avg_loss_bottom10%': np.float64(27.372771), 'val_avg_loss_top10%': np.float64(43.029253), 'val_avg_loss_cos1': np.float64(0.991337), 'val_avg_loss_entropy': np.float64(2.293994), 'val_loss_std': np.float64(15595.934538), 'val_loss_bottom_decile': np.float64(101262.037537), 'val_loss_top_decile': np.float64(151462.969849), 'val_loss_min': np.float64(96352.154663), 'val_loss_max': np.float64(151462.969849), 'val_loss_bottom10%': np.float64(96352.154663), 'val_loss_top10%': np.float64(151462.969849), 'val_loss_cos1': np.float64(0.991337), 'val_loss_entropy': np.float64(2.293994)}} 2024-11-14 13:18:21,343 (server:353) INFO: Server: Starting evaluation at the end of round 8. 2024-11-14 13:18:21,343 (server:359) INFO: ----------- Starting a new training round (Round #9) ------------- 2024-11-14 13:20:33,608 (client:354) INFO: {'Role': 'Client #2', 'Round': 9, 'Results_raw': {'train_loss': 25.459839, 'val_loss': 25.681149, 'test_loss': 24.883517}} 2024-11-14 13:21:19,721 (client:354) INFO: {'Role': 'Client #9', 'Round': 9, 'Results_raw': {'train_loss': 27.279617, 'val_loss': 29.665357, 'test_loss': 25.431611}} 2024-11-14 13:22:06,061 (client:354) INFO: {'Role': 'Client #8', 'Round': 9, 'Results_raw': {'train_loss': 25.330145, 'val_loss': 24.864153, 'test_loss': 21.778208}} 2024-11-14 13:22:50,394 (client:354) INFO: {'Role': 'Client #5', 'Round': 9, 'Results_raw': {'train_loss': 22.173935, 'val_loss': 21.091619, 'test_loss': 21.869976}} 2024-11-14 13:23:34,327 (client:354) INFO: {'Role': 'Client #1', 'Round': 9, 'Results_raw': {'train_loss': 33.956008, 'val_loss': 31.316868, 'test_loss': 31.453925}} 2024-11-14 13:24:18,058 (client:354) INFO: {'Role': 'Client #4', 'Round': 9, 'Results_raw': {'train_loss': 26.519118, 'val_loss': 22.281019, 'test_loss': 22.375419}} 2024-11-14 13:25:04,875 (client:354) INFO: {'Role': 'Client #3', 'Round': 9, 'Results_raw': {'train_loss': 28.530482, 'val_loss': 26.707813, 'test_loss': 26.68811}} 2024-11-14 13:25:48,687 (client:354) INFO: {'Role': 'Client #6', 'Round': 9, 'Results_raw': {'train_loss': 19.532437, 'val_loss': 19.737236, 'test_loss': 19.890852}} 2024-11-14 13:26:33,069 (client:354) INFO: {'Role': 'Client #10', 'Round': 9, 'Results_raw': {'train_loss': 24.383337, 'val_loss': 22.417813, 'test_loss': 22.629719}} 2024-11-14 13:27:17,285 (client:354) INFO: {'Role': 'Client #7', 'Round': 9, 'Results_raw': {'train_loss': 20.889741, 'val_loss': 20.267166, 'test_loss': 19.029611}} 2024-11-14 13:27:17,289 (server:615) INFO: {'Role': 'Server #', 'Round': 8, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.926633), 'test_loss': np.float64(112381.749036), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.602438), 'val_loss': np.float64(118280.5823), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.926633), 'test_loss': np.float64(112381.749036), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.602438), 'val_loss': np.float64(118280.5823), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.437902), 'test_avg_loss_bottom_decile': np.float64(27.607775), 'test_avg_loss_top_decile': np.float64(42.112807), 'test_avg_loss_min': np.float64(25.493903), 'test_avg_loss_max': np.float64(42.112807), 'test_avg_loss_bottom10%': np.float64(25.493903), 'test_avg_loss_top10%': np.float64(42.112807), 'test_avg_loss_cos1': np.float64(0.990477), 'test_avg_loss_entropy': np.float64(2.29317), 'test_loss_std': np.float64(15621.416747), 'test_loss_bottom_decile': np.float64(97179.368042), 'test_loss_top_decile': np.float64(148237.081909), 'test_loss_min': np.float64(89738.537109), 'test_loss_max': np.float64(148237.081909), 'test_loss_bottom10%': np.float64(89738.537109), 'test_loss_top10%': np.float64(148237.081909), 'test_loss_cos1': np.float64(0.990477), 'test_loss_entropy': np.float64(2.29317), 'val_avg_loss_std': np.float64(4.525106), 'val_avg_loss_bottom_decile': np.float64(28.961724), 'val_avg_loss_top_decile': np.float64(43.376493), 'val_avg_loss_min': np.float64(27.079206), 'val_avg_loss_max': np.float64(43.376493), 'val_avg_loss_bottom10%': np.float64(27.079206), 'val_avg_loss_top10%': np.float64(43.376493), 'val_avg_loss_cos1': np.float64(0.991054), 'val_avg_loss_entropy': np.float64(2.293696), 'val_loss_std': np.float64(15928.372732), 'val_loss_bottom_decile': np.float64(101945.267639), 'val_loss_top_decile': np.float64(152685.255981), 'val_loss_min': np.float64(95318.804993), 'val_loss_max': np.float64(152685.255981), 'val_loss_bottom10%': np.float64(95318.804993), 'val_loss_top10%': np.float64(152685.255981), 'val_loss_cos1': np.float64(0.991054), 'val_loss_entropy': np.float64(2.293696)}} 2024-11-14 13:27:17,327 (server:353) INFO: Server: Starting evaluation at the end of round 9. 2024-11-14 13:27:17,328 (server:359) INFO: ----------- Starting a new training round (Round #10) ------------- 2024-11-14 13:29:28,611 (client:354) INFO: {'Role': 'Client #6', 'Round': 10, 'Results_raw': {'train_loss': 19.343562, 'val_loss': 20.237863, 'test_loss': 20.769029}} 2024-11-14 13:30:14,324 (client:354) INFO: {'Role': 'Client #10', 'Round': 10, 'Results_raw': {'train_loss': 24.258264, 'val_loss': 22.575968, 'test_loss': 22.433323}} 2024-11-14 13:31:00,879 (client:354) INFO: {'Role': 'Client #4', 'Round': 10, 'Results_raw': {'train_loss': 26.407185, 'val_loss': 22.52227, 'test_loss': 22.547348}} 2024-11-14 13:31:47,567 (client:354) INFO: {'Role': 'Client #9', 'Round': 10, 'Results_raw': {'train_loss': 27.220941, 'val_loss': 29.411973, 'test_loss': 25.110224}} 2024-11-14 13:32:33,529 (client:354) INFO: {'Role': 'Client #3', 'Round': 10, 'Results_raw': {'train_loss': 28.275446, 'val_loss': 26.668239, 'test_loss': 26.835408}} 2024-11-14 13:33:19,669 (client:354) INFO: {'Role': 'Client #1', 'Round': 10, 'Results_raw': {'train_loss': 33.840145, 'val_loss': 30.950844, 'test_loss': 30.76438}} 2024-11-14 13:34:04,748 (client:354) INFO: {'Role': 'Client #5', 'Round': 10, 'Results_raw': {'train_loss': 21.999835, 'val_loss': 20.610055, 'test_loss': 21.624917}} 2024-11-14 13:34:51,734 (client:354) INFO: {'Role': 'Client #8', 'Round': 10, 'Results_raw': {'train_loss': 25.298571, 'val_loss': 25.126824, 'test_loss': 22.06679}} 2024-11-14 13:35:37,746 (client:354) INFO: {'Role': 'Client #2', 'Round': 10, 'Results_raw': {'train_loss': 25.287819, 'val_loss': 25.883712, 'test_loss': 25.010911}} 2024-11-14 13:36:24,408 (client:354) INFO: {'Role': 'Client #7', 'Round': 10, 'Results_raw': {'train_loss': 20.783565, 'val_loss': 19.895761, 'test_loss': 18.698319}} 2024-11-14 13:36:24,411 (server:615) INFO: {'Role': 'Server #', 'Round': 9, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.342183), 'test_loss': np.float64(110324.483313), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.014168), 'val_loss': np.float64(116209.870007), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.342183), 'test_loss': np.float64(110324.483313), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.014168), 'val_loss': np.float64(116209.870007), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.338692), 'test_avg_loss_bottom_decile': np.float64(27.127488), 'test_avg_loss_top_decile': np.float64(41.524295), 'test_avg_loss_min': np.float64(25.291009), 'test_avg_loss_max': np.float64(41.524295), 'test_avg_loss_bottom10%': np.float64(25.291009), 'test_avg_loss_top10%': np.float64(41.524295), 'test_avg_loss_cos1': np.float64(0.990554), 'test_avg_loss_entropy': np.float64(2.293296), 'test_loss_std': np.float64(15272.195825), 'test_loss_bottom_decile': np.float64(95488.757263), 'test_loss_top_decile': np.float64(146165.51709), 'test_loss_min': np.float64(89024.350098), 'test_loss_max': np.float64(146165.51709), 'test_loss_bottom10%': np.float64(89024.350098), 'test_loss_top10%': np.float64(146165.51709), 'test_loss_cos1': np.float64(0.990554), 'test_loss_entropy': np.float64(2.293296), 'val_avg_loss_std': np.float64(4.408931), 'val_avg_loss_bottom_decile': np.float64(28.483091), 'val_avg_loss_top_decile': np.float64(42.737327), 'val_avg_loss_min': np.float64(26.89096), 'val_avg_loss_max': np.float64(42.737327), 'val_avg_loss_bottom10%': np.float64(26.89096), 'val_avg_loss_top10%': np.float64(42.737327), 'val_avg_loss_cos1': np.float64(0.9912), 'val_avg_loss_entropy': np.float64(2.293879), 'val_loss_std': np.float64(15519.43726), 'val_loss_bottom_decile': np.float64(100260.481262), 'val_loss_top_decile': np.float64(150435.391968), 'val_loss_min': np.float64(94656.180847), 'val_loss_max': np.float64(150435.391968), 'val_loss_bottom10%': np.float64(94656.180847), 'val_loss_top10%': np.float64(150435.391968), 'val_loss_cos1': np.float64(0.9912), 'val_loss_entropy': np.float64(2.293879)}} 2024-11-14 13:36:24,448 (server:353) INFO: Server: Starting evaluation at the end of round 10. 2024-11-14 13:36:24,449 (server:359) INFO: ----------- Starting a new training round (Round #11) ------------- 2024-11-14 13:38:42,234 (client:354) INFO: {'Role': 'Client #4', 'Round': 11, 'Results_raw': {'train_loss': 26.229198, 'val_loss': 22.999234, 'test_loss': 23.135812}} 2024-11-14 13:39:26,575 (client:354) INFO: {'Role': 'Client #9', 'Round': 11, 'Results_raw': {'train_loss': 27.124181, 'val_loss': 29.385711, 'test_loss': 25.545865}} 2024-11-14 13:40:10,850 (client:354) INFO: {'Role': 'Client #7', 'Round': 11, 'Results_raw': {'train_loss': 20.706675, 'val_loss': 19.64355, 'test_loss': 18.392657}} 2024-11-14 13:40:56,390 (client:354) INFO: {'Role': 'Client #5', 'Round': 11, 'Results_raw': {'train_loss': 21.946275, 'val_loss': 20.751376, 'test_loss': 22.344547}} 2024-11-14 13:41:44,136 (client:354) INFO: {'Role': 'Client #8', 'Round': 11, 'Results_raw': {'train_loss': 24.91511, 'val_loss': 25.145141, 'test_loss': 22.125391}} 2024-11-14 13:42:30,624 (client:354) INFO: {'Role': 'Client #10', 'Round': 11, 'Results_raw': {'train_loss': 24.121944, 'val_loss': 22.577072, 'test_loss': 22.417076}} 2024-11-14 13:43:16,822 (client:354) INFO: {'Role': 'Client #1', 'Round': 11, 'Results_raw': {'train_loss': 33.634894, 'val_loss': 30.915749, 'test_loss': 30.650478}} 2024-11-14 13:44:03,156 (client:354) INFO: {'Role': 'Client #6', 'Round': 11, 'Results_raw': {'train_loss': 19.334226, 'val_loss': 19.716305, 'test_loss': 19.967822}} 2024-11-14 13:44:48,678 (client:354) INFO: {'Role': 'Client #3', 'Round': 11, 'Results_raw': {'train_loss': 28.253645, 'val_loss': 26.695227, 'test_loss': 26.782472}} 2024-11-14 13:45:34,583 (client:354) INFO: {'Role': 'Client #2', 'Round': 11, 'Results_raw': {'train_loss': 25.215226, 'val_loss': 25.466282, 'test_loss': 24.648794}} 2024-11-14 13:45:34,588 (server:615) INFO: {'Role': 'Server #', 'Round': 10, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.544847), 'test_loss': np.float64(111037.860974), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.215162), 'val_loss': np.float64(116917.368964), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.544847), 'test_loss': np.float64(111037.860974), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.215162), 'val_loss': np.float64(116917.368964), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.362927), 'test_avg_loss_bottom_decile': np.float64(27.319616), 'test_avg_loss_top_decile': np.float64(41.62449), 'test_avg_loss_min': np.float64(25.366847), 'test_avg_loss_max': np.float64(41.62449), 'test_avg_loss_bottom10%': np.float64(25.366847), 'test_avg_loss_top10%': np.float64(41.62449), 'test_avg_loss_cos1': np.float64(0.99057), 'test_avg_loss_entropy': np.float64(2.29328), 'test_loss_std': np.float64(15357.503995), 'test_loss_bottom_decile': np.float64(96165.049805), 'test_loss_top_decile': np.float64(146518.203857), 'test_loss_min': np.float64(89291.300598), 'test_loss_max': np.float64(146518.203857), 'test_loss_bottom10%': np.float64(89291.300598), 'test_loss_top10%': np.float64(146518.203857), 'test_loss_cos1': np.float64(0.99057), 'test_loss_entropy': np.float64(2.29328), 'val_avg_loss_std': np.float64(4.450636), 'val_avg_loss_bottom_decile': np.float64(28.700987), 'val_avg_loss_top_decile': np.float64(42.873718), 'val_avg_loss_min': np.float64(26.99784), 'val_avg_loss_max': np.float64(42.873718), 'val_avg_loss_bottom10%': np.float64(26.99784), 'val_avg_loss_top10%': np.float64(42.873718), 'val_avg_loss_cos1': np.float64(0.991142), 'val_avg_loss_entropy': np.float64(2.293802), 'val_loss_std': np.float64(15666.237541), 'val_loss_bottom_decile': np.float64(101027.472778), 'val_loss_top_decile': np.float64(150915.487427), 'val_loss_min': np.float64(95032.395142), 'val_loss_max': np.float64(150915.487427), 'val_loss_bottom10%': np.float64(95032.395142), 'val_loss_top10%': np.float64(150915.487427), 'val_loss_cos1': np.float64(0.991142), 'val_loss_entropy': np.float64(2.293802)}} 2024-11-14 13:45:34,629 (server:353) INFO: Server: Starting evaluation at the end of round 11. 2024-11-14 13:45:34,629 (server:359) INFO: ----------- Starting a new training round (Round #12) ------------- 2024-11-14 13:47:50,801 (client:354) INFO: {'Role': 'Client #9', 'Round': 12, 'Results_raw': {'train_loss': 27.055154, 'val_loss': 29.259066, 'test_loss': 25.174191}} 2024-11-14 13:48:36,139 (client:354) INFO: {'Role': 'Client #2', 'Round': 12, 'Results_raw': {'train_loss': 25.11408, 'val_loss': 25.680905, 'test_loss': 24.657165}} 2024-11-14 13:49:21,306 (client:354) INFO: {'Role': 'Client #10', 'Round': 12, 'Results_raw': {'train_loss': 24.023382, 'val_loss': 22.886908, 'test_loss': 22.699495}} 2024-11-14 13:50:07,565 (client:354) INFO: {'Role': 'Client #7', 'Round': 12, 'Results_raw': {'train_loss': 20.578505, 'val_loss': 20.094868, 'test_loss': 18.842044}} 2024-11-14 13:50:53,014 (client:354) INFO: {'Role': 'Client #3', 'Round': 12, 'Results_raw': {'train_loss': 28.095725, 'val_loss': 26.418768, 'test_loss': 26.617503}} 2024-11-14 13:51:38,407 (client:354) INFO: {'Role': 'Client #5', 'Round': 12, 'Results_raw': {'train_loss': 21.704206, 'val_loss': 20.776446, 'test_loss': 21.613718}} 2024-11-14 13:52:27,223 (client:354) INFO: {'Role': 'Client #1', 'Round': 12, 'Results_raw': {'train_loss': 33.588343, 'val_loss': 31.886696, 'test_loss': 31.2987}} 2024-11-14 13:53:14,276 (client:354) INFO: {'Role': 'Client #6', 'Round': 12, 'Results_raw': {'train_loss': 19.196633, 'val_loss': 19.820468, 'test_loss': 19.710491}} 2024-11-14 13:54:00,876 (client:354) INFO: {'Role': 'Client #4', 'Round': 12, 'Results_raw': {'train_loss': 26.209895, 'val_loss': 22.453945, 'test_loss': 22.61444}} 2024-11-14 13:54:47,361 (client:354) INFO: {'Role': 'Client #8', 'Round': 12, 'Results_raw': {'train_loss': 24.844809, 'val_loss': 24.927894, 'test_loss': 21.791136}} 2024-11-14 13:54:47,372 (server:615) INFO: {'Role': 'Server #', 'Round': 11, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.448168), 'test_loss': np.float64(110697.551196), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.112438), 'val_loss': np.float64(116555.781915), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.448168), 'test_loss': np.float64(110697.551196), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(33.112438), 'val_loss': np.float64(116555.781915), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.394579), 'test_avg_loss_bottom_decile': np.float64(27.055052), 'test_avg_loss_top_decile': np.float64(41.536678), 'test_avg_loss_min': np.float64(25.132841), 'test_avg_loss_max': np.float64(41.536678), 'test_avg_loss_bottom10%': np.float64(25.132841), 'test_avg_loss_top10%': np.float64(41.536678), 'test_avg_loss_cos1': np.float64(0.990377), 'test_avg_loss_entropy': np.float64(2.293071), 'test_loss_std': np.float64(15468.919778), 'test_loss_bottom_decile': np.float64(95233.782654), 'test_loss_top_decile': np.float64(146209.10498), 'test_loss_min': np.float64(88467.60083), 'test_loss_max': np.float64(146209.10498), 'test_loss_bottom10%': np.float64(88467.60083), 'test_loss_top10%': np.float64(146209.10498), 'test_loss_cos1': np.float64(0.990377), 'test_loss_entropy': np.float64(2.293071), 'val_avg_loss_std': np.float64(4.496658), 'val_avg_loss_bottom_decile': np.float64(28.361109), 'val_avg_loss_top_decile': np.float64(42.751466), 'val_avg_loss_min': np.float64(26.772617), 'val_avg_loss_max': np.float64(42.751466), 'val_avg_loss_bottom10%': np.float64(26.772617), 'val_avg_loss_top10%': np.float64(42.751466), 'val_avg_loss_cos1': np.float64(0.990905), 'val_avg_loss_entropy': np.float64(2.29355), 'val_loss_std': np.float64(15828.235109), 'val_loss_bottom_decile': np.float64(99831.103394), 'val_loss_top_decile': np.float64(150485.159302), 'val_loss_min': np.float64(94239.61322), 'val_loss_max': np.float64(150485.159302), 'val_loss_bottom10%': np.float64(94239.61322), 'val_loss_top10%': np.float64(150485.159302), 'val_loss_cos1': np.float64(0.990905), 'val_loss_entropy': np.float64(2.29355)}} 2024-11-14 13:54:47,414 (server:353) INFO: Server: Starting evaluation at the end of round 12. 2024-11-14 13:54:47,415 (server:359) INFO: ----------- Starting a new training round (Round #13) ------------- 2024-11-14 13:57:05,381 (client:354) INFO: {'Role': 'Client #10', 'Round': 13, 'Results_raw': {'train_loss': 23.918857, 'val_loss': 22.644348, 'test_loss': 22.864548}} 2024-11-14 13:57:50,523 (client:354) INFO: {'Role': 'Client #5', 'Round': 13, 'Results_raw': {'train_loss': 21.641679, 'val_loss': 20.361818, 'test_loss': 22.783454}} 2024-11-14 13:58:36,089 (client:354) INFO: {'Role': 'Client #7', 'Round': 13, 'Results_raw': {'train_loss': 20.487003, 'val_loss': 19.691733, 'test_loss': 18.398801}} 2024-11-14 13:59:21,675 (client:354) INFO: {'Role': 'Client #8', 'Round': 13, 'Results_raw': {'train_loss': 24.732249, 'val_loss': 26.292327, 'test_loss': 23.336601}} 2024-11-14 14:00:07,698 (client:354) INFO: {'Role': 'Client #1', 'Round': 13, 'Results_raw': {'train_loss': 33.470266, 'val_loss': 30.968806, 'test_loss': 30.6092}} 2024-11-14 14:00:51,533 (client:354) INFO: {'Role': 'Client #9', 'Round': 13, 'Results_raw': {'train_loss': 26.860635, 'val_loss': 29.325022, 'test_loss': 25.096785}} 2024-11-14 14:01:36,047 (client:354) INFO: {'Role': 'Client #6', 'Round': 13, 'Results_raw': {'train_loss': 19.079004, 'val_loss': 19.800083, 'test_loss': 19.602446}} 2024-11-14 14:02:22,574 (client:354) INFO: {'Role': 'Client #3', 'Round': 13, 'Results_raw': {'train_loss': 27.914767, 'val_loss': 26.37046, 'test_loss': 26.42948}} 2024-11-14 14:03:08,699 (client:354) INFO: {'Role': 'Client #2', 'Round': 13, 'Results_raw': {'train_loss': 25.083321, 'val_loss': 25.465273, 'test_loss': 24.490138}} 2024-11-14 14:03:54,757 (client:354) INFO: {'Role': 'Client #4', 'Round': 13, 'Results_raw': {'train_loss': 26.156664, 'val_loss': 22.46803, 'test_loss': 22.516638}} 2024-11-14 14:03:54,768 (server:615) INFO: {'Role': 'Server #', 'Round': 12, 'Results_weighted_avg': {'test_avg_loss': np.float64(31.244981), 'test_loss': np.float64(109982.333008), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.924008), 'val_loss': np.float64(115892.508459), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(31.244981), 'test_loss': np.float64(109982.333008), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.924008), 'val_loss': np.float64(115892.508459), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.399818), 'test_avg_loss_bottom_decile': np.float64(26.873983), 'test_avg_loss_top_decile': np.float64(41.39439), 'test_avg_loss_min': np.float64(24.920843), 'test_avg_loss_max': np.float64(41.39439), 'test_avg_loss_bottom10%': np.float64(24.920843), 'test_avg_loss_top10%': np.float64(41.39439), 'test_avg_loss_cos1': np.float64(0.99023), 'test_avg_loss_entropy': np.float64(2.292939), 'test_loss_std': np.float64(15487.358207), 'test_loss_bottom_decile': np.float64(94596.419312), 'test_loss_top_decile': np.float64(145708.251099), 'test_loss_min': np.float64(87721.368835), 'test_loss_max': np.float64(145708.251099), 'test_loss_bottom10%': np.float64(87721.368835), 'test_loss_top10%': np.float64(145708.251099), 'test_loss_cos1': np.float64(0.99023), 'test_loss_entropy': np.float64(2.292939), 'val_avg_loss_std': np.float64(4.493589), 'val_avg_loss_bottom_decile': np.float64(28.23366), 'val_avg_loss_top_decile': np.float64(42.609577), 'val_avg_loss_min': np.float64(26.524237), 'val_avg_loss_max': np.float64(42.609577), 'val_avg_loss_bottom10%': np.float64(26.524237), 'val_avg_loss_top10%': np.float64(42.609577), 'val_avg_loss_cos1': np.float64(0.990814), 'val_avg_loss_entropy': np.float64(2.293467), 'val_loss_std': np.float64(15817.432072), 'val_loss_bottom_decile': np.float64(99382.482849), 'val_loss_top_decile': np.float64(149985.710449), 'val_loss_min': np.float64(93365.314026), 'val_loss_max': np.float64(149985.710449), 'val_loss_bottom10%': np.float64(93365.314026), 'val_loss_top10%': np.float64(149985.710449), 'val_loss_cos1': np.float64(0.990814), 'val_loss_entropy': np.float64(2.293467)}} 2024-11-14 14:03:54,804 (server:353) INFO: Server: Starting evaluation at the end of round 13. 2024-11-14 14:03:54,805 (server:359) INFO: ----------- Starting a new training round (Round #14) ------------- 2024-11-14 14:06:12,801 (client:354) INFO: {'Role': 'Client #9', 'Round': 14, 'Results_raw': {'train_loss': 26.770812, 'val_loss': 29.399668, 'test_loss': 25.125352}} 2024-11-14 14:06:58,820 (client:354) INFO: {'Role': 'Client #4', 'Round': 14, 'Results_raw': {'train_loss': 26.03558, 'val_loss': 22.093181, 'test_loss': 22.403719}} 2024-11-14 14:07:44,526 (client:354) INFO: {'Role': 'Client #8', 'Round': 14, 'Results_raw': {'train_loss': 24.597943, 'val_loss': 25.851106, 'test_loss': 23.02555}} 2024-11-14 14:08:29,736 (client:354) INFO: {'Role': 'Client #6', 'Round': 14, 'Results_raw': {'train_loss': 19.023439, 'val_loss': 19.698453, 'test_loss': 19.662776}} 2024-11-14 14:09:15,782 (client:354) INFO: {'Role': 'Client #7', 'Round': 14, 'Results_raw': {'train_loss': 20.516573, 'val_loss': 19.815459, 'test_loss': 18.601883}} 2024-11-14 14:10:01,189 (client:354) INFO: {'Role': 'Client #5', 'Round': 14, 'Results_raw': {'train_loss': 21.497023, 'val_loss': 20.378628, 'test_loss': 22.160241}} 2024-11-14 14:10:48,419 (client:354) INFO: {'Role': 'Client #3', 'Round': 14, 'Results_raw': {'train_loss': 27.925169, 'val_loss': 26.375038, 'test_loss': 26.413555}} 2024-11-14 14:11:34,299 (client:354) INFO: {'Role': 'Client #10', 'Round': 14, 'Results_raw': {'train_loss': 23.708716, 'val_loss': 22.405088, 'test_loss': 22.174422}} 2024-11-14 14:12:20,580 (client:354) INFO: {'Role': 'Client #1', 'Round': 14, 'Results_raw': {'train_loss': 33.389302, 'val_loss': 30.612807, 'test_loss': 30.301663}} 2024-11-14 14:13:06,567 (client:354) INFO: {'Role': 'Client #2', 'Round': 14, 'Results_raw': {'train_loss': 24.960029, 'val_loss': 25.477225, 'test_loss': 24.638746}} 2024-11-14 14:13:06,570 (server:615) INFO: {'Role': 'Server #', 'Round': 13, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.988995), 'test_loss': np.float64(109081.263824), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.666517), 'val_loss': np.float64(114986.141095), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.988995), 'test_loss': np.float64(109081.263824), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.666517), 'val_loss': np.float64(114986.141095), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.350388), 'test_avg_loss_bottom_decile': np.float64(26.680865), 'test_avg_loss_top_decile': np.float64(41.225298), 'test_avg_loss_min': np.float64(24.954927), 'test_avg_loss_max': np.float64(41.225298), 'test_avg_loss_bottom10%': np.float64(24.954927), 'test_avg_loss_top10%': np.float64(41.225298), 'test_avg_loss_cos1': np.float64(0.990289), 'test_avg_loss_entropy': np.float64(2.293041), 'test_loss_std': np.float64(15313.367094), 'test_loss_bottom_decile': np.float64(93916.645386), 'test_loss_top_decile': np.float64(145113.049805), 'test_loss_min': np.float64(87841.342651), 'test_loss_max': np.float64(145113.049805), 'test_loss_bottom10%': np.float64(87841.342651), 'test_loss_top10%': np.float64(145113.049805), 'test_loss_cos1': np.float64(0.990289), 'test_loss_entropy': np.float64(2.293041), 'val_avg_loss_std': np.float64(4.430091), 'val_avg_loss_bottom_decile': np.float64(28.072764), 'val_avg_loss_top_decile': np.float64(42.391572), 'val_avg_loss_min': np.float64(26.572497), 'val_avg_loss_max': np.float64(42.391572), 'val_avg_loss_bottom10%': np.float64(26.572497), 'val_avg_loss_top10%': np.float64(42.391572), 'val_avg_loss_cos1': np.float64(0.990929), 'val_avg_loss_entropy': np.float64(2.293613), 'val_loss_std': np.float64(15593.918963), 'val_loss_bottom_decile': np.float64(98816.1297), 'val_loss_top_decile': np.float64(149218.334106), 'val_loss_min': np.float64(93535.190247), 'val_loss_max': np.float64(149218.334106), 'val_loss_bottom10%': np.float64(93535.190247), 'val_loss_top10%': np.float64(149218.334106), 'val_loss_cos1': np.float64(0.990929), 'val_loss_entropy': np.float64(2.293613)}} 2024-11-14 14:13:06,606 (server:353) INFO: Server: Starting evaluation at the end of round 14. 2024-11-14 14:13:06,606 (server:359) INFO: ----------- Starting a new training round (Round #15) ------------- 2024-11-14 14:15:23,178 (client:354) INFO: {'Role': 'Client #7', 'Round': 15, 'Results_raw': {'train_loss': 20.431255, 'val_loss': 19.926873, 'test_loss': 18.657537}} 2024-11-14 14:16:09,215 (client:354) INFO: {'Role': 'Client #3', 'Round': 15, 'Results_raw': {'train_loss': 27.777334, 'val_loss': 26.440768, 'test_loss': 26.694447}} 2024-11-14 14:16:55,440 (client:354) INFO: {'Role': 'Client #4', 'Round': 15, 'Results_raw': {'train_loss': 25.925956, 'val_loss': 22.719201, 'test_loss': 22.968798}} 2024-11-14 14:17:41,739 (client:354) INFO: {'Role': 'Client #5', 'Round': 15, 'Results_raw': {'train_loss': 21.423625, 'val_loss': 20.263053, 'test_loss': 21.860793}} 2024-11-14 14:18:26,483 (client:354) INFO: {'Role': 'Client #9', 'Round': 15, 'Results_raw': {'train_loss': 26.594657, 'val_loss': 29.342497, 'test_loss': 25.033813}} 2024-11-14 14:19:11,794 (client:354) INFO: {'Role': 'Client #2', 'Round': 15, 'Results_raw': {'train_loss': 24.794396, 'val_loss': 25.706108, 'test_loss': 24.668944}} 2024-11-14 14:19:58,243 (client:354) INFO: {'Role': 'Client #6', 'Round': 15, 'Results_raw': {'train_loss': 19.075183, 'val_loss': 19.606117, 'test_loss': 19.861408}} 2024-11-14 14:20:44,239 (client:354) INFO: {'Role': 'Client #8', 'Round': 15, 'Results_raw': {'train_loss': 24.491689, 'val_loss': 26.063838, 'test_loss': 23.129287}} 2024-11-14 14:21:30,263 (client:354) INFO: {'Role': 'Client #10', 'Round': 15, 'Results_raw': {'train_loss': 23.762709, 'val_loss': 22.382314, 'test_loss': 22.480193}} 2024-11-14 14:22:16,139 (client:354) INFO: {'Role': 'Client #1', 'Round': 15, 'Results_raw': {'train_loss': 33.264532, 'val_loss': 31.055962, 'test_loss': 30.438658}} 2024-11-14 14:22:16,144 (server:615) INFO: {'Role': 'Server #', 'Round': 14, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.954753), 'test_loss': np.float64(108960.730261), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.607193), 'val_loss': np.float64(114777.319922), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.954753), 'test_loss': np.float64(108960.730261), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.607193), 'val_loss': np.float64(114777.319922), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.308471), 'test_avg_loss_bottom_decile': np.float64(26.630972), 'test_avg_loss_top_decile': np.float64(40.941373), 'test_avg_loss_min': np.float64(24.873067), 'test_avg_loss_max': np.float64(40.941373), 'test_avg_loss_bottom10%': np.float64(24.873067), 'test_avg_loss_top10%': np.float64(40.941373), 'test_avg_loss_cos1': np.float64(0.990452), 'test_avg_loss_entropy': np.float64(2.29317), 'test_loss_std': np.float64(15165.816993), 'test_loss_bottom_decile': np.float64(93741.020142), 'test_loss_top_decile': np.float64(144113.634033), 'test_loss_min': np.float64(87553.19458), 'test_loss_max': np.float64(144113.634033), 'test_loss_bottom10%': np.float64(87553.19458), 'test_loss_top10%': np.float64(144113.634033), 'test_loss_cos1': np.float64(0.990452), 'test_loss_entropy': np.float64(2.29317), 'val_avg_loss_std': np.float64(4.410484), 'val_avg_loss_bottom_decile': np.float64(27.991463), 'val_avg_loss_top_decile': np.float64(42.130179), 'val_avg_loss_min': np.float64(26.472392), 'val_avg_loss_max': np.float64(42.130179), 'val_avg_loss_bottom10%': np.float64(26.472392), 'val_avg_loss_top10%': np.float64(42.130179), 'val_avg_loss_cos1': np.float64(0.990976), 'val_avg_loss_entropy': np.float64(2.293637), 'val_loss_std': np.float64(15524.902245), 'val_loss_bottom_decile': np.float64(98529.950439), 'val_loss_top_decile': np.float64(148298.229004), 'val_loss_min': np.float64(93182.819519), 'val_loss_max': np.float64(148298.229004), 'val_loss_bottom10%': np.float64(93182.819519), 'val_loss_top10%': np.float64(148298.229004), 'val_loss_cos1': np.float64(0.990976), 'val_loss_entropy': np.float64(2.293637)}} 2024-11-14 14:22:16,182 (server:353) INFO: Server: Starting evaluation at the end of round 15. 2024-11-14 14:22:16,183 (server:359) INFO: ----------- Starting a new training round (Round #16) ------------- 2024-11-14 14:24:25,881 (client:354) INFO: {'Role': 'Client #2', 'Round': 16, 'Results_raw': {'train_loss': 24.704107, 'val_loss': 25.415352, 'test_loss': 24.480068}} 2024-11-14 14:25:11,433 (client:354) INFO: {'Role': 'Client #9', 'Round': 16, 'Results_raw': {'train_loss': 26.551627, 'val_loss': 29.186507, 'test_loss': 24.871285}} 2024-11-14 14:25:58,112 (client:354) INFO: {'Role': 'Client #5', 'Round': 16, 'Results_raw': {'train_loss': 21.379682, 'val_loss': 20.500872, 'test_loss': 21.564829}} 2024-11-14 14:26:46,015 (client:354) INFO: {'Role': 'Client #8', 'Round': 16, 'Results_raw': {'train_loss': 24.546578, 'val_loss': 26.430942, 'test_loss': 23.233742}} 2024-11-14 14:27:33,534 (client:354) INFO: {'Role': 'Client #6', 'Round': 16, 'Results_raw': {'train_loss': 19.029537, 'val_loss': 19.323635, 'test_loss': 19.312758}} 2024-11-14 14:28:21,135 (client:354) INFO: {'Role': 'Client #10', 'Round': 16, 'Results_raw': {'train_loss': 23.626995, 'val_loss': 22.549135, 'test_loss': 22.61214}} 2024-11-14 14:29:09,374 (client:354) INFO: {'Role': 'Client #4', 'Round': 16, 'Results_raw': {'train_loss': 25.836785, 'val_loss': 21.860153, 'test_loss': 22.101278}} 2024-11-14 14:29:54,893 (client:354) INFO: {'Role': 'Client #3', 'Round': 16, 'Results_raw': {'train_loss': 27.637448, 'val_loss': 26.462028, 'test_loss': 26.532403}} 2024-11-14 14:30:39,350 (client:354) INFO: {'Role': 'Client #1', 'Round': 16, 'Results_raw': {'train_loss': 33.091454, 'val_loss': 30.840515, 'test_loss': 30.389119}} 2024-11-14 14:31:21,576 (client:354) INFO: {'Role': 'Client #7', 'Round': 16, 'Results_raw': {'train_loss': 20.228086, 'val_loss': 19.828883, 'test_loss': 18.584485}} 2024-11-14 14:31:21,580 (server:615) INFO: {'Role': 'Server #', 'Round': 15, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.833212), 'test_loss': np.float64(108532.907654), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.470442), 'val_loss': np.float64(114295.955334), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.833212), 'test_loss': np.float64(108532.907654), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.470442), 'val_loss': np.float64(114295.955334), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.413083), 'test_avg_loss_bottom_decile': np.float64(26.036212), 'test_avg_loss_top_decile': np.float64(40.837689), 'test_avg_loss_min': np.float64(24.452051), 'test_avg_loss_max': np.float64(40.837689), 'test_avg_loss_bottom10%': np.float64(24.452051), 'test_avg_loss_top10%': np.float64(40.837689), 'test_avg_loss_cos1': np.float64(0.989912), 'test_avg_loss_entropy': np.float64(2.292587), 'test_loss_std': np.float64(15534.052816), 'test_loss_bottom_decile': np.float64(91647.466187), 'test_loss_top_decile': np.float64(143748.66687), 'test_loss_min': np.float64(86071.219727), 'test_loss_max': np.float64(143748.66687), 'test_loss_bottom10%': np.float64(86071.219727), 'test_loss_top10%': np.float64(143748.66687), 'test_loss_cos1': np.float64(0.989912), 'test_loss_entropy': np.float64(2.292587), 'val_avg_loss_std': np.float64(4.513362), 'val_avg_loss_bottom_decile': np.float64(27.392401), 'val_avg_loss_top_decile': np.float64(41.990358), 'val_avg_loss_min': np.float64(26.022275), 'val_avg_loss_max': np.float64(41.990358), 'val_avg_loss_bottom10%': np.float64(26.022275), 'val_avg_loss_top10%': np.float64(41.990358), 'val_avg_loss_cos1': np.float64(0.990477), 'val_avg_loss_entropy': np.float64(2.293099), 'val_loss_std': np.float64(15887.03493), 'val_loss_bottom_decile': np.float64(96421.252563), 'val_loss_top_decile': np.float64(147806.059082), 'val_loss_min': np.float64(91598.408569), 'val_loss_max': np.float64(147806.059082), 'val_loss_bottom10%': np.float64(91598.408569), 'val_loss_top10%': np.float64(147806.059082), 'val_loss_cos1': np.float64(0.990477), 'val_loss_entropy': np.float64(2.293099)}} 2024-11-14 14:31:21,613 (server:353) INFO: Server: Starting evaluation at the end of round 16. 2024-11-14 14:31:21,614 (server:359) INFO: ----------- Starting a new training round (Round #17) ------------- 2024-11-14 14:33:28,884 (client:354) INFO: {'Role': 'Client #10', 'Round': 17, 'Results_raw': {'train_loss': 23.607741, 'val_loss': 22.732884, 'test_loss': 22.491397}} 2024-11-14 14:34:11,436 (client:354) INFO: {'Role': 'Client #5', 'Round': 17, 'Results_raw': {'train_loss': 21.255579, 'val_loss': 20.285942, 'test_loss': 21.182447}} 2024-11-14 14:34:53,698 (client:354) INFO: {'Role': 'Client #3', 'Round': 17, 'Results_raw': {'train_loss': 27.603032, 'val_loss': 26.425212, 'test_loss': 26.505749}} 2024-11-14 14:35:35,646 (client:354) INFO: {'Role': 'Client #7', 'Round': 17, 'Results_raw': {'train_loss': 20.165219, 'val_loss': 19.54246, 'test_loss': 18.246423}} 2024-11-14 14:36:19,447 (client:354) INFO: {'Role': 'Client #2', 'Round': 17, 'Results_raw': {'train_loss': 24.666574, 'val_loss': 25.396834, 'test_loss': 24.607457}} 2024-11-14 14:37:05,471 (client:354) INFO: {'Role': 'Client #4', 'Round': 17, 'Results_raw': {'train_loss': 25.757799, 'val_loss': 22.163997, 'test_loss': 22.598119}} 2024-11-14 14:37:46,698 (client:354) INFO: {'Role': 'Client #8', 'Round': 17, 'Results_raw': {'train_loss': 24.355836, 'val_loss': 25.574924, 'test_loss': 22.062799}} 2024-11-14 14:38:30,460 (client:354) INFO: {'Role': 'Client #9', 'Round': 17, 'Results_raw': {'train_loss': 26.46525, 'val_loss': 29.480264, 'test_loss': 25.25826}} 2024-11-14 14:39:13,867 (client:354) INFO: {'Role': 'Client #6', 'Round': 17, 'Results_raw': {'train_loss': 18.847803, 'val_loss': 19.704264, 'test_loss': 20.222926}} 2024-11-14 14:39:57,960 (client:354) INFO: {'Role': 'Client #1', 'Round': 17, 'Results_raw': {'train_loss': 33.08656, 'val_loss': 30.739096, 'test_loss': 30.479795}} 2024-11-14 14:39:57,963 (server:615) INFO: {'Role': 'Server #', 'Round': 16, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.754551), 'test_loss': np.float64(108256.020703), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.431374), 'val_loss': np.float64(114158.437567), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.754551), 'test_loss': np.float64(108256.020703), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.431374), 'val_loss': np.float64(114158.437567), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.315783), 'test_avg_loss_bottom_decile': np.float64(26.057208), 'test_avg_loss_top_decile': np.float64(40.603907), 'test_avg_loss_min': np.float64(24.605851), 'test_avg_loss_max': np.float64(40.603907), 'test_avg_loss_bottom10%': np.float64(24.605851), 'test_avg_loss_top10%': np.float64(40.603907), 'test_avg_loss_cos1': np.float64(0.990297), 'test_avg_loss_entropy': np.float64(2.292985), 'test_loss_std': np.float64(15191.557554), 'test_loss_bottom_decile': np.float64(91721.370544), 'test_loss_top_decile': np.float64(142925.753784), 'test_loss_min': np.float64(86612.59613), 'test_loss_max': np.float64(142925.753784), 'test_loss_bottom10%': np.float64(86612.59613), 'test_loss_top10%': np.float64(142925.753784), 'test_loss_cos1': np.float64(0.990297), 'test_loss_entropy': np.float64(2.292985), 'val_avg_loss_std': np.float64(4.435669), 'val_avg_loss_bottom_decile': np.float64(27.411194), 'val_avg_loss_top_decile': np.float64(41.783339), 'val_avg_loss_min': np.float64(26.16214), 'val_avg_loss_max': np.float64(41.783339), 'val_avg_loss_bottom10%': np.float64(26.16214), 'val_avg_loss_top10%': np.float64(41.783339), 'val_avg_loss_cos1': np.float64(0.990776), 'val_avg_loss_entropy': np.float64(2.293403), 'val_loss_std': np.float64(15613.555228), 'val_loss_bottom_decile': np.float64(96487.404053), 'val_loss_top_decile': np.float64(147077.354004), 'val_loss_min': np.float64(92090.734009), 'val_loss_max': np.float64(147077.354004), 'val_loss_bottom10%': np.float64(92090.734009), 'val_loss_top10%': np.float64(147077.354004), 'val_loss_cos1': np.float64(0.990776), 'val_loss_entropy': np.float64(2.293403)}} 2024-11-14 14:39:58,002 (server:353) INFO: Server: Starting evaluation at the end of round 17. 2024-11-14 14:39:58,002 (server:359) INFO: ----------- Starting a new training round (Round #18) ------------- 2024-11-14 14:42:19,233 (client:354) INFO: {'Role': 'Client #3', 'Round': 18, 'Results_raw': {'train_loss': 27.50603, 'val_loss': 26.132623, 'test_loss': 26.377146}} 2024-11-14 14:43:05,530 (client:354) INFO: {'Role': 'Client #5', 'Round': 18, 'Results_raw': {'train_loss': 21.356177, 'val_loss': 20.547564, 'test_loss': 21.272659}} 2024-11-14 14:43:51,589 (client:354) INFO: {'Role': 'Client #7', 'Round': 18, 'Results_raw': {'train_loss': 20.21347, 'val_loss': 19.707298, 'test_loss': 18.331909}} 2024-11-14 14:44:37,482 (client:354) INFO: {'Role': 'Client #9', 'Round': 18, 'Results_raw': {'train_loss': 26.398035, 'val_loss': 29.303646, 'test_loss': 24.897907}} 2024-11-14 14:45:22,483 (client:354) INFO: {'Role': 'Client #10', 'Round': 18, 'Results_raw': {'train_loss': 23.47876, 'val_loss': 22.142229, 'test_loss': 22.276684}} 2024-11-14 14:46:07,325 (client:354) INFO: {'Role': 'Client #1', 'Round': 18, 'Results_raw': {'train_loss': 33.015497, 'val_loss': 30.720998, 'test_loss': 30.402002}} 2024-11-14 14:46:52,348 (client:354) INFO: {'Role': 'Client #4', 'Round': 18, 'Results_raw': {'train_loss': 25.695038, 'val_loss': 22.391442, 'test_loss': 22.911151}} 2024-11-14 14:47:38,504 (client:354) INFO: {'Role': 'Client #8', 'Round': 18, 'Results_raw': {'train_loss': 24.266732, 'val_loss': 25.946416, 'test_loss': 22.664168}} 2024-11-14 14:48:24,878 (client:354) INFO: {'Role': 'Client #2', 'Round': 18, 'Results_raw': {'train_loss': 24.637801, 'val_loss': 25.214946, 'test_loss': 24.354486}} 2024-11-14 14:49:10,521 (client:354) INFO: {'Role': 'Client #6', 'Round': 18, 'Results_raw': {'train_loss': 18.829709, 'val_loss': 20.310636, 'test_loss': 20.753063}} 2024-11-14 14:49:10,524 (server:615) INFO: {'Role': 'Server #', 'Round': 17, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.663366), 'test_loss': np.float64(107935.049615), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.311827), 'val_loss': np.float64(113737.629724), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.663366), 'test_loss': np.float64(107935.049615), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.311827), 'val_loss': np.float64(113737.629724), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.226162), 'test_avg_loss_bottom_decile': np.float64(26.46698), 'test_avg_loss_top_decile': np.float64(40.556014), 'test_avg_loss_min': np.float64(24.806396), 'test_avg_loss_max': np.float64(40.556014), 'test_avg_loss_bottom10%': np.float64(24.806396), 'test_avg_loss_top10%': np.float64(40.556014), 'test_avg_loss_cos1': np.float64(0.990635), 'test_avg_loss_entropy': np.float64(2.293379), 'test_loss_std': np.float64(14876.091727), 'test_loss_bottom_decile': np.float64(93163.768555), 'test_loss_top_decile': np.float64(142757.170044), 'test_loss_min': np.float64(87318.512695), 'test_loss_max': np.float64(142757.170044), 'test_loss_bottom10%': np.float64(87318.512695), 'test_loss_top10%': np.float64(142757.170044), 'test_loss_cos1': np.float64(0.990635), 'test_loss_entropy': np.float64(2.293379), 'val_avg_loss_std': np.float64(4.311624), 'val_avg_loss_bottom_decile': np.float64(27.866887), 'val_avg_loss_top_decile': np.float64(41.668088), 'val_avg_loss_min': np.float64(26.376571), 'val_avg_loss_max': np.float64(41.668088), 'val_avg_loss_bottom10%': np.float64(26.376571), 'val_avg_loss_top10%': np.float64(41.668088), 'val_avg_loss_cos1': np.float64(0.991214), 'val_avg_loss_entropy': np.float64(2.293888), 'val_loss_std': np.float64(15176.916233), 'val_loss_bottom_decile': np.float64(98091.441345), 'val_loss_top_decile': np.float64(146671.669189), 'val_loss_min': np.float64(92845.529358), 'val_loss_max': np.float64(146671.669189), 'val_loss_bottom10%': np.float64(92845.529358), 'val_loss_top10%': np.float64(146671.669189), 'val_loss_cos1': np.float64(0.991214), 'val_loss_entropy': np.float64(2.293888)}} 2024-11-14 14:49:10,564 (server:353) INFO: Server: Starting evaluation at the end of round 18. 2024-11-14 14:49:10,565 (server:359) INFO: ----------- Starting a new training round (Round #19) ------------- 2024-11-14 14:51:23,957 (client:354) INFO: {'Role': 'Client #3', 'Round': 19, 'Results_raw': {'train_loss': 27.467148, 'val_loss': 26.238037, 'test_loss': 26.225206}} 2024-11-14 14:52:09,862 (client:354) INFO: {'Role': 'Client #7', 'Round': 19, 'Results_raw': {'train_loss': 20.124002, 'val_loss': 19.8386, 'test_loss': 18.549695}} 2024-11-14 14:52:55,644 (client:354) INFO: {'Role': 'Client #2', 'Round': 19, 'Results_raw': {'train_loss': 24.499911, 'val_loss': 25.173914, 'test_loss': 24.353328}} 2024-11-14 14:53:44,275 (client:354) INFO: {'Role': 'Client #4', 'Round': 19, 'Results_raw': {'train_loss': 25.598317, 'val_loss': 22.211983, 'test_loss': 22.888339}} 2024-11-14 14:54:33,977 (client:354) INFO: {'Role': 'Client #5', 'Round': 19, 'Results_raw': {'train_loss': 21.235173, 'val_loss': 20.511508, 'test_loss': 21.538837}} 2024-11-14 14:55:20,447 (client:354) INFO: {'Role': 'Client #9', 'Round': 19, 'Results_raw': {'train_loss': 26.349442, 'val_loss': 29.328751, 'test_loss': 24.843293}} 2024-11-14 14:56:07,346 (client:354) INFO: {'Role': 'Client #10', 'Round': 19, 'Results_raw': {'train_loss': 23.384354, 'val_loss': 22.508391, 'test_loss': 22.377054}} 2024-11-14 14:56:54,372 (client:354) INFO: {'Role': 'Client #1', 'Round': 19, 'Results_raw': {'train_loss': 32.813696, 'val_loss': 30.307648, 'test_loss': 30.033113}} 2024-11-14 14:57:41,313 (client:354) INFO: {'Role': 'Client #8', 'Round': 19, 'Results_raw': {'train_loss': 24.179017, 'val_loss': 25.250322, 'test_loss': 22.118744}} 2024-11-14 14:58:27,634 (client:354) INFO: {'Role': 'Client #6', 'Round': 19, 'Results_raw': {'train_loss': 18.860679, 'val_loss': 19.467459, 'test_loss': 19.650326}} 2024-11-14 14:58:27,637 (server:615) INFO: {'Role': 'Server #', 'Round': 18, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.774366), 'test_loss': np.float64(108325.766882), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.409718), 'val_loss': np.float64(114082.209027), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.774366), 'test_loss': np.float64(108325.766882), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.409718), 'val_loss': np.float64(114082.209027), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.260028), 'test_avg_loss_bottom_decile': np.float64(26.46402), 'test_avg_loss_top_decile': np.float64(40.564249), 'test_avg_loss_min': np.float64(24.75946), 'test_avg_loss_max': np.float64(40.564249), 'test_avg_loss_bottom10%': np.float64(24.75946), 'test_avg_loss_top10%': np.float64(40.564249), 'test_avg_loss_cos1': np.float64(0.990554), 'test_avg_loss_entropy': np.float64(2.293265), 'test_loss_std': np.float64(14995.29944), 'test_loss_bottom_decile': np.float64(93153.349915), 'test_loss_top_decile': np.float64(142786.155518), 'test_loss_min': np.float64(87153.299194), 'test_loss_max': np.float64(142786.155518), 'test_loss_bottom10%': np.float64(87153.299194), 'test_loss_top10%': np.float64(142786.155518), 'test_loss_cos1': np.float64(0.990554), 'test_loss_entropy': np.float64(2.293265), 'val_avg_loss_std': np.float64(4.360774), 'val_avg_loss_bottom_decile': np.float64(27.829535), 'val_avg_loss_top_decile': np.float64(41.721259), 'val_avg_loss_min': np.float64(26.276955), 'val_avg_loss_max': np.float64(41.721259), 'val_avg_loss_bottom10%': np.float64(26.276955), 'val_avg_loss_top10%': np.float64(41.721259), 'val_avg_loss_cos1': np.float64(0.991069), 'val_avg_loss_entropy': np.float64(2.293724), 'val_loss_std': np.float64(15349.924613), 'val_loss_bottom_decile': np.float64(97959.961731), 'val_loss_top_decile': np.float64(146858.830933), 'val_loss_min': np.float64(92494.881592), 'val_loss_max': np.float64(146858.830933), 'val_loss_bottom10%': np.float64(92494.881592), 'val_loss_top10%': np.float64(146858.830933), 'val_loss_cos1': np.float64(0.991069), 'val_loss_entropy': np.float64(2.293724)}} 2024-11-14 14:58:27,682 (server:353) INFO: Server: Starting evaluation at the end of round 19. 2024-11-14 14:58:27,683 (server:359) INFO: ----------- Starting a new training round (Round #20) ------------- 2024-11-14 15:00:42,103 (client:354) INFO: {'Role': 'Client #3', 'Round': 20, 'Results_raw': {'train_loss': 27.476555, 'val_loss': 26.177587, 'test_loss': 26.196948}} 2024-11-14 15:01:29,179 (client:354) INFO: {'Role': 'Client #4', 'Round': 20, 'Results_raw': {'train_loss': 25.626746, 'val_loss': 21.719759, 'test_loss': 22.222298}} 2024-11-14 15:02:16,477 (client:354) INFO: {'Role': 'Client #1', 'Round': 20, 'Results_raw': {'train_loss': 32.728339, 'val_loss': 30.543123, 'test_loss': 30.265488}} 2024-11-14 15:03:03,513 (client:354) INFO: {'Role': 'Client #5', 'Round': 20, 'Results_raw': {'train_loss': 21.136446, 'val_loss': 20.368037, 'test_loss': 21.42454}} 2024-11-14 15:03:49,746 (client:354) INFO: {'Role': 'Client #9', 'Round': 20, 'Results_raw': {'train_loss': 26.396469, 'val_loss': 29.48984, 'test_loss': 24.924619}} 2024-11-14 15:04:36,306 (client:354) INFO: {'Role': 'Client #8', 'Round': 20, 'Results_raw': {'train_loss': 24.116659, 'val_loss': 24.907918, 'test_loss': 21.852707}} 2024-11-14 15:05:23,784 (client:354) INFO: {'Role': 'Client #7', 'Round': 20, 'Results_raw': {'train_loss': 20.100422, 'val_loss': 19.650102, 'test_loss': 18.198965}} 2024-11-14 15:06:11,225 (client:354) INFO: {'Role': 'Client #6', 'Round': 20, 'Results_raw': {'train_loss': 18.73937, 'val_loss': 19.711917, 'test_loss': 20.365243}} 2024-11-14 15:06:58,412 (client:354) INFO: {'Role': 'Client #2', 'Round': 20, 'Results_raw': {'train_loss': 24.421703, 'val_loss': 25.455296, 'test_loss': 24.671593}} 2024-11-14 15:07:44,033 (client:354) INFO: {'Role': 'Client #10', 'Round': 20, 'Results_raw': {'train_loss': 23.372046, 'val_loss': 22.510159, 'test_loss': 22.344551}} 2024-11-14 15:07:44,036 (server:615) INFO: {'Role': 'Server #', 'Round': 19, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.525246), 'test_loss': np.float64(107448.86507), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.171428), 'val_loss': np.float64(113243.425421), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.525246), 'test_loss': np.float64(107448.86507), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.171428), 'val_loss': np.float64(113243.425421), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.256037), 'test_avg_loss_bottom_decile': np.float64(25.849631), 'test_avg_loss_top_decile': np.float64(40.141855), 'test_avg_loss_min': np.float64(24.428282), 'test_avg_loss_max': np.float64(40.141855), 'test_avg_loss_bottom10%': np.float64(24.428282), 'test_avg_loss_top10%': np.float64(40.141855), 'test_avg_loss_cos1': np.float64(0.99042), 'test_avg_loss_entropy': np.float64(2.293092), 'test_loss_std': np.float64(14981.24949), 'test_loss_bottom_decile': np.float64(90990.700806), 'test_loss_top_decile': np.float64(141299.329712), 'test_loss_min': np.float64(85987.553101), 'test_loss_max': np.float64(141299.329712), 'test_loss_bottom10%': np.float64(85987.553101), 'test_loss_top10%': np.float64(141299.329712), 'test_loss_cos1': np.float64(0.99042), 'test_loss_entropy': np.float64(2.293092), 'val_avg_loss_std': np.float64(4.381579), 'val_avg_loss_bottom_decile': np.float64(27.274664), 'val_avg_loss_top_decile': np.float64(41.315256), 'val_avg_loss_min': np.float64(25.964474), 'val_avg_loss_max': np.float64(41.315256), 'val_avg_loss_bottom10%': np.float64(25.964474), 'val_avg_loss_top10%': np.float64(41.315256), 'val_avg_loss_cos1': np.float64(0.990853), 'val_avg_loss_entropy': np.float64(2.293476), 'val_loss_std': np.float64(15423.159275), 'val_loss_bottom_decile': np.float64(96006.818054), 'val_loss_top_decile': np.float64(145429.701782), 'val_loss_min': np.float64(91394.94873), 'val_loss_max': np.float64(145429.701782), 'val_loss_bottom10%': np.float64(91394.94873), 'val_loss_top10%': np.float64(145429.701782), 'val_loss_cos1': np.float64(0.990853), 'val_loss_entropy': np.float64(2.293476)}} 2024-11-14 15:07:44,067 (server:353) INFO: Server: Starting evaluation at the end of round 20. 2024-11-14 15:07:44,068 (server:359) INFO: ----------- Starting a new training round (Round #21) ------------- 2024-11-14 15:09:55,436 (client:354) INFO: {'Role': 'Client #5', 'Round': 21, 'Results_raw': {'train_loss': 21.161157, 'val_loss': 20.416549, 'test_loss': 21.803838}} 2024-11-14 15:10:41,535 (client:354) INFO: {'Role': 'Client #3', 'Round': 21, 'Results_raw': {'train_loss': 27.32289, 'val_loss': 26.182108, 'test_loss': 26.278364}} 2024-11-14 15:11:27,838 (client:354) INFO: {'Role': 'Client #6', 'Round': 21, 'Results_raw': {'train_loss': 18.703017, 'val_loss': 19.33617, 'test_loss': 19.145696}} 2024-11-14 15:12:14,881 (client:354) INFO: {'Role': 'Client #9', 'Round': 21, 'Results_raw': {'train_loss': 26.284618, 'val_loss': 29.636445, 'test_loss': 25.028246}} 2024-11-14 15:13:02,729 (client:354) INFO: {'Role': 'Client #4', 'Round': 21, 'Results_raw': {'train_loss': 25.607978, 'val_loss': 21.842961, 'test_loss': 22.135377}} 2024-11-14 15:13:46,749 (client:354) INFO: {'Role': 'Client #1', 'Round': 21, 'Results_raw': {'train_loss': 32.709081, 'val_loss': 30.374558, 'test_loss': 29.98256}} 2024-11-14 15:14:32,672 (client:354) INFO: {'Role': 'Client #7', 'Round': 21, 'Results_raw': {'train_loss': 19.991638, 'val_loss': 19.609841, 'test_loss': 18.393958}} 2024-11-14 15:15:18,532 (client:354) INFO: {'Role': 'Client #2', 'Round': 21, 'Results_raw': {'train_loss': 24.428079, 'val_loss': 25.39134, 'test_loss': 24.532752}} 2024-11-14 15:16:04,761 (client:354) INFO: {'Role': 'Client #10', 'Round': 21, 'Results_raw': {'train_loss': 23.325322, 'val_loss': 22.352588, 'test_loss': 22.346446}} 2024-11-14 15:16:50,826 (client:354) INFO: {'Role': 'Client #8', 'Round': 21, 'Results_raw': {'train_loss': 23.990312, 'val_loss': 25.543821, 'test_loss': 22.39754}} 2024-11-14 15:16:50,830 (server:615) INFO: {'Role': 'Server #', 'Round': 20, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.336696), 'test_loss': np.float64(106785.170197), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.984656), 'val_loss': np.float64(112585.989685), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.336696), 'test_loss': np.float64(106785.170197), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.984656), 'val_loss': np.float64(112585.989685), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.189038), 'test_avg_loss_bottom_decile': np.float64(25.752958), 'test_avg_loss_top_decile': np.float64(39.966807), 'test_avg_loss_min': np.float64(24.505089), 'test_avg_loss_max': np.float64(39.966807), 'test_avg_loss_bottom10%': np.float64(24.505089), 'test_avg_loss_top10%': np.float64(39.966807), 'test_avg_loss_cos1': np.float64(0.9906), 'test_avg_loss_entropy': np.float64(2.293309), 'test_loss_std': np.float64(14745.412431), 'test_loss_bottom_decile': np.float64(90650.413635), 'test_loss_top_decile': np.float64(140683.160522), 'test_loss_min': np.float64(86257.91156), 'test_loss_max': np.float64(140683.160522), 'test_loss_bottom10%': np.float64(86257.91156), 'test_loss_top10%': np.float64(140683.160522), 'test_loss_cos1': np.float64(0.9906), 'test_loss_entropy': np.float64(2.293309), 'val_avg_loss_std': np.float64(4.29864), 'val_avg_loss_bottom_decile': np.float64(27.197312), 'val_avg_loss_top_decile': np.float64(41.087008), 'val_avg_loss_min': np.float64(26.078828), 'val_avg_loss_max': np.float64(41.087008), 'val_avg_loss_bottom10%': np.float64(26.078828), 'val_avg_loss_top10%': np.float64(41.087008), 'val_avg_loss_cos1': np.float64(0.991089), 'val_avg_loss_entropy': np.float64(2.293739), 'val_loss_std': np.float64(15131.211898), 'val_loss_bottom_decile': np.float64(95734.538574), 'val_loss_top_decile': np.float64(144626.269775), 'val_loss_min': np.float64(91797.474792), 'val_loss_max': np.float64(144626.269775), 'val_loss_bottom10%': np.float64(91797.474792), 'val_loss_top10%': np.float64(144626.269775), 'val_loss_cos1': np.float64(0.991089), 'val_loss_entropy': np.float64(2.293739)}} 2024-11-14 15:16:50,868 (server:353) INFO: Server: Starting evaluation at the end of round 21. 2024-11-14 15:16:50,868 (server:359) INFO: ----------- Starting a new training round (Round #22) ------------- 2024-11-14 15:19:03,292 (client:354) INFO: {'Role': 'Client #4', 'Round': 22, 'Results_raw': {'train_loss': 25.486575, 'val_loss': 22.337514, 'test_loss': 22.845319}} 2024-11-14 15:19:50,766 (client:354) INFO: {'Role': 'Client #8', 'Round': 22, 'Results_raw': {'train_loss': 23.953261, 'val_loss': 25.62434, 'test_loss': 22.308978}} 2024-11-14 15:20:38,105 (client:354) INFO: {'Role': 'Client #2', 'Round': 22, 'Results_raw': {'train_loss': 24.328136, 'val_loss': 25.454576, 'test_loss': 24.52854}} 2024-11-14 15:21:24,158 (client:354) INFO: {'Role': 'Client #6', 'Round': 22, 'Results_raw': {'train_loss': 18.670394, 'val_loss': 19.39632, 'test_loss': 19.715931}} 2024-11-14 15:22:10,265 (client:354) INFO: {'Role': 'Client #3', 'Round': 22, 'Results_raw': {'train_loss': 27.314874, 'val_loss': 26.055903, 'test_loss': 26.159579}} 2024-11-14 15:22:55,468 (client:354) INFO: {'Role': 'Client #10', 'Round': 22, 'Results_raw': {'train_loss': 23.274242, 'val_loss': 22.212975, 'test_loss': 22.101201}} 2024-11-14 15:23:39,477 (client:354) INFO: {'Role': 'Client #1', 'Round': 22, 'Results_raw': {'train_loss': 32.54047, 'val_loss': 30.415967, 'test_loss': 30.143354}} 2024-11-14 15:24:21,418 (client:354) INFO: {'Role': 'Client #9', 'Round': 22, 'Results_raw': {'train_loss': 26.148984, 'val_loss': 29.684326, 'test_loss': 25.245975}} 2024-11-14 15:25:05,439 (client:354) INFO: {'Role': 'Client #7', 'Round': 22, 'Results_raw': {'train_loss': 19.920487, 'val_loss': 19.411854, 'test_loss': 18.211276}} 2024-11-14 15:25:48,352 (client:354) INFO: {'Role': 'Client #5', 'Round': 22, 'Results_raw': {'train_loss': 21.092124, 'val_loss': 20.061119, 'test_loss': 22.011561}} 2024-11-14 15:25:48,355 (server:615) INFO: {'Role': 'Server #', 'Round': 21, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.476303), 'test_loss': np.float64(107276.586841), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.104222), 'val_loss': np.float64(113006.861877), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.476303), 'test_loss': np.float64(107276.586841), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(32.104222), 'val_loss': np.float64(113006.861877), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.309235), 'test_avg_loss_bottom_decile': np.float64(25.777525), 'test_avg_loss_top_decile': np.float64(40.314829), 'test_avg_loss_min': np.float64(24.501549), 'test_avg_loss_max': np.float64(40.314829), 'test_avg_loss_bottom10%': np.float64(24.501549), 'test_avg_loss_top10%': np.float64(40.314829), 'test_avg_loss_cos1': np.float64(0.990151), 'test_avg_loss_entropy': np.float64(2.292852), 'test_loss_std': np.float64(15168.507338), 'test_loss_bottom_decile': np.float64(90736.887146), 'test_loss_top_decile': np.float64(141908.198486), 'test_loss_min': np.float64(86245.45105), 'test_loss_max': np.float64(141908.198486), 'test_loss_bottom10%': np.float64(86245.45105), 'test_loss_top10%': np.float64(141908.198486), 'test_loss_cos1': np.float64(0.990151), 'test_loss_entropy': np.float64(2.292852), 'val_avg_loss_std': np.float64(4.452219), 'val_avg_loss_bottom_decile': np.float64(27.167514), 'val_avg_loss_top_decile': np.float64(41.428479), 'val_avg_loss_min': np.float64(26.034972), 'val_avg_loss_max': np.float64(41.428479), 'val_avg_loss_bottom10%': np.float64(26.034972), 'val_avg_loss_top10%': np.float64(41.428479), 'val_avg_loss_cos1': np.float64(0.99052), 'val_avg_loss_entropy': np.float64(2.293161), 'val_loss_std': np.float64(15671.811418), 'val_loss_bottom_decile': np.float64(95629.64801), 'val_loss_top_decile': np.float64(145828.244385), 'val_loss_min': np.float64(91643.102539), 'val_loss_max': np.float64(145828.244385), 'val_loss_bottom10%': np.float64(91643.102539), 'val_loss_top10%': np.float64(145828.244385), 'val_loss_cos1': np.float64(0.99052), 'val_loss_entropy': np.float64(2.293161)}} 2024-11-14 15:25:48,391 (server:353) INFO: Server: Starting evaluation at the end of round 22. 2024-11-14 15:25:48,392 (server:359) INFO: ----------- Starting a new training round (Round #23) ------------- 2024-11-14 15:27:58,545 (client:354) INFO: {'Role': 'Client #5', 'Round': 23, 'Results_raw': {'train_loss': 20.928128, 'val_loss': 20.549023, 'test_loss': 22.132405}} 2024-11-14 15:28:45,943 (client:354) INFO: {'Role': 'Client #6', 'Round': 23, 'Results_raw': {'train_loss': 18.617327, 'val_loss': 19.653867, 'test_loss': 20.088281}} 2024-11-14 15:29:30,706 (client:354) INFO: {'Role': 'Client #9', 'Round': 23, 'Results_raw': {'train_loss': 26.146001, 'val_loss': 30.199659, 'test_loss': 25.036476}} 2024-11-14 15:30:15,232 (client:354) INFO: {'Role': 'Client #1', 'Round': 23, 'Results_raw': {'train_loss': 32.392153, 'val_loss': 30.40481, 'test_loss': 30.187721}} 2024-11-14 15:31:00,377 (client:354) INFO: {'Role': 'Client #8', 'Round': 23, 'Results_raw': {'train_loss': 23.845845, 'val_loss': 25.668048, 'test_loss': 22.236308}} 2024-11-14 15:31:45,872 (client:354) INFO: {'Role': 'Client #3', 'Round': 23, 'Results_raw': {'train_loss': 27.212491, 'val_loss': 26.124789, 'test_loss': 26.262912}} 2024-11-14 15:32:31,884 (client:354) INFO: {'Role': 'Client #4', 'Round': 23, 'Results_raw': {'train_loss': 25.454024, 'val_loss': 21.869724, 'test_loss': 22.405964}} 2024-11-14 15:33:19,121 (client:354) INFO: {'Role': 'Client #7', 'Round': 23, 'Results_raw': {'train_loss': 19.947492, 'val_loss': 19.601106, 'test_loss': 18.221607}} 2024-11-14 15:34:05,085 (client:354) INFO: {'Role': 'Client #10', 'Round': 23, 'Results_raw': {'train_loss': 23.253355, 'val_loss': 22.184821, 'test_loss': 22.415624}} 2024-11-14 15:34:50,668 (client:354) INFO: {'Role': 'Client #2', 'Round': 23, 'Results_raw': {'train_loss': 24.239656, 'val_loss': 25.295732, 'test_loss': 24.443318}} 2024-11-14 15:34:50,673 (server:615) INFO: {'Role': 'Server #', 'Round': 22, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.264561), 'test_loss': np.float64(106531.256055), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.891336), 'val_loss': np.float64(112257.50271), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.264561), 'test_loss': np.float64(106531.256055), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.891336), 'val_loss': np.float64(112257.50271), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.10685), 'test_avg_loss_bottom_decile': np.float64(25.916164), 'test_avg_loss_top_decile': np.float64(39.711297), 'test_avg_loss_min': np.float64(24.562035), 'test_avg_loss_max': np.float64(39.711297), 'test_avg_loss_bottom10%': np.float64(24.562035), 'test_avg_loss_top10%': np.float64(39.711297), 'test_avg_loss_cos1': np.float64(0.990918), 'test_avg_loss_entropy': np.float64(2.293628), 'test_loss_std': np.float64(14456.111752), 'test_loss_bottom_decile': np.float64(91224.896423), 'test_loss_top_decile': np.float64(139783.765747), 'test_loss_min': np.float64(86458.362122), 'test_loss_max': np.float64(139783.765747), 'test_loss_bottom10%': np.float64(86458.362122), 'test_loss_top10%': np.float64(139783.765747), 'test_loss_cos1': np.float64(0.990918), 'test_loss_entropy': np.float64(2.293628), 'val_avg_loss_std': np.float64(4.224712), 'val_avg_loss_bottom_decile': np.float64(27.322932), 'val_avg_loss_top_decile': np.float64(40.801536), 'val_avg_loss_min': np.float64(26.077895), 'val_avg_loss_max': np.float64(40.801536), 'val_avg_loss_bottom10%': np.float64(26.077895), 'val_avg_loss_top10%': np.float64(40.801536), 'val_avg_loss_cos1': np.float64(0.991339), 'val_avg_loss_entropy': np.float64(2.293986), 'val_loss_std': np.float64(14870.987991), 'val_loss_bottom_decile': np.float64(96176.720215), 'val_loss_top_decile': np.float64(143621.405151), 'val_loss_min': np.float64(91794.190186), 'val_loss_max': np.float64(143621.405151), 'val_loss_bottom10%': np.float64(91794.190186), 'val_loss_top10%': np.float64(143621.405151), 'val_loss_cos1': np.float64(0.991339), 'val_loss_entropy': np.float64(2.293986)}} 2024-11-14 15:34:50,717 (server:353) INFO: Server: Starting evaluation at the end of round 23. 2024-11-14 15:34:50,718 (server:359) INFO: ----------- Starting a new training round (Round #24) ------------- 2024-11-14 15:37:01,750 (client:354) INFO: {'Role': 'Client #4', 'Round': 24, 'Results_raw': {'train_loss': 25.404099, 'val_loss': 21.916178, 'test_loss': 22.357508}} 2024-11-14 15:37:46,729 (client:354) INFO: {'Role': 'Client #2', 'Round': 24, 'Results_raw': {'train_loss': 24.260094, 'val_loss': 25.601551, 'test_loss': 24.648413}} 2024-11-14 15:38:33,339 (client:354) INFO: {'Role': 'Client #10', 'Round': 24, 'Results_raw': {'train_loss': 23.290451, 'val_loss': 22.042335, 'test_loss': 22.162228}} 2024-11-14 15:39:19,316 (client:354) INFO: {'Role': 'Client #6', 'Round': 24, 'Results_raw': {'train_loss': 18.669486, 'val_loss': 19.54962, 'test_loss': 20.116874}} 2024-11-14 15:40:05,107 (client:354) INFO: {'Role': 'Client #9', 'Round': 24, 'Results_raw': {'train_loss': 25.922828, 'val_loss': 30.014058, 'test_loss': 24.981156}} 2024-11-14 15:40:50,594 (client:354) INFO: {'Role': 'Client #8', 'Round': 24, 'Results_raw': {'train_loss': 23.801667, 'val_loss': 26.070977, 'test_loss': 22.353502}} 2024-11-14 15:41:36,967 (client:354) INFO: {'Role': 'Client #3', 'Round': 24, 'Results_raw': {'train_loss': 27.231525, 'val_loss': 26.148022, 'test_loss': 26.298994}} 2024-11-14 15:42:22,316 (client:354) INFO: {'Role': 'Client #7', 'Round': 24, 'Results_raw': {'train_loss': 19.902858, 'val_loss': 19.648819, 'test_loss': 18.210732}} 2024-11-14 15:43:11,513 (client:354) INFO: {'Role': 'Client #1', 'Round': 24, 'Results_raw': {'train_loss': 32.481753, 'val_loss': 30.469479, 'test_loss': 30.409937}} 2024-11-14 15:43:58,132 (client:354) INFO: {'Role': 'Client #5', 'Round': 24, 'Results_raw': {'train_loss': 20.9902, 'val_loss': 20.352767, 'test_loss': 22.42527}} 2024-11-14 15:43:58,137 (server:615) INFO: {'Role': 'Server #', 'Round': 23, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.210525), 'test_loss': np.float64(106341.047711), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.837406), 'val_loss': np.float64(112067.668146), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.210525), 'test_loss': np.float64(106341.047711), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.837406), 'val_loss': np.float64(112067.668146), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.17766), 'test_avg_loss_bottom_decile': np.float64(25.759721), 'test_avg_loss_top_decile': np.float64(39.814427), 'test_avg_loss_min': np.float64(24.506532), 'test_avg_loss_max': np.float64(39.814427), 'test_avg_loss_bottom10%': np.float64(24.506532), 'test_avg_loss_top10%': np.float64(39.814427), 'test_avg_loss_cos1': np.float64(0.990574), 'test_avg_loss_entropy': np.float64(2.293292), 'test_loss_std': np.float64(14705.364938), 'test_loss_bottom_decile': np.float64(90674.217346), 'test_loss_top_decile': np.float64(140146.782471), 'test_loss_min': np.float64(86262.993591), 'test_loss_max': np.float64(140146.782471), 'test_loss_bottom10%': np.float64(86262.993591), 'test_loss_top10%': np.float64(140146.782471), 'test_loss_cos1': np.float64(0.990574), 'test_loss_entropy': np.float64(2.293292), 'val_avg_loss_std': np.float64(4.307024), 'val_avg_loss_bottom_decile': np.float64(27.173527), 'val_avg_loss_top_decile': np.float64(40.902947), 'val_avg_loss_min': np.float64(26.003831), 'val_avg_loss_max': np.float64(40.902947), 'val_avg_loss_bottom10%': np.float64(26.003831), 'val_avg_loss_top10%': np.float64(40.902947), 'val_avg_loss_cos1': np.float64(0.990973), 'val_avg_loss_entropy': np.float64(2.293628), 'val_loss_std': np.float64(15160.724922), 'val_loss_bottom_decile': np.float64(95650.815552), 'val_loss_top_decile': np.float64(143978.372437), 'val_loss_min': np.float64(91533.486633), 'val_loss_max': np.float64(143978.372437), 'val_loss_bottom10%': np.float64(91533.486633), 'val_loss_top10%': np.float64(143978.372437), 'val_loss_cos1': np.float64(0.990973), 'val_loss_entropy': np.float64(2.293628)}} 2024-11-14 15:43:58,192 (server:353) INFO: Server: Starting evaluation at the end of round 24. 2024-11-14 15:43:58,192 (server:359) INFO: ----------- Starting a new training round (Round #25) ------------- 2024-11-14 15:46:05,241 (client:354) INFO: {'Role': 'Client #5', 'Round': 25, 'Results_raw': {'train_loss': 20.889008, 'val_loss': 20.00704, 'test_loss': 22.000778}} 2024-11-14 15:46:50,040 (client:354) INFO: {'Role': 'Client #2', 'Round': 25, 'Results_raw': {'train_loss': 24.170914, 'val_loss': 25.319673, 'test_loss': 24.458419}} 2024-11-14 15:47:35,250 (client:354) INFO: {'Role': 'Client #4', 'Round': 25, 'Results_raw': {'train_loss': 25.295157, 'val_loss': 21.979028, 'test_loss': 22.90208}} 2024-11-14 15:48:20,056 (client:354) INFO: {'Role': 'Client #8', 'Round': 25, 'Results_raw': {'train_loss': 23.749955, 'val_loss': 25.773711, 'test_loss': 22.131032}} 2024-11-14 15:49:07,734 (client:354) INFO: {'Role': 'Client #10', 'Round': 25, 'Results_raw': {'train_loss': 23.154155, 'val_loss': 22.166287, 'test_loss': 22.373671}} 2024-11-14 15:49:53,081 (client:354) INFO: {'Role': 'Client #3', 'Round': 25, 'Results_raw': {'train_loss': 27.174496, 'val_loss': 26.044697, 'test_loss': 26.04558}} 2024-11-14 15:50:37,747 (client:354) INFO: {'Role': 'Client #9', 'Round': 25, 'Results_raw': {'train_loss': 25.954305, 'val_loss': 29.418958, 'test_loss': 24.87705}} 2024-11-14 15:51:22,659 (client:354) INFO: {'Role': 'Client #1', 'Round': 25, 'Results_raw': {'train_loss': 32.301255, 'val_loss': 30.536004, 'test_loss': 29.993522}} 2024-11-14 15:52:04,875 (client:354) INFO: {'Role': 'Client #6', 'Round': 25, 'Results_raw': {'train_loss': 18.529071, 'val_loss': 19.594757, 'test_loss': 20.400495}} 2024-11-14 15:52:47,541 (client:354) INFO: {'Role': 'Client #7', 'Round': 25, 'Results_raw': {'train_loss': 19.922224, 'val_loss': 19.424217, 'test_loss': 18.063656}} 2024-11-14 15:52:47,553 (server:615) INFO: {'Role': 'Server #', 'Round': 24, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.304714), 'test_loss': np.float64(106672.592194), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.92299), 'val_loss': np.float64(112368.924133), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.304714), 'test_loss': np.float64(106672.592194), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.92299), 'val_loss': np.float64(112368.924133), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.338424), 'test_avg_loss_bottom_decile': np.float64(25.628128), 'test_avg_loss_top_decile': np.float64(40.318816), 'test_avg_loss_min': np.float64(24.288019), 'test_avg_loss_max': np.float64(40.318816), 'test_avg_loss_bottom10%': np.float64(24.288019), 'test_avg_loss_top10%': np.float64(40.318816), 'test_avg_loss_cos1': np.float64(0.989907), 'test_avg_loss_entropy': np.float64(2.292633), 'test_loss_std': np.float64(15271.252785), 'test_loss_bottom_decile': np.float64(90211.01062), 'test_loss_top_decile': np.float64(141922.230957), 'test_loss_min': np.float64(85493.827515), 'test_loss_max': np.float64(141922.230957), 'test_loss_bottom10%': np.float64(85493.827515), 'test_loss_top10%': np.float64(141922.230957), 'test_loss_cos1': np.float64(0.989907), 'test_loss_entropy': np.float64(2.292633), 'val_avg_loss_std': np.float64(4.466025), 'val_avg_loss_bottom_decile': np.float64(27.081992), 'val_avg_loss_top_decile': np.float64(41.432987), 'val_avg_loss_min': np.float64(25.763131), 'val_avg_loss_max': np.float64(41.432987), 'val_avg_loss_bottom10%': np.float64(25.763131), 'val_avg_loss_top10%': np.float64(41.432987), 'val_avg_loss_cos1': np.float64(0.990355), 'val_avg_loss_entropy': np.float64(2.293011), 'val_loss_std': np.float64(15720.408448), 'val_loss_bottom_decile': np.float64(95328.612854), 'val_loss_top_decile': np.float64(145844.114502), 'val_loss_min': np.float64(90686.22052), 'val_loss_max': np.float64(145844.114502), 'val_loss_bottom10%': np.float64(90686.22052), 'val_loss_top10%': np.float64(145844.114502), 'val_loss_cos1': np.float64(0.990355), 'val_loss_entropy': np.float64(2.293011)}} 2024-11-14 15:52:47,589 (server:353) INFO: Server: Starting evaluation at the end of round 25. 2024-11-14 15:52:47,590 (server:359) INFO: ----------- Starting a new training round (Round #26) ------------- 2024-11-14 15:54:59,535 (client:354) INFO: {'Role': 'Client #8', 'Round': 26, 'Results_raw': {'train_loss': 23.609923, 'val_loss': 25.554499, 'test_loss': 22.027021}} 2024-11-14 15:55:42,433 (client:354) INFO: {'Role': 'Client #9', 'Round': 26, 'Results_raw': {'train_loss': 25.946661, 'val_loss': 29.527078, 'test_loss': 25.342944}} 2024-11-14 15:56:26,393 (client:354) INFO: {'Role': 'Client #7', 'Round': 26, 'Results_raw': {'train_loss': 19.875789, 'val_loss': 19.661117, 'test_loss': 18.146896}} 2024-11-14 15:57:10,831 (client:354) INFO: {'Role': 'Client #10', 'Round': 26, 'Results_raw': {'train_loss': 23.054361, 'val_loss': 21.880056, 'test_loss': 22.008956}} 2024-11-14 15:57:56,300 (client:354) INFO: {'Role': 'Client #2', 'Round': 26, 'Results_raw': {'train_loss': 24.140746, 'val_loss': 25.335964, 'test_loss': 24.364861}} 2024-11-14 15:58:39,719 (client:354) INFO: {'Role': 'Client #5', 'Round': 26, 'Results_raw': {'train_loss': 20.87289, 'val_loss': 20.10055, 'test_loss': 21.294835}} 2024-11-14 15:59:23,000 (client:354) INFO: {'Role': 'Client #1', 'Round': 26, 'Results_raw': {'train_loss': 32.360549, 'val_loss': 31.000759, 'test_loss': 30.224428}} 2024-11-14 16:00:06,505 (client:354) INFO: {'Role': 'Client #6', 'Round': 26, 'Results_raw': {'train_loss': 18.6229, 'val_loss': 19.265568, 'test_loss': 19.846549}} 2024-11-14 16:00:48,112 (client:354) INFO: {'Role': 'Client #4', 'Round': 26, 'Results_raw': {'train_loss': 25.255138, 'val_loss': 21.807602, 'test_loss': 22.323338}} 2024-11-14 16:01:31,475 (client:354) INFO: {'Role': 'Client #3', 'Round': 26, 'Results_raw': {'train_loss': 27.123384, 'val_loss': 26.187415, 'test_loss': 26.419603}} 2024-11-14 16:01:31,478 (server:615) INFO: {'Role': 'Server #', 'Round': 25, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.269061), 'test_loss': np.float64(106547.096307), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.905084), 'val_loss': np.float64(112305.894287), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.269061), 'test_loss': np.float64(106547.096307), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.905084), 'val_loss': np.float64(112305.894287), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.167452), 'test_avg_loss_bottom_decile': np.float64(25.833885), 'test_avg_loss_top_decile': np.float64(39.91212), 'test_avg_loss_min': np.float64(24.5972), 'test_avg_loss_max': np.float64(39.91212), 'test_avg_loss_bottom10%': np.float64(24.5972), 'test_avg_loss_top10%': np.float64(39.91212), 'test_avg_loss_cos1': np.float64(0.990655), 'test_avg_loss_entropy': np.float64(2.293387), 'test_loss_std': np.float64(14669.429841), 'test_loss_bottom_decile': np.float64(90935.27356), 'test_loss_top_decile': np.float64(140490.661987), 'test_loss_min': np.float64(86582.145569), 'test_loss_max': np.float64(140490.661987), 'test_loss_bottom10%': np.float64(86582.145569), 'test_loss_top10%': np.float64(140490.661987), 'test_loss_cos1': np.float64(0.990655), 'test_loss_entropy': np.float64(2.293387), 'val_avg_loss_std': np.float64(4.307941), 'val_avg_loss_bottom_decile': np.float64(27.261746), 'val_avg_loss_top_decile': np.float64(40.984285), 'val_avg_loss_min': np.float64(26.092116), 'val_avg_loss_max': np.float64(40.984285), 'val_avg_loss_bottom10%': np.float64(26.092116), 'val_avg_loss_top10%': np.float64(40.984285), 'val_avg_loss_cos1': np.float64(0.991007), 'val_avg_loss_entropy': np.float64(2.293668), 'val_loss_std': np.float64(15163.950916), 'val_loss_bottom_decile': np.float64(95961.346497), 'val_loss_top_decile': np.float64(144264.682007), 'val_loss_min': np.float64(91844.250061), 'val_loss_max': np.float64(144264.682007), 'val_loss_bottom10%': np.float64(91844.250061), 'val_loss_top10%': np.float64(144264.682007), 'val_loss_cos1': np.float64(0.991007), 'val_loss_entropy': np.float64(2.293668)}} 2024-11-14 16:01:31,521 (server:353) INFO: Server: Starting evaluation at the end of round 26. 2024-11-14 16:01:31,521 (server:359) INFO: ----------- Starting a new training round (Round #27) ------------- 2024-11-14 16:03:45,900 (client:354) INFO: {'Role': 'Client #9', 'Round': 27, 'Results_raw': {'train_loss': 25.840595, 'val_loss': 29.463672, 'test_loss': 24.915179}} 2024-11-14 16:04:30,653 (client:354) INFO: {'Role': 'Client #8', 'Round': 27, 'Results_raw': {'train_loss': 23.569688, 'val_loss': 26.162822, 'test_loss': 22.199689}} 2024-11-14 16:05:14,894 (client:354) INFO: {'Role': 'Client #4', 'Round': 27, 'Results_raw': {'train_loss': 25.27079, 'val_loss': 21.698192, 'test_loss': 22.426099}} 2024-11-14 16:05:58,961 (client:354) INFO: {'Role': 'Client #1', 'Round': 27, 'Results_raw': {'train_loss': 32.346458, 'val_loss': 30.890037, 'test_loss': 30.243176}} 2024-11-14 16:06:42,363 (client:354) INFO: {'Role': 'Client #2', 'Round': 27, 'Results_raw': {'train_loss': 24.05099, 'val_loss': 25.166521, 'test_loss': 24.153192}} 2024-11-14 16:07:25,629 (client:354) INFO: {'Role': 'Client #3', 'Round': 27, 'Results_raw': {'train_loss': 27.081617, 'val_loss': 26.045637, 'test_loss': 25.976548}} 2024-11-14 16:08:08,856 (client:354) INFO: {'Role': 'Client #7', 'Round': 27, 'Results_raw': {'train_loss': 19.783588, 'val_loss': 19.543346, 'test_loss': 18.193482}} 2024-11-14 16:08:52,673 (client:354) INFO: {'Role': 'Client #10', 'Round': 27, 'Results_raw': {'train_loss': 23.034064, 'val_loss': 22.237252, 'test_loss': 22.359667}} 2024-11-14 16:09:33,407 (client:354) INFO: {'Role': 'Client #6', 'Round': 27, 'Results_raw': {'train_loss': 18.546077, 'val_loss': 19.691053, 'test_loss': 20.088178}} 2024-11-14 16:10:18,266 (client:354) INFO: {'Role': 'Client #5', 'Round': 27, 'Results_raw': {'train_loss': 20.794583, 'val_loss': 20.624385, 'test_loss': 21.595008}} 2024-11-14 16:10:18,271 (server:615) INFO: {'Role': 'Server #', 'Round': 26, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.145934), 'test_loss': np.float64(106113.689349), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.769545), 'val_loss': np.float64(111828.799097), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.145934), 'test_loss': np.float64(106113.689349), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.769545), 'val_loss': np.float64(111828.799097), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.203232), 'test_avg_loss_bottom_decile': np.float64(25.554501), 'test_avg_loss_top_decile': np.float64(39.814508), 'test_avg_loss_min': np.float64(24.304805), 'test_avg_loss_max': np.float64(39.814508), 'test_avg_loss_bottom10%': np.float64(24.304805), 'test_avg_loss_top10%': np.float64(39.814508), 'test_avg_loss_cos1': np.float64(0.990419), 'test_avg_loss_entropy': np.float64(2.293135), 'test_loss_std': np.float64(14795.377724), 'test_loss_bottom_decile': np.float64(89951.844299), 'test_loss_top_decile': np.float64(140147.066406), 'test_loss_min': np.float64(85552.914795), 'test_loss_max': np.float64(140147.066406), 'test_loss_bottom10%': np.float64(85552.914795), 'test_loss_top10%': np.float64(140147.066406), 'test_loss_cos1': np.float64(0.990419), 'test_loss_entropy': np.float64(2.293135), 'val_avg_loss_std': np.float64(4.346071), 'val_avg_loss_bottom_decile': np.float64(27.039629), 'val_avg_loss_top_decile': np.float64(40.89715), 'val_avg_loss_min': np.float64(25.795634), 'val_avg_loss_max': np.float64(40.89715), 'val_avg_loss_bottom10%': np.float64(25.795634), 'val_avg_loss_top10%': np.float64(40.89715), 'val_avg_loss_cos1': np.float64(0.990772), 'val_avg_loss_entropy': np.float64(2.293421), 'val_loss_std': np.float64(15298.168694), 'val_loss_bottom_decile': np.float64(95179.495667), 'val_loss_top_decile': np.float64(143957.969727), 'val_loss_min': np.float64(90800.632141), 'val_loss_max': np.float64(143957.969727), 'val_loss_bottom10%': np.float64(90800.632141), 'val_loss_top10%': np.float64(143957.969727), 'val_loss_cos1': np.float64(0.990772), 'val_loss_entropy': np.float64(2.293421)}} 2024-11-14 16:10:18,308 (server:353) INFO: Server: Starting evaluation at the end of round 27. 2024-11-14 16:10:18,308 (server:359) INFO: ----------- Starting a new training round (Round #28) ------------- 2024-11-14 16:12:31,587 (client:354) INFO: {'Role': 'Client #8', 'Round': 28, 'Results_raw': {'train_loss': 23.534123, 'val_loss': 25.419008, 'test_loss': 22.03581}} 2024-11-14 16:13:18,127 (client:354) INFO: {'Role': 'Client #9', 'Round': 28, 'Results_raw': {'train_loss': 25.847503, 'val_loss': 29.434093, 'test_loss': 24.979503}} 2024-11-14 16:14:04,609 (client:354) INFO: {'Role': 'Client #3', 'Round': 28, 'Results_raw': {'train_loss': 26.959542, 'val_loss': 26.123457, 'test_loss': 26.110483}} 2024-11-14 16:14:51,098 (client:354) INFO: {'Role': 'Client #4', 'Round': 28, 'Results_raw': {'train_loss': 25.312803, 'val_loss': 21.879736, 'test_loss': 22.656972}} 2024-11-14 16:15:38,223 (client:354) INFO: {'Role': 'Client #6', 'Round': 28, 'Results_raw': {'train_loss': 18.480989, 'val_loss': 19.469287, 'test_loss': 19.51381}} 2024-11-14 16:16:24,193 (client:354) INFO: {'Role': 'Client #1', 'Round': 28, 'Results_raw': {'train_loss': 32.283241, 'val_loss': 30.236964, 'test_loss': 29.878234}} 2024-11-14 16:17:10,015 (client:354) INFO: {'Role': 'Client #2', 'Round': 28, 'Results_raw': {'train_loss': 24.00947, 'val_loss': 25.084876, 'test_loss': 24.155201}} 2024-11-14 16:17:53,512 (client:354) INFO: {'Role': 'Client #10', 'Round': 28, 'Results_raw': {'train_loss': 23.088386, 'val_loss': 22.622145, 'test_loss': 22.64221}} 2024-11-14 16:18:33,615 (client:354) INFO: {'Role': 'Client #7', 'Round': 28, 'Results_raw': {'train_loss': 19.777882, 'val_loss': 19.204563, 'test_loss': 17.926311}} 2024-11-14 16:19:16,049 (client:354) INFO: {'Role': 'Client #5', 'Round': 28, 'Results_raw': {'train_loss': 20.729645, 'val_loss': 20.609196, 'test_loss': 22.074102}} 2024-11-14 16:19:16,052 (server:615) INFO: {'Role': 'Server #', 'Round': 27, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.244911), 'test_loss': np.float64(106462.085852), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.83926), 'val_loss': np.float64(112074.196112), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.244911), 'test_loss': np.float64(106462.085852), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.83926), 'val_loss': np.float64(112074.196112), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.109285), 'test_avg_loss_bottom_decile': np.float64(26.177639), 'test_avg_loss_top_decile': np.float64(39.776025), 'test_avg_loss_min': np.float64(24.573035), 'test_avg_loss_max': np.float64(39.776025), 'test_avg_loss_bottom10%': np.float64(24.573035), 'test_avg_loss_top10%': np.float64(39.776025), 'test_avg_loss_cos1': np.float64(0.990896), 'test_avg_loss_entropy': np.float64(2.293633), 'test_loss_std': np.float64(14464.681842), 'test_loss_bottom_decile': np.float64(92145.288147), 'test_loss_top_decile': np.float64(140011.6073), 'test_loss_min': np.float64(86497.08313), 'test_loss_max': np.float64(140011.6073), 'test_loss_bottom10%': np.float64(86497.08313), 'test_loss_top10%': np.float64(140011.6073), 'test_loss_cos1': np.float64(0.990896), 'test_loss_entropy': np.float64(2.293633), 'val_avg_loss_std': np.float64(4.255417), 'val_avg_loss_bottom_decile': np.float64(27.663017), 'val_avg_loss_top_decile': np.float64(40.863694), 'val_avg_loss_min': np.float64(26.052058), 'val_avg_loss_max': np.float64(40.863694), 'val_avg_loss_bottom10%': np.float64(26.052058), 'val_avg_loss_top10%': np.float64(40.863694), 'val_avg_loss_cos1': np.float64(0.991186), 'val_avg_loss_entropy': np.float64(2.293861), 'val_loss_std': np.float64(14979.067322), 'val_loss_bottom_decile': np.float64(97373.821289), 'val_loss_top_decile': np.float64(143840.20459), 'val_loss_min': np.float64(91703.245422), 'val_loss_max': np.float64(143840.20459), 'val_loss_bottom10%': np.float64(91703.245422), 'val_loss_top10%': np.float64(143840.20459), 'val_loss_cos1': np.float64(0.991186), 'val_loss_entropy': np.float64(2.293861)}} 2024-11-14 16:19:16,085 (server:353) INFO: Server: Starting evaluation at the end of round 28. 2024-11-14 16:19:16,086 (server:359) INFO: ----------- Starting a new training round (Round #29) ------------- 2024-11-14 16:21:29,391 (client:354) INFO: {'Role': 'Client #1', 'Round': 29, 'Results_raw': {'train_loss': 32.145909, 'val_loss': 30.684439, 'test_loss': 30.387209}} 2024-11-14 16:22:14,002 (client:354) INFO: {'Role': 'Client #5', 'Round': 29, 'Results_raw': {'train_loss': 20.701458, 'val_loss': 20.41609, 'test_loss': 21.3905}} 2024-11-14 16:22:58,435 (client:354) INFO: {'Role': 'Client #7', 'Round': 29, 'Results_raw': {'train_loss': 19.779237, 'val_loss': 19.309537, 'test_loss': 17.932294}} 2024-11-14 16:23:43,495 (client:354) INFO: {'Role': 'Client #6', 'Round': 29, 'Results_raw': {'train_loss': 18.453763, 'val_loss': 19.361526, 'test_loss': 19.807142}} 2024-11-14 16:24:28,625 (client:354) INFO: {'Role': 'Client #3', 'Round': 29, 'Results_raw': {'train_loss': 26.970433, 'val_loss': 25.921643, 'test_loss': 26.081297}} 2024-11-14 16:25:12,454 (client:354) INFO: {'Role': 'Client #4', 'Round': 29, 'Results_raw': {'train_loss': 25.149187, 'val_loss': 21.555667, 'test_loss': 22.164765}} 2024-11-14 16:25:55,600 (client:354) INFO: {'Role': 'Client #9', 'Round': 29, 'Results_raw': {'train_loss': 25.788592, 'val_loss': 29.631004, 'test_loss': 24.97704}} 2024-11-14 16:26:36,388 (client:354) INFO: {'Role': 'Client #10', 'Round': 29, 'Results_raw': {'train_loss': 23.00547, 'val_loss': 21.942537, 'test_loss': 22.141598}} 2024-11-14 16:27:17,904 (client:354) INFO: {'Role': 'Client #2', 'Round': 29, 'Results_raw': {'train_loss': 23.992782, 'val_loss': 25.331512, 'test_loss': 24.32382}} 2024-11-14 16:27:59,867 (client:354) INFO: {'Role': 'Client #8', 'Round': 29, 'Results_raw': {'train_loss': 23.486435, 'val_loss': 26.682634, 'test_loss': 23.20398}} 2024-11-14 16:27:59,870 (server:615) INFO: {'Role': 'Server #', 'Round': 28, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.214775), 'test_loss': np.float64(106356.007812), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.820214), 'val_loss': np.float64(112007.152887), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.214775), 'test_loss': np.float64(106356.007812), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.820214), 'val_loss': np.float64(112007.152887), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.321329), 'test_avg_loss_bottom_decile': np.float64(25.42633), 'test_avg_loss_top_decile': np.float64(40.32234), 'test_avg_loss_min': np.float64(24.36103), 'test_avg_loss_max': np.float64(40.32234), 'test_avg_loss_bottom10%': np.float64(24.36103), 'test_avg_loss_top10%': np.float64(40.32234), 'test_avg_loss_cos1': np.float64(0.989927), 'test_avg_loss_entropy': np.float64(2.292674), 'test_loss_std': np.float64(15211.079167), 'test_loss_bottom_decile': np.float64(89500.680237), 'test_loss_top_decile': np.float64(141934.637329), 'test_loss_min': np.float64(85750.826538), 'test_loss_max': np.float64(141934.637329), 'test_loss_bottom10%': np.float64(85750.826538), 'test_loss_top10%': np.float64(141934.637329), 'test_loss_cos1': np.float64(0.989927), 'test_loss_entropy': np.float64(2.292674), 'val_avg_loss_std': np.float64(4.45505), 'val_avg_loss_bottom_decile': np.float64(26.899697), 'val_avg_loss_top_decile': np.float64(41.367987), 'val_avg_loss_min': np.float64(25.846257), 'val_avg_loss_max': np.float64(41.367987), 'val_avg_loss_bottom10%': np.float64(25.846257), 'val_avg_loss_top10%': np.float64(41.367987), 'val_avg_loss_cos1': np.float64(0.990341), 'val_avg_loss_entropy': np.float64(2.29301), 'val_loss_std': np.float64(15681.775938), 'val_loss_bottom_decile': np.float64(94686.933777), 'val_loss_top_decile': np.float64(145615.313599), 'val_loss_min': np.float64(90978.825134), 'val_loss_max': np.float64(145615.313599), 'val_loss_bottom10%': np.float64(90978.825134), 'val_loss_top10%': np.float64(145615.313599), 'val_loss_cos1': np.float64(0.990341), 'val_loss_entropy': np.float64(2.29301)}} 2024-11-14 16:27:59,906 (server:353) INFO: Server: Starting evaluation at the end of round 29. 2024-11-14 16:27:59,907 (server:359) INFO: ----------- Starting a new training round (Round #30) ------------- 2024-11-14 16:30:12,323 (client:354) INFO: {'Role': 'Client #7', 'Round': 30, 'Results_raw': {'train_loss': 19.731222, 'val_loss': 19.334652, 'test_loss': 17.931137}} 2024-11-14 16:30:54,851 (client:354) INFO: {'Role': 'Client #6', 'Round': 30, 'Results_raw': {'train_loss': 18.484369, 'val_loss': 19.381644, 'test_loss': 20.208296}} 2024-11-14 16:31:36,891 (client:354) INFO: {'Role': 'Client #10', 'Round': 30, 'Results_raw': {'train_loss': 22.901426, 'val_loss': 21.804827, 'test_loss': 22.164387}} 2024-11-14 16:32:18,879 (client:354) INFO: {'Role': 'Client #8', 'Round': 30, 'Results_raw': {'train_loss': 23.420621, 'val_loss': 25.691415, 'test_loss': 21.48577}} 2024-11-14 16:33:00,950 (client:354) INFO: {'Role': 'Client #3', 'Round': 30, 'Results_raw': {'train_loss': 26.950977, 'val_loss': 26.290546, 'test_loss': 26.391408}} 2024-11-14 16:33:42,503 (client:354) INFO: {'Role': 'Client #2', 'Round': 30, 'Results_raw': {'train_loss': 23.974479, 'val_loss': 25.403195, 'test_loss': 24.474446}} 2024-11-14 16:34:23,527 (client:354) INFO: {'Role': 'Client #5', 'Round': 30, 'Results_raw': {'train_loss': 20.733504, 'val_loss': 20.103189, 'test_loss': 22.324758}} 2024-11-14 16:35:03,673 (client:354) INFO: {'Role': 'Client #4', 'Round': 30, 'Results_raw': {'train_loss': 25.166141, 'val_loss': 21.967523, 'test_loss': 22.538343}} 2024-11-14 16:35:44,835 (client:354) INFO: {'Role': 'Client #9', 'Round': 30, 'Results_raw': {'train_loss': 25.685925, 'val_loss': 29.218553, 'test_loss': 24.943413}} 2024-11-14 16:36:26,047 (client:354) INFO: {'Role': 'Client #1', 'Round': 30, 'Results_raw': {'train_loss': 32.199869, 'val_loss': 30.72007, 'test_loss': 30.29965}} 2024-11-14 16:36:26,058 (server:615) INFO: {'Role': 'Server #', 'Round': 29, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.213693), 'test_loss': np.float64(106352.200262), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.808342), 'val_loss': np.float64(111965.364465), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.213693), 'test_loss': np.float64(106352.200262), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.808342), 'val_loss': np.float64(111965.364465), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.278791), 'test_avg_loss_bottom_decile': np.float64(25.33387), 'test_avg_loss_top_decile': np.float64(39.895105), 'test_avg_loss_min': np.float64(24.24257), 'test_avg_loss_max': np.float64(39.895105), 'test_avg_loss_bottom10%': np.float64(24.24257), 'test_avg_loss_top10%': np.float64(39.895105), 'test_avg_loss_cos1': np.float64(0.990121), 'test_avg_loss_entropy': np.float64(2.292807), 'test_loss_std': np.float64(15061.344431), 'test_loss_bottom_decile': np.float64(89175.222595), 'test_loss_top_decile': np.float64(140430.769653), 'test_loss_min': np.float64(85333.845764), 'test_loss_max': np.float64(140430.769653), 'test_loss_bottom10%': np.float64(85333.845764), 'test_loss_top10%': np.float64(140430.769653), 'test_loss_cos1': np.float64(0.990121), 'test_loss_entropy': np.float64(2.292807), 'val_avg_loss_std': np.float64(4.443869), 'val_avg_loss_bottom_decile': np.float64(26.819666), 'val_avg_loss_top_decile': np.float64(40.952083), 'val_avg_loss_min': np.float64(25.718067), 'val_avg_loss_max': np.float64(40.952083), 'val_avg_loss_bottom10%': np.float64(25.718067), 'val_avg_loss_top10%': np.float64(40.952083), 'val_avg_loss_cos1': np.float64(0.990381), 'val_avg_loss_entropy': np.float64(2.293014), 'val_loss_std': np.float64(15642.418693), 'val_loss_bottom_decile': np.float64(94405.225342), 'val_loss_top_decile': np.float64(144151.330688), 'val_loss_min': np.float64(90527.594666), 'val_loss_max': np.float64(144151.330688), 'val_loss_bottom10%': np.float64(90527.594666), 'val_loss_top10%': np.float64(144151.330688), 'val_loss_cos1': np.float64(0.990381), 'val_loss_entropy': np.float64(2.293014)}} 2024-11-14 16:36:26,095 (server:353) INFO: Server: Starting evaluation at the end of round 30. 2024-11-14 16:36:26,095 (server:359) INFO: ----------- Starting a new training round (Round #31) ------------- 2024-11-14 16:38:36,696 (client:354) INFO: {'Role': 'Client #4', 'Round': 31, 'Results_raw': {'train_loss': 25.087022, 'val_loss': 21.924113, 'test_loss': 22.829321}} 2024-11-14 16:39:21,067 (client:354) INFO: {'Role': 'Client #2', 'Round': 31, 'Results_raw': {'train_loss': 23.849306, 'val_loss': 25.307266, 'test_loss': 24.406976}} 2024-11-14 16:40:04,656 (client:354) INFO: {'Role': 'Client #5', 'Round': 31, 'Results_raw': {'train_loss': 20.681111, 'val_loss': 20.508904, 'test_loss': 21.50587}} 2024-11-14 16:40:48,507 (client:354) INFO: {'Role': 'Client #10', 'Round': 31, 'Results_raw': {'train_loss': 22.927812, 'val_loss': 21.765851, 'test_loss': 22.051696}} 2024-11-14 16:41:31,841 (client:354) INFO: {'Role': 'Client #3', 'Round': 31, 'Results_raw': {'train_loss': 26.889675, 'val_loss': 26.238536, 'test_loss': 26.345374}} 2024-11-14 16:42:15,487 (client:354) INFO: {'Role': 'Client #9', 'Round': 31, 'Results_raw': {'train_loss': 25.699457, 'val_loss': 29.058076, 'test_loss': 24.732816}} 2024-11-14 16:43:00,838 (client:354) INFO: {'Role': 'Client #1', 'Round': 31, 'Results_raw': {'train_loss': 32.011911, 'val_loss': 30.721081, 'test_loss': 30.037081}} 2024-11-14 16:43:43,950 (client:354) INFO: {'Role': 'Client #6', 'Round': 31, 'Results_raw': {'train_loss': 18.546277, 'val_loss': 19.452217, 'test_loss': 19.56158}} 2024-11-14 16:44:28,464 (client:354) INFO: {'Role': 'Client #7', 'Round': 31, 'Results_raw': {'train_loss': 19.654565, 'val_loss': 19.617281, 'test_loss': 18.133024}} 2024-11-14 16:45:12,730 (client:354) INFO: {'Role': 'Client #8', 'Round': 31, 'Results_raw': {'train_loss': 23.422379, 'val_loss': 26.611156, 'test_loss': 22.444305}} 2024-11-14 16:45:12,733 (server:615) INFO: {'Role': 'Server #', 'Round': 30, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.082932), 'test_loss': np.float64(105891.921271), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.673052), 'val_loss': np.float64(111489.144189), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.082932), 'test_loss': np.float64(105891.921271), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.673052), 'val_loss': np.float64(111489.144189), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.258139), 'test_avg_loss_bottom_decile': np.float64(25.390714), 'test_avg_loss_top_decile': np.float64(39.813197), 'test_avg_loss_min': np.float64(24.059129), 'test_avg_loss_max': np.float64(39.813197), 'test_avg_loss_bottom10%': np.float64(24.059129), 'test_avg_loss_top10%': np.float64(39.813197), 'test_avg_loss_cos1': np.float64(0.99013), 'test_avg_loss_entropy': np.float64(2.292831), 'test_loss_std': np.float64(14988.64918), 'test_loss_bottom_decile': np.float64(89375.312317), 'test_loss_top_decile': np.float64(140142.453003), 'test_loss_min': np.float64(84688.133423), 'test_loss_max': np.float64(140142.453003), 'test_loss_bottom10%': np.float64(84688.133423), 'test_loss_top10%': np.float64(140142.453003), 'test_loss_cos1': np.float64(0.99013), 'test_loss_entropy': np.float64(2.292831), 'val_avg_loss_std': np.float64(4.411798), 'val_avg_loss_bottom_decile': np.float64(26.88773), 'val_avg_loss_top_decile': np.float64(40.88589), 'val_avg_loss_min': np.float64(25.532158), 'val_avg_loss_max': np.float64(40.88589), 'val_avg_loss_bottom10%': np.float64(25.532158), 'val_avg_loss_top10%': np.float64(40.88589), 'val_avg_loss_cos1': np.float64(0.990438), 'val_avg_loss_entropy': np.float64(2.293079), 'val_loss_std': np.float64(15529.528577), 'val_loss_bottom_decile': np.float64(94644.808533), 'val_loss_top_decile': np.float64(143918.333984), 'val_loss_min': np.float64(89873.197266), 'val_loss_max': np.float64(143918.333984), 'val_loss_bottom10%': np.float64(89873.197266), 'val_loss_top10%': np.float64(143918.333984), 'val_loss_cos1': np.float64(0.990438), 'val_loss_entropy': np.float64(2.293079)}} 2024-11-14 16:45:12,773 (server:353) INFO: Server: Starting evaluation at the end of round 31. 2024-11-14 16:45:12,773 (server:359) INFO: ----------- Starting a new training round (Round #32) ------------- 2024-11-14 16:47:25,214 (client:354) INFO: {'Role': 'Client #5', 'Round': 32, 'Results_raw': {'train_loss': 20.617412, 'val_loss': 20.707555, 'test_loss': 23.134574}} 2024-11-14 16:48:09,608 (client:354) INFO: {'Role': 'Client #10', 'Round': 32, 'Results_raw': {'train_loss': 22.882138, 'val_loss': 21.764273, 'test_loss': 21.947774}} 2024-11-14 16:48:53,444 (client:354) INFO: {'Role': 'Client #3', 'Round': 32, 'Results_raw': {'train_loss': 26.838287, 'val_loss': 25.925328, 'test_loss': 26.010399}} 2024-11-14 16:49:37,108 (client:354) INFO: {'Role': 'Client #2', 'Round': 32, 'Results_raw': {'train_loss': 23.879532, 'val_loss': 25.428842, 'test_loss': 24.38472}} 2024-11-14 16:50:19,553 (client:354) INFO: {'Role': 'Client #7', 'Round': 32, 'Results_raw': {'train_loss': 19.694166, 'val_loss': 19.441927, 'test_loss': 18.133656}} 2024-11-14 16:51:03,694 (client:354) INFO: {'Role': 'Client #8', 'Round': 32, 'Results_raw': {'train_loss': 23.437983, 'val_loss': 26.477739, 'test_loss': 22.088879}} 2024-11-14 16:51:45,570 (client:354) INFO: {'Role': 'Client #9', 'Round': 32, 'Results_raw': {'train_loss': 25.726814, 'val_loss': 29.734023, 'test_loss': 24.811783}} 2024-11-14 16:52:28,602 (client:354) INFO: {'Role': 'Client #1', 'Round': 32, 'Results_raw': {'train_loss': 32.066167, 'val_loss': 31.034193, 'test_loss': 30.550548}} 2024-11-14 16:53:13,423 (client:354) INFO: {'Role': 'Client #4', 'Round': 32, 'Results_raw': {'train_loss': 25.024774, 'val_loss': 21.603574, 'test_loss': 22.556571}} 2024-11-14 16:53:58,843 (client:354) INFO: {'Role': 'Client #6', 'Round': 32, 'Results_raw': {'train_loss': 18.402033, 'val_loss': 19.412357, 'test_loss': 19.91109}} 2024-11-14 16:53:58,848 (server:615) INFO: {'Role': 'Server #', 'Round': 31, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.770046), 'test_loss': np.float64(104790.560645), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.37289), 'val_loss': np.float64(110432.574237), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.770046), 'test_loss': np.float64(104790.560645), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.37289), 'val_loss': np.float64(110432.574237), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.203443), 'test_avg_loss_bottom_decile': np.float64(25.089192), 'test_avg_loss_top_decile': np.float64(39.346684), 'test_avg_loss_min': np.float64(23.989051), 'test_avg_loss_max': np.float64(39.346684), 'test_avg_loss_bottom10%': np.float64(23.989051), 'test_avg_loss_top10%': np.float64(39.346684), 'test_avg_loss_cos1': np.float64(0.990178), 'test_avg_loss_entropy': np.float64(2.292886), 'test_loss_std': np.float64(14796.118183), 'test_loss_bottom_decile': np.float64(88313.955322), 'test_loss_top_decile': np.float64(138500.326416), 'test_loss_min': np.float64(84441.460754), 'test_loss_max': np.float64(138500.326416), 'test_loss_bottom10%': np.float64(84441.460754), 'test_loss_top10%': np.float64(138500.326416), 'test_loss_cos1': np.float64(0.990178), 'test_loss_entropy': np.float64(2.292886), 'val_avg_loss_std': np.float64(4.36532), 'val_avg_loss_bottom_decile': np.float64(26.571779), 'val_avg_loss_top_decile': np.float64(40.421292), 'val_avg_loss_min': np.float64(25.437884), 'val_avg_loss_max': np.float64(40.421292), 'val_avg_loss_bottom10%': np.float64(25.437884), 'val_avg_loss_top10%': np.float64(40.421292), 'val_avg_loss_cos1': np.float64(0.990458), 'val_avg_loss_entropy': np.float64(2.2931), 'val_loss_std': np.float64(15365.927305), 'val_loss_bottom_decile': np.float64(93532.663574), 'val_loss_top_decile': np.float64(142282.947998), 'val_loss_min': np.float64(89541.351501), 'val_loss_max': np.float64(142282.947998), 'val_loss_bottom10%': np.float64(89541.351501), 'val_loss_top10%': np.float64(142282.947998), 'val_loss_cos1': np.float64(0.990458), 'val_loss_entropy': np.float64(2.2931)}} 2024-11-14 16:53:58,895 (server:353) INFO: Server: Starting evaluation at the end of round 32. 2024-11-14 16:53:58,896 (server:359) INFO: ----------- Starting a new training round (Round #33) ------------- 2024-11-14 16:56:26,404 (client:354) INFO: {'Role': 'Client #9', 'Round': 33, 'Results_raw': {'train_loss': 25.649322, 'val_loss': 29.802829, 'test_loss': 24.804206}} 2024-11-14 16:57:12,323 (client:354) INFO: {'Role': 'Client #8', 'Round': 33, 'Results_raw': {'train_loss': 23.302427, 'val_loss': 25.531696, 'test_loss': 21.894598}} 2024-11-14 16:57:57,078 (client:354) INFO: {'Role': 'Client #7', 'Round': 33, 'Results_raw': {'train_loss': 19.71032, 'val_loss': 19.402485, 'test_loss': 18.100485}} 2024-11-14 16:58:43,062 (client:354) INFO: {'Role': 'Client #10', 'Round': 33, 'Results_raw': {'train_loss': 22.828806, 'val_loss': 21.861617, 'test_loss': 22.056384}} 2024-11-14 16:59:27,003 (client:354) INFO: {'Role': 'Client #1', 'Round': 33, 'Results_raw': {'train_loss': 31.998635, 'val_loss': 30.781252, 'test_loss': 30.031309}} 2024-11-14 17:00:10,108 (client:354) INFO: {'Role': 'Client #5', 'Round': 33, 'Results_raw': {'train_loss': 20.605369, 'val_loss': 20.179728, 'test_loss': 22.943672}} 2024-11-14 17:00:51,501 (client:354) INFO: {'Role': 'Client #3', 'Round': 33, 'Results_raw': {'train_loss': 26.846283, 'val_loss': 26.319921, 'test_loss': 26.214462}} 2024-11-14 17:01:34,103 (client:354) INFO: {'Role': 'Client #6', 'Round': 33, 'Results_raw': {'train_loss': 18.345659, 'val_loss': 19.514777, 'test_loss': 19.7178}} 2024-11-14 17:02:17,667 (client:354) INFO: {'Role': 'Client #2', 'Round': 33, 'Results_raw': {'train_loss': 23.825992, 'val_loss': 25.621066, 'test_loss': 24.466265}} 2024-11-14 17:03:00,940 (client:354) INFO: {'Role': 'Client #4', 'Round': 33, 'Results_raw': {'train_loss': 25.029864, 'val_loss': 21.854395, 'test_loss': 22.431945}} 2024-11-14 17:03:00,943 (server:615) INFO: {'Role': 'Server #', 'Round': 32, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.183057), 'test_loss': np.float64(106244.361346), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.763455), 'val_loss': np.float64(111807.360083), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.183057), 'test_loss': np.float64(106244.361346), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.763455), 'val_loss': np.float64(111807.360083), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.238173), 'test_avg_loss_bottom_decile': np.float64(25.638933), 'test_avg_loss_top_decile': np.float64(39.832322), 'test_avg_loss_min': np.float64(24.221192), 'test_avg_loss_max': np.float64(39.832322), 'test_avg_loss_bottom10%': np.float64(24.221192), 'test_avg_loss_top10%': np.float64(39.832322), 'test_avg_loss_cos1': np.float64(0.990285), 'test_avg_loss_entropy': np.float64(2.292991), 'test_loss_std': np.float64(14918.368457), 'test_loss_bottom_decile': np.float64(90249.044861), 'test_loss_top_decile': np.float64(140209.773315), 'test_loss_min': np.float64(85258.594299), 'test_loss_max': np.float64(140209.773315), 'test_loss_bottom10%': np.float64(85258.594299), 'test_loss_top10%': np.float64(140209.773315), 'test_loss_cos1': np.float64(0.990285), 'test_loss_entropy': np.float64(2.292991), 'val_avg_loss_std': np.float64(4.427278), 'val_avg_loss_bottom_decile': np.float64(27.114419), 'val_avg_loss_top_decile': np.float64(40.860781), 'val_avg_loss_min': np.float64(25.635903), 'val_avg_loss_max': np.float64(40.860781), 'val_avg_loss_bottom10%': np.float64(25.635903), 'val_avg_loss_top10%': np.float64(40.860781), 'val_avg_loss_cos1': np.float64(0.990426), 'val_avg_loss_entropy': np.float64(2.293067), 'val_loss_std': np.float64(15584.019445), 'val_loss_bottom_decile': np.float64(95442.755981), 'val_loss_top_decile': np.float64(143829.949219), 'val_loss_min': np.float64(90238.380127), 'val_loss_max': np.float64(143829.949219), 'val_loss_bottom10%': np.float64(90238.380127), 'val_loss_top10%': np.float64(143829.949219), 'val_loss_cos1': np.float64(0.990426), 'val_loss_entropy': np.float64(2.293067)}} 2024-11-14 17:03:00,975 (server:353) INFO: Server: Starting evaluation at the end of round 33. 2024-11-14 17:03:00,976 (server:359) INFO: ----------- Starting a new training round (Round #34) ------------- 2024-11-14 17:05:13,804 (client:354) INFO: {'Role': 'Client #4', 'Round': 34, 'Results_raw': {'train_loss': 24.965425, 'val_loss': 22.15663, 'test_loss': 22.914436}} 2024-11-14 17:05:57,373 (client:354) INFO: {'Role': 'Client #7', 'Round': 34, 'Results_raw': {'train_loss': 19.579481, 'val_loss': 19.406627, 'test_loss': 18.007972}} 2024-11-14 17:06:41,313 (client:354) INFO: {'Role': 'Client #8', 'Round': 34, 'Results_raw': {'train_loss': 23.338494, 'val_loss': 25.751378, 'test_loss': 22.255837}} 2024-11-14 17:07:24,444 (client:354) INFO: {'Role': 'Client #10', 'Round': 34, 'Results_raw': {'train_loss': 22.828218, 'val_loss': 21.982156, 'test_loss': 22.223013}} 2024-11-14 17:08:08,304 (client:354) INFO: {'Role': 'Client #5', 'Round': 34, 'Results_raw': {'train_loss': 20.568928, 'val_loss': 20.207292, 'test_loss': 22.924205}} 2024-11-14 17:08:50,377 (client:354) INFO: {'Role': 'Client #2', 'Round': 34, 'Results_raw': {'train_loss': 23.852798, 'val_loss': 25.355899, 'test_loss': 24.154767}} 2024-11-14 17:09:34,020 (client:354) INFO: {'Role': 'Client #9', 'Round': 34, 'Results_raw': {'train_loss': 25.546855, 'val_loss': 29.301549, 'test_loss': 24.682247}} 2024-11-14 17:10:17,979 (client:354) INFO: {'Role': 'Client #3', 'Round': 34, 'Results_raw': {'train_loss': 26.791396, 'val_loss': 25.852624, 'test_loss': 26.039716}} 2024-11-14 17:11:02,317 (client:354) INFO: {'Role': 'Client #1', 'Round': 34, 'Results_raw': {'train_loss': 32.028215, 'val_loss': 30.540485, 'test_loss': 30.083803}} 2024-11-14 17:11:47,166 (client:354) INFO: {'Role': 'Client #6', 'Round': 34, 'Results_raw': {'train_loss': 18.377373, 'val_loss': 19.271479, 'test_loss': 19.396997}} 2024-11-14 17:11:47,172 (server:615) INFO: {'Role': 'Server #', 'Round': 33, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.912151), 'test_loss': np.float64(105290.772119), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.519472), 'val_loss': np.float64(110948.540393), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.912151), 'test_loss': np.float64(105290.772119), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.519472), 'val_loss': np.float64(110948.540393), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.282516), 'test_avg_loss_bottom_decile': np.float64(25.057924), 'test_avg_loss_top_decile': np.float64(39.73269), 'test_avg_loss_min': np.float64(24.002643), 'test_avg_loss_max': np.float64(39.73269), 'test_avg_loss_bottom10%': np.float64(24.002643), 'test_avg_loss_top10%': np.float64(39.73269), 'test_avg_loss_cos1': np.float64(0.989906), 'test_avg_loss_entropy': np.float64(2.29262), 'test_loss_std': np.float64(15074.455213), 'test_loss_bottom_decile': np.float64(88203.892761), 'test_loss_top_decile': np.float64(139859.067505), 'test_loss_min': np.float64(84489.302917), 'test_loss_max': np.float64(139859.067505), 'test_loss_bottom10%': np.float64(84489.302917), 'test_loss_top10%': np.float64(139859.067505), 'test_loss_cos1': np.float64(0.989906), 'test_loss_entropy': np.float64(2.29262), 'val_avg_loss_std': np.float64(4.449171), 'val_avg_loss_bottom_decile': np.float64(26.492239), 'val_avg_loss_top_decile': np.float64(40.779827), 'val_avg_loss_min': np.float64(25.42994), 'val_avg_loss_max': np.float64(40.779827), 'val_avg_loss_bottom10%': np.float64(25.42994), 'val_avg_loss_top10%': np.float64(40.779827), 'val_avg_loss_cos1': np.float64(0.990184), 'val_avg_loss_entropy': np.float64(2.29282), 'val_loss_std': np.float64(15661.082471), 'val_loss_bottom_decile': np.float64(93252.681458), 'val_loss_top_decile': np.float64(143544.989746), 'val_loss_min': np.float64(89513.387329), 'val_loss_max': np.float64(143544.989746), 'val_loss_bottom10%': np.float64(89513.387329), 'val_loss_top10%': np.float64(143544.989746), 'val_loss_cos1': np.float64(0.990184), 'val_loss_entropy': np.float64(2.29282)}} 2024-11-14 17:11:47,203 (server:353) INFO: Server: Starting evaluation at the end of round 34. 2024-11-14 17:11:47,204 (server:359) INFO: ----------- Starting a new training round (Round #35) ------------- 2024-11-14 17:14:00,723 (client:354) INFO: {'Role': 'Client #3', 'Round': 35, 'Results_raw': {'train_loss': 26.789593, 'val_loss': 26.094932, 'test_loss': 26.109161}} 2024-11-14 17:14:45,416 (client:354) INFO: {'Role': 'Client #9', 'Round': 35, 'Results_raw': {'train_loss': 25.507747, 'val_loss': 29.512714, 'test_loss': 24.841245}} 2024-11-14 17:15:29,832 (client:354) INFO: {'Role': 'Client #2', 'Round': 35, 'Results_raw': {'train_loss': 23.70461, 'val_loss': 25.11037, 'test_loss': 24.09887}} 2024-11-14 17:16:13,635 (client:354) INFO: {'Role': 'Client #8', 'Round': 35, 'Results_raw': {'train_loss': 23.210625, 'val_loss': 25.79757, 'test_loss': 22.088981}} 2024-11-14 17:16:57,436 (client:354) INFO: {'Role': 'Client #5', 'Round': 35, 'Results_raw': {'train_loss': 20.529453, 'val_loss': 20.629132, 'test_loss': 22.774405}} 2024-11-14 17:17:39,144 (client:354) INFO: {'Role': 'Client #4', 'Round': 35, 'Results_raw': {'train_loss': 25.007544, 'val_loss': 21.843712, 'test_loss': 22.488225}} 2024-11-14 17:18:22,392 (client:354) INFO: {'Role': 'Client #6', 'Round': 35, 'Results_raw': {'train_loss': 18.331062, 'val_loss': 19.422704, 'test_loss': 19.684472}} 2024-11-14 17:19:05,880 (client:354) INFO: {'Role': 'Client #7', 'Round': 35, 'Results_raw': {'train_loss': 19.556281, 'val_loss': 19.353086, 'test_loss': 17.998069}} 2024-11-14 17:19:49,591 (client:354) INFO: {'Role': 'Client #10', 'Round': 35, 'Results_raw': {'train_loss': 22.835005, 'val_loss': 22.029948, 'test_loss': 22.141079}} 2024-11-14 17:20:33,241 (client:354) INFO: {'Role': 'Client #1', 'Round': 35, 'Results_raw': {'train_loss': 31.837246, 'val_loss': 30.952516, 'test_loss': 30.363393}} 2024-11-14 17:20:33,246 (server:615) INFO: {'Role': 'Server #', 'Round': 34, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.020434), 'test_loss': np.float64(105671.929242), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.6072), 'val_loss': np.float64(111257.342755), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.020434), 'test_loss': np.float64(105671.929242), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.6072), 'val_loss': np.float64(111257.342755), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.275498), 'test_avg_loss_bottom_decile': np.float64(25.307122), 'test_avg_loss_top_decile': np.float64(39.712018), 'test_avg_loss_min': np.float64(23.99374), 'test_avg_loss_max': np.float64(39.712018), 'test_avg_loss_bottom10%': np.float64(23.99374), 'test_avg_loss_top10%': np.float64(39.712018), 'test_avg_loss_cos1': np.float64(0.99001), 'test_avg_loss_entropy': np.float64(2.292705), 'test_loss_std': np.float64(15049.75261), 'test_loss_bottom_decile': np.float64(89081.070374), 'test_loss_top_decile': np.float64(139786.303101), 'test_loss_min': np.float64(84457.963562), 'test_loss_max': np.float64(139786.303101), 'test_loss_bottom10%': np.float64(84457.963562), 'test_loss_top10%': np.float64(139786.303101), 'test_loss_cos1': np.float64(0.99001), 'test_loss_entropy': np.float64(2.292705), 'val_avg_loss_std': np.float64(4.444222), 'val_avg_loss_bottom_decile': np.float64(26.791455), 'val_avg_loss_top_decile': np.float64(40.75317), 'val_avg_loss_min': np.float64(25.426262), 'val_avg_loss_max': np.float64(40.75317), 'val_avg_loss_bottom10%': np.float64(25.426262), 'val_avg_loss_top10%': np.float64(40.75317), 'val_avg_loss_cos1': np.float64(0.990259), 'val_avg_loss_entropy': np.float64(2.292888), 'val_loss_std': np.float64(15643.662016), 'val_loss_bottom_decile': np.float64(94305.920166), 'val_loss_top_decile': np.float64(143451.159546), 'val_loss_min': np.float64(89500.440796), 'val_loss_max': np.float64(143451.159546), 'val_loss_bottom10%': np.float64(89500.440796), 'val_loss_top10%': np.float64(143451.159546), 'val_loss_cos1': np.float64(0.990259), 'val_loss_entropy': np.float64(2.292888)}} 2024-11-14 17:20:33,283 (server:353) INFO: Server: Starting evaluation at the end of round 35. 2024-11-14 17:20:33,284 (server:359) INFO: ----------- Starting a new training round (Round #36) ------------- 2024-11-14 17:22:48,446 (client:354) INFO: {'Role': 'Client #9', 'Round': 36, 'Results_raw': {'train_loss': 25.481806, 'val_loss': 29.76214, 'test_loss': 24.822678}} 2024-11-14 17:23:32,433 (client:354) INFO: {'Role': 'Client #6', 'Round': 36, 'Results_raw': {'train_loss': 18.37717, 'val_loss': 19.557833, 'test_loss': 19.794315}} 2024-11-14 17:24:16,110 (client:354) INFO: {'Role': 'Client #8', 'Round': 36, 'Results_raw': {'train_loss': 23.255424, 'val_loss': 25.366345, 'test_loss': 21.739108}} 2024-11-14 17:25:02,092 (client:354) INFO: {'Role': 'Client #5', 'Round': 36, 'Results_raw': {'train_loss': 20.527533, 'val_loss': 20.14619, 'test_loss': 20.957385}} 2024-11-14 17:25:46,518 (client:354) INFO: {'Role': 'Client #7', 'Round': 36, 'Results_raw': {'train_loss': 19.531484, 'val_loss': 19.299214, 'test_loss': 17.946801}} 2024-11-14 17:26:31,512 (client:354) INFO: {'Role': 'Client #4', 'Round': 36, 'Results_raw': {'train_loss': 25.008569, 'val_loss': 22.3115, 'test_loss': 23.021441}} 2024-11-14 17:27:15,425 (client:354) INFO: {'Role': 'Client #3', 'Round': 36, 'Results_raw': {'train_loss': 26.702824, 'val_loss': 25.899785, 'test_loss': 25.931081}} 2024-11-14 17:27:58,958 (client:354) INFO: {'Role': 'Client #2', 'Round': 36, 'Results_raw': {'train_loss': 23.722798, 'val_loss': 25.27458, 'test_loss': 24.235258}} 2024-11-14 17:28:42,691 (client:354) INFO: {'Role': 'Client #10', 'Round': 36, 'Results_raw': {'train_loss': 22.736309, 'val_loss': 22.062228, 'test_loss': 22.122721}} 2024-11-14 17:29:26,825 (client:354) INFO: {'Role': 'Client #1', 'Round': 36, 'Results_raw': {'train_loss': 31.859497, 'val_loss': 30.20454, 'test_loss': 29.914095}} 2024-11-14 17:29:26,831 (server:615) INFO: {'Role': 'Server #', 'Round': 35, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.900085), 'test_loss': np.float64(105248.300543), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.489778), 'val_loss': np.float64(110844.018481), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.900085), 'test_loss': np.float64(105248.300543), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.489778), 'val_loss': np.float64(110844.018481), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.341889), 'test_avg_loss_bottom_decile': np.float64(24.887614), 'test_avg_loss_top_decile': np.float64(39.807549), 'test_avg_loss_min': np.float64(23.890565), 'test_avg_loss_max': np.float64(39.807549), 'test_avg_loss_bottom10%': np.float64(23.890565), 'test_avg_loss_top10%': np.float64(39.807549), 'test_avg_loss_cos1': np.float64(0.98962), 'test_avg_loss_entropy': np.float64(2.292322), 'test_loss_std': np.float64(15283.448633), 'test_loss_bottom_decile': np.float64(87604.401489), 'test_loss_top_decile': np.float64(140122.572266), 'test_loss_min': np.float64(84094.788208), 'test_loss_max': np.float64(140122.572266), 'test_loss_bottom10%': np.float64(84094.788208), 'test_loss_top10%': np.float64(140122.572266), 'test_loss_cos1': np.float64(0.98962), 'test_loss_entropy': np.float64(2.292322), 'val_avg_loss_std': np.float64(4.497992), 'val_avg_loss_bottom_decile': np.float64(26.379028), 'val_avg_loss_top_decile': np.float64(40.85427), 'val_avg_loss_min': np.float64(25.323914), 'val_avg_loss_max': np.float64(40.85427), 'val_avg_loss_bottom10%': np.float64(25.323914), 'val_avg_loss_top10%': np.float64(40.85427), 'val_avg_loss_cos1': np.float64(0.989952), 'val_avg_loss_entropy': np.float64(2.292584), 'val_loss_std': np.float64(15832.930903), 'val_loss_bottom_decile': np.float64(92854.177307), 'val_loss_top_decile': np.float64(143807.030273), 'val_loss_min': np.float64(89140.17688), 'val_loss_max': np.float64(143807.030273), 'val_loss_bottom10%': np.float64(89140.17688), 'val_loss_top10%': np.float64(143807.030273), 'val_loss_cos1': np.float64(0.989952), 'val_loss_entropy': np.float64(2.292584)}} 2024-11-14 17:29:26,864 (server:353) INFO: Server: Starting evaluation at the end of round 36. 2024-11-14 17:29:26,864 (server:359) INFO: ----------- Starting a new training round (Round #37) ------------- 2024-11-14 17:31:43,265 (client:354) INFO: {'Role': 'Client #9', 'Round': 37, 'Results_raw': {'train_loss': 25.475104, 'val_loss': 29.549539, 'test_loss': 25.125385}} 2024-11-14 17:32:30,153 (client:354) INFO: {'Role': 'Client #8', 'Round': 37, 'Results_raw': {'train_loss': 23.133317, 'val_loss': 26.025022, 'test_loss': 22.080943}} 2024-11-14 17:33:17,620 (client:354) INFO: {'Role': 'Client #1', 'Round': 37, 'Results_raw': {'train_loss': 31.947149, 'val_loss': 30.586832, 'test_loss': 30.203432}} 2024-11-14 17:34:04,120 (client:354) INFO: {'Role': 'Client #2', 'Round': 37, 'Results_raw': {'train_loss': 23.688679, 'val_loss': 25.383394, 'test_loss': 24.271535}} 2024-11-14 17:34:49,829 (client:354) INFO: {'Role': 'Client #4', 'Round': 37, 'Results_raw': {'train_loss': 24.974343, 'val_loss': 21.461369, 'test_loss': 22.236377}} 2024-11-14 17:35:36,321 (client:354) INFO: {'Role': 'Client #5', 'Round': 37, 'Results_raw': {'train_loss': 20.541611, 'val_loss': 20.523316, 'test_loss': 22.844059}} 2024-11-14 17:36:22,612 (client:354) INFO: {'Role': 'Client #3', 'Round': 37, 'Results_raw': {'train_loss': 26.674742, 'val_loss': 25.827791, 'test_loss': 25.91101}} 2024-11-14 17:37:07,275 (client:354) INFO: {'Role': 'Client #6', 'Round': 37, 'Results_raw': {'train_loss': 18.331227, 'val_loss': 19.477985, 'test_loss': 19.991852}} 2024-11-14 17:37:51,159 (client:354) INFO: {'Role': 'Client #10', 'Round': 37, 'Results_raw': {'train_loss': 22.770758, 'val_loss': 21.959767, 'test_loss': 22.087642}} 2024-11-14 17:38:35,242 (client:354) INFO: {'Role': 'Client #7', 'Round': 37, 'Results_raw': {'train_loss': 19.530176, 'val_loss': 19.421859, 'test_loss': 18.129117}} 2024-11-14 17:38:35,248 (server:615) INFO: {'Role': 'Server #', 'Round': 36, 'Results_weighted_avg': {'test_avg_loss': np.float64(30.015775), 'test_loss': np.float64(105655.527112), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.564075), 'val_loss': np.float64(111105.545221), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(30.015775), 'test_loss': np.float64(105655.527112), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.564075), 'val_loss': np.float64(111105.545221), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.318046), 'test_avg_loss_bottom_decile': np.float64(25.198527), 'test_avg_loss_top_decile': np.float64(39.882244), 'test_avg_loss_min': np.float64(24.03285), 'test_avg_loss_max': np.float64(39.882244), 'test_avg_loss_bottom10%': np.float64(24.03285), 'test_avg_loss_top10%': np.float64(39.882244), 'test_avg_loss_cos1': np.float64(0.98981), 'test_avg_loss_entropy': np.float64(2.292516), 'test_loss_std': np.float64(15199.522803), 'test_loss_bottom_decile': np.float64(88698.815186), 'test_loss_top_decile': np.float64(140385.499023), 'test_loss_min': np.float64(84595.632629), 'test_loss_max': np.float64(140385.499023), 'test_loss_bottom10%': np.float64(84595.632629), 'test_loss_top10%': np.float64(140385.499023), 'test_loss_cos1': np.float64(0.98981), 'test_loss_entropy': np.float64(2.292516), 'val_avg_loss_std': np.float64(4.495217), 'val_avg_loss_bottom_decile': np.float64(26.665056), 'val_avg_loss_top_decile': np.float64(40.917044), 'val_avg_loss_min': np.float64(25.483825), 'val_avg_loss_max': np.float64(40.917044), 'val_avg_loss_bottom10%': np.float64(25.483825), 'val_avg_loss_top10%': np.float64(40.917044), 'val_avg_loss_cos1': np.float64(0.990011), 'val_avg_loss_entropy': np.float64(2.292654), 'val_loss_std': np.float64(15823.163006), 'val_loss_bottom_decile': np.float64(93860.996094), 'val_loss_top_decile': np.float64(144027.99585), 'val_loss_min': np.float64(89703.063293), 'val_loss_max': np.float64(144027.99585), 'val_loss_bottom10%': np.float64(89703.063293), 'val_loss_top10%': np.float64(144027.99585), 'val_loss_cos1': np.float64(0.990011), 'val_loss_entropy': np.float64(2.292654)}} 2024-11-14 17:38:35,279 (server:353) INFO: Server: Starting evaluation at the end of round 37. 2024-11-14 17:38:35,280 (server:359) INFO: ----------- Starting a new training round (Round #38) ------------- 2024-11-14 17:40:48,296 (client:354) INFO: {'Role': 'Client #4', 'Round': 38, 'Results_raw': {'train_loss': 24.960106, 'val_loss': 21.592387, 'test_loss': 22.391242}} 2024-11-14 17:41:31,690 (client:354) INFO: {'Role': 'Client #2', 'Round': 38, 'Results_raw': {'train_loss': 23.772132, 'val_loss': 25.477849, 'test_loss': 24.347153}} 2024-11-14 17:42:15,107 (client:354) INFO: {'Role': 'Client #9', 'Round': 38, 'Results_raw': {'train_loss': 25.359918, 'val_loss': 29.436838, 'test_loss': 25.129972}} 2024-11-14 17:42:58,825 (client:354) INFO: {'Role': 'Client #6', 'Round': 38, 'Results_raw': {'train_loss': 18.288559, 'val_loss': 19.309569, 'test_loss': 19.931005}} 2024-11-14 17:43:39,526 (client:354) INFO: {'Role': 'Client #5', 'Round': 38, 'Results_raw': {'train_loss': 20.453445, 'val_loss': 20.183303, 'test_loss': 21.12876}} 2024-11-14 17:44:22,931 (client:354) INFO: {'Role': 'Client #7', 'Round': 38, 'Results_raw': {'train_loss': 19.530125, 'val_loss': 19.377784, 'test_loss': 17.971246}} 2024-11-14 17:45:06,656 (client:354) INFO: {'Role': 'Client #10', 'Round': 38, 'Results_raw': {'train_loss': 22.769838, 'val_loss': 21.777402, 'test_loss': 21.874838}} 2024-11-14 17:45:50,488 (client:354) INFO: {'Role': 'Client #3', 'Round': 38, 'Results_raw': {'train_loss': 26.69975, 'val_loss': 26.058749, 'test_loss': 26.212249}} 2024-11-14 17:46:34,188 (client:354) INFO: {'Role': 'Client #1', 'Round': 38, 'Results_raw': {'train_loss': 31.908934, 'val_loss': 31.509499, 'test_loss': 31.052701}} 2024-11-14 17:47:17,974 (client:354) INFO: {'Role': 'Client #8', 'Round': 38, 'Results_raw': {'train_loss': 23.094881, 'val_loss': 25.275006, 'test_loss': 21.575398}} 2024-11-14 17:47:17,976 (server:615) INFO: {'Role': 'Server #', 'Round': 37, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.78562), 'test_loss': np.float64(104845.382007), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.360801), 'val_loss': np.float64(110390.019696), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.78562), 'test_loss': np.float64(104845.382007), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.360801), 'val_loss': np.float64(110390.019696), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.230782), 'test_avg_loss_bottom_decile': np.float64(24.99866), 'test_avg_loss_top_decile': np.float64(39.490639), 'test_avg_loss_min': np.float64(24.025525), 'test_avg_loss_max': np.float64(39.490639), 'test_avg_loss_bottom10%': np.float64(24.025525), 'test_avg_loss_top10%': np.float64(39.490639), 'test_avg_loss_cos1': np.float64(0.990062), 'test_avg_loss_entropy': np.float64(2.292777), 'test_loss_std': np.float64(14892.351631), 'test_loss_bottom_decile': np.float64(87995.284668), 'test_loss_top_decile': np.float64(139007.051025), 'test_loss_min': np.float64(84569.848267), 'test_loss_max': np.float64(139007.051025), 'test_loss_bottom10%': np.float64(84569.848267), 'test_loss_top10%': np.float64(139007.051025), 'test_loss_cos1': np.float64(0.990062), 'test_loss_entropy': np.float64(2.292777), 'val_avg_loss_std': np.float64(4.3848), 'val_avg_loss_bottom_decile': np.float64(26.510912), 'val_avg_loss_top_decile': np.float64(40.493767), 'val_avg_loss_min': np.float64(25.456999), 'val_avg_loss_max': np.float64(40.493767), 'val_avg_loss_bottom10%': np.float64(25.456999), 'val_avg_loss_top10%': np.float64(40.493767), 'val_avg_loss_cos1': np.float64(0.990367), 'val_avg_loss_entropy': np.float64(2.293013), 'val_loss_std': np.float64(15434.495635), 'val_loss_bottom_decile': np.float64(93318.408752), 'val_loss_top_decile': np.float64(142538.05835), 'val_loss_min': np.float64(89608.635559), 'val_loss_max': np.float64(142538.05835), 'val_loss_bottom10%': np.float64(89608.635559), 'val_loss_top10%': np.float64(142538.05835), 'val_loss_cos1': np.float64(0.990367), 'val_loss_entropy': np.float64(2.293013)}} 2024-11-14 17:47:18,006 (server:353) INFO: Server: Starting evaluation at the end of round 38. 2024-11-14 17:47:18,006 (server:359) INFO: ----------- Starting a new training round (Round #39) ------------- 2024-11-14 17:49:27,313 (client:354) INFO: {'Role': 'Client #5', 'Round': 39, 'Results_raw': {'train_loss': 20.422743, 'val_loss': 20.316935, 'test_loss': 21.454041}} 2024-11-14 17:50:10,719 (client:354) INFO: {'Role': 'Client #10', 'Round': 39, 'Results_raw': {'train_loss': 22.690256, 'val_loss': 21.651737, 'test_loss': 22.355509}} 2024-11-14 17:50:54,691 (client:354) INFO: {'Role': 'Client #7', 'Round': 39, 'Results_raw': {'train_loss': 19.414175, 'val_loss': 19.364781, 'test_loss': 17.976583}} 2024-11-14 17:51:37,241 (client:354) INFO: {'Role': 'Client #3', 'Round': 39, 'Results_raw': {'train_loss': 26.637459, 'val_loss': 26.047285, 'test_loss': 26.241828}} 2024-11-14 17:52:19,810 (client:354) INFO: {'Role': 'Client #8', 'Round': 39, 'Results_raw': {'train_loss': 23.132196, 'val_loss': 25.930761, 'test_loss': 21.686575}} 2024-11-14 17:53:03,812 (client:354) INFO: {'Role': 'Client #4', 'Round': 39, 'Results_raw': {'train_loss': 24.877635, 'val_loss': 21.538507, 'test_loss': 22.304498}} 2024-11-14 17:53:47,733 (client:354) INFO: {'Role': 'Client #2', 'Round': 39, 'Results_raw': {'train_loss': 23.652006, 'val_loss': 25.190238, 'test_loss': 24.166401}} 2024-11-14 17:54:31,916 (client:354) INFO: {'Role': 'Client #6', 'Round': 39, 'Results_raw': {'train_loss': 18.260662, 'val_loss': 19.467455, 'test_loss': 19.781132}} 2024-11-14 17:55:15,493 (client:354) INFO: {'Role': 'Client #1', 'Round': 39, 'Results_raw': {'train_loss': 31.874242, 'val_loss': 31.072115, 'test_loss': 30.412829}} 2024-11-14 17:55:59,170 (client:354) INFO: {'Role': 'Client #9', 'Round': 39, 'Results_raw': {'train_loss': 25.342167, 'val_loss': 29.484863, 'test_loss': 24.998913}} 2024-11-14 17:55:59,173 (server:615) INFO: {'Role': 'Server #', 'Round': 38, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.88492), 'test_loss': np.float64(105194.916736), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.461753), 'val_loss': np.float64(110745.370837), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.88492), 'test_loss': np.float64(105194.916736), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.461753), 'val_loss': np.float64(110745.370837), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.202667), 'test_avg_loss_bottom_decile': np.float64(25.368908), 'test_avg_loss_top_decile': np.float64(39.64856), 'test_avg_loss_min': np.float64(24.184618), 'test_avg_loss_max': np.float64(39.64856), 'test_avg_loss_bottom10%': np.float64(24.184618), 'test_avg_loss_top10%': np.float64(39.64856), 'test_avg_loss_cos1': np.float64(0.990256), 'test_avg_loss_entropy': np.float64(2.292999), 'test_loss_std': np.float64(14793.387983), 'test_loss_bottom_decile': np.float64(89298.556824), 'test_loss_top_decile': np.float64(139562.930542), 'test_loss_min': np.float64(85129.856079), 'test_loss_max': np.float64(139562.930542), 'test_loss_bottom10%': np.float64(85129.856079), 'test_loss_top10%': np.float64(139562.930542), 'test_loss_cos1': np.float64(0.990256), 'test_loss_entropy': np.float64(2.292999), 'val_avg_loss_std': np.float64(4.366699), 'val_avg_loss_bottom_decile': np.float64(26.864024), 'val_avg_loss_top_decile': np.float64(40.698847), 'val_avg_loss_min': np.float64(25.602907), 'val_avg_loss_max': np.float64(40.698847), 'val_avg_loss_bottom10%': np.float64(25.602907), 'val_avg_loss_top10%': np.float64(40.698847), 'val_avg_loss_cos1': np.float64(0.990505), 'val_avg_loss_entropy': np.float64(2.293176), 'val_loss_std': np.float64(15370.779304), 'val_loss_bottom_decile': np.float64(94561.366028), 'val_loss_top_decile': np.float64(143259.940796), 'val_loss_min': np.float64(90122.232605), 'val_loss_max': np.float64(143259.940796), 'val_loss_bottom10%': np.float64(90122.232605), 'val_loss_top10%': np.float64(143259.940796), 'val_loss_cos1': np.float64(0.990505), 'val_loss_entropy': np.float64(2.293176)}} 2024-11-14 17:55:59,203 (server:353) INFO: Server: Starting evaluation at the end of round 39. 2024-11-14 17:55:59,203 (server:359) INFO: ----------- Starting a new training round (Round #40) ------------- 2024-11-14 17:58:09,600 (client:354) INFO: {'Role': 'Client #3', 'Round': 40, 'Results_raw': {'train_loss': 26.60965, 'val_loss': 25.940223, 'test_loss': 26.000666}} 2024-11-14 17:58:53,472 (client:354) INFO: {'Role': 'Client #5', 'Round': 40, 'Results_raw': {'train_loss': 20.369348, 'val_loss': 19.992144, 'test_loss': 22.020558}} 2024-11-14 17:59:36,169 (client:354) INFO: {'Role': 'Client #4', 'Round': 40, 'Results_raw': {'train_loss': 24.862307, 'val_loss': 21.880645, 'test_loss': 22.431926}} 2024-11-14 18:00:16,476 (client:354) INFO: {'Role': 'Client #6', 'Round': 40, 'Results_raw': {'train_loss': 18.285934, 'val_loss': 19.477067, 'test_loss': 19.289935}} 2024-11-14 18:00:56,942 (client:354) INFO: {'Role': 'Client #7', 'Round': 40, 'Results_raw': {'train_loss': 19.47118, 'val_loss': 19.618377, 'test_loss': 18.274111}} 2024-11-14 18:01:37,954 (client:354) INFO: {'Role': 'Client #2', 'Round': 40, 'Results_raw': {'train_loss': 23.639421, 'val_loss': 25.288376, 'test_loss': 24.151321}} 2024-11-14 18:02:19,039 (client:354) INFO: {'Role': 'Client #9', 'Round': 40, 'Results_raw': {'train_loss': 25.333371, 'val_loss': 29.384647, 'test_loss': 24.741569}} 2024-11-14 18:03:00,723 (client:354) INFO: {'Role': 'Client #1', 'Round': 40, 'Results_raw': {'train_loss': 31.744633, 'val_loss': 30.275732, 'test_loss': 29.889578}} 2024-11-14 18:03:43,314 (client:354) INFO: {'Role': 'Client #8', 'Round': 40, 'Results_raw': {'train_loss': 23.088977, 'val_loss': 26.868529, 'test_loss': 21.830184}} 2024-11-14 18:04:25,326 (client:354) INFO: {'Role': 'Client #10', 'Round': 40, 'Results_raw': {'train_loss': 22.644803, 'val_loss': 21.80515, 'test_loss': 22.061345}} 2024-11-14 18:04:25,329 (server:615) INFO: {'Role': 'Server #', 'Round': 39, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.97987), 'test_loss': np.float64(105529.142755), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.522564), 'val_loss': np.float64(110959.425391), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.97987), 'test_loss': np.float64(105529.142755), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.522564), 'val_loss': np.float64(110959.425391), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.240699), 'test_avg_loss_bottom_decile': np.float64(25.24311), 'test_avg_loss_top_decile': np.float64(39.685106), 'test_avg_loss_min': np.float64(24.153296), 'test_avg_loss_max': np.float64(39.685106), 'test_avg_loss_bottom10%': np.float64(24.153296), 'test_avg_loss_top10%': np.float64(39.685106), 'test_avg_loss_cos1': np.float64(0.990143), 'test_avg_loss_entropy': np.float64(2.292853), 'test_loss_std': np.float64(14927.260835), 'test_loss_bottom_decile': np.float64(88855.747437), 'test_loss_top_decile': np.float64(139691.57312), 'test_loss_min': np.float64(85019.601257), 'test_loss_max': np.float64(139691.57312), 'test_loss_bottom10%': np.float64(85019.601257), 'test_loss_top10%': np.float64(139691.57312), 'test_loss_cos1': np.float64(0.990143), 'test_loss_entropy': np.float64(2.292853), 'val_avg_loss_std': np.float64(4.390546), 'val_avg_loss_bottom_decile': np.float64(26.73869), 'val_avg_loss_top_decile': np.float64(40.684079), 'val_avg_loss_min': np.float64(25.530725), 'val_avg_loss_max': np.float64(40.684079), 'val_avg_loss_bottom10%': np.float64(25.530725), 'val_avg_loss_top10%': np.float64(40.684079), 'val_avg_loss_cos1': np.float64(0.990439), 'val_avg_loss_entropy': np.float64(2.293082), 'val_loss_std': np.float64(15454.723012), 'val_loss_bottom_decile': np.float64(94120.189087), 'val_loss_top_decile': np.float64(143207.958374), 'val_loss_min': np.float64(89868.152954), 'val_loss_max': np.float64(143207.958374), 'val_loss_bottom10%': np.float64(89868.152954), 'val_loss_top10%': np.float64(143207.958374), 'val_loss_cos1': np.float64(0.990439), 'val_loss_entropy': np.float64(2.293082)}} 2024-11-14 18:04:25,361 (server:353) INFO: Server: Starting evaluation at the end of round 40. 2024-11-14 18:04:25,362 (server:359) INFO: ----------- Starting a new training round (Round #41) ------------- 2024-11-14 18:06:33,672 (client:354) INFO: {'Role': 'Client #6', 'Round': 41, 'Results_raw': {'train_loss': 18.271666, 'val_loss': 19.326469, 'test_loss': 19.572623}} 2024-11-14 18:07:16,198 (client:354) INFO: {'Role': 'Client #4', 'Round': 41, 'Results_raw': {'train_loss': 24.83111, 'val_loss': 22.114339, 'test_loss': 23.194339}} 2024-11-14 18:08:00,056 (client:354) INFO: {'Role': 'Client #9', 'Round': 41, 'Results_raw': {'train_loss': 25.272003, 'val_loss': 29.408474, 'test_loss': 24.774497}} 2024-11-14 18:08:42,522 (client:354) INFO: {'Role': 'Client #8', 'Round': 41, 'Results_raw': {'train_loss': 23.040126, 'val_loss': 26.054943, 'test_loss': 22.071437}} 2024-11-14 18:09:26,549 (client:354) INFO: {'Role': 'Client #7', 'Round': 41, 'Results_raw': {'train_loss': 19.42444, 'val_loss': 19.332291, 'test_loss': 17.922992}} 2024-11-14 18:10:10,218 (client:354) INFO: {'Role': 'Client #1', 'Round': 41, 'Results_raw': {'train_loss': 31.918071, 'val_loss': 30.538952, 'test_loss': 30.201109}} 2024-11-14 18:10:53,806 (client:354) INFO: {'Role': 'Client #10', 'Round': 41, 'Results_raw': {'train_loss': 22.69466, 'val_loss': 21.965482, 'test_loss': 22.097625}} 2024-11-14 18:11:37,177 (client:354) INFO: {'Role': 'Client #3', 'Round': 41, 'Results_raw': {'train_loss': 26.594054, 'val_loss': 26.176178, 'test_loss': 26.137796}} 2024-11-14 18:12:18,943 (client:354) INFO: {'Role': 'Client #5', 'Round': 41, 'Results_raw': {'train_loss': 20.335005, 'val_loss': 20.224337, 'test_loss': 21.226542}} 2024-11-14 18:13:01,133 (client:354) INFO: {'Role': 'Client #2', 'Round': 41, 'Results_raw': {'train_loss': 23.5946, 'val_loss': 25.670678, 'test_loss': 24.301506}} 2024-11-14 18:13:01,136 (server:615) INFO: {'Role': 'Server #', 'Round': 40, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.87913), 'test_loss': np.float64(105174.537561), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.425784), 'val_loss': np.float64(110618.761212), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.87913), 'test_loss': np.float64(105174.537561), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.425784), 'val_loss': np.float64(110618.761212), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.313412), 'test_avg_loss_bottom_decile': np.float64(25.079591), 'test_avg_loss_top_decile': np.float64(39.760377), 'test_avg_loss_min': np.float64(23.947266), 'test_avg_loss_max': np.float64(39.760377), 'test_avg_loss_bottom10%': np.float64(23.947266), 'test_avg_loss_top10%': np.float64(39.760377), 'test_avg_loss_cos1': np.float64(0.98974), 'test_avg_loss_entropy': np.float64(2.292453), 'test_loss_std': np.float64(15183.209543), 'test_loss_bottom_decile': np.float64(88280.160828), 'test_loss_top_decile': np.float64(139956.528564), 'test_loss_min': np.float64(84294.375305), 'test_loss_max': np.float64(139956.528564), 'test_loss_bottom10%': np.float64(84294.375305), 'test_loss_top10%': np.float64(139956.528564), 'test_loss_cos1': np.float64(0.98974), 'test_loss_entropy': np.float64(2.292453), 'val_avg_loss_std': np.float64(4.49852), 'val_avg_loss_bottom_decile': np.float64(26.530903), 'val_avg_loss_top_decile': np.float64(40.801411), 'val_avg_loss_min': np.float64(25.319679), 'val_avg_loss_max': np.float64(40.801411), 'val_avg_loss_bottom10%': np.float64(25.319679), 'val_avg_loss_top10%': np.float64(40.801411), 'val_avg_loss_cos1': np.float64(0.989909), 'val_avg_loss_entropy': np.float64(2.292552), 'val_loss_std': np.float64(15834.791595), 'val_loss_bottom_decile': np.float64(93388.779663), 'val_loss_top_decile': np.float64(143620.966553), 'val_loss_min': np.float64(89125.271729), 'val_loss_max': np.float64(143620.966553), 'val_loss_bottom10%': np.float64(89125.271729), 'val_loss_top10%': np.float64(143620.966553), 'val_loss_cos1': np.float64(0.989909), 'val_loss_entropy': np.float64(2.292552)}} 2024-11-14 18:13:01,168 (server:353) INFO: Server: Starting evaluation at the end of round 41. 2024-11-14 18:13:01,169 (server:359) INFO: ----------- Starting a new training round (Round #42) ------------- 2024-11-14 18:15:08,026 (client:354) INFO: {'Role': 'Client #3', 'Round': 42, 'Results_raw': {'train_loss': 26.56646, 'val_loss': 26.109999, 'test_loss': 26.161543}} 2024-11-14 18:15:50,210 (client:354) INFO: {'Role': 'Client #6', 'Round': 42, 'Results_raw': {'train_loss': 18.204247, 'val_loss': 19.356685, 'test_loss': 19.618994}} 2024-11-14 18:16:30,097 (client:354) INFO: {'Role': 'Client #10', 'Round': 42, 'Results_raw': {'train_loss': 22.570268, 'val_loss': 21.628276, 'test_loss': 22.217442}} 2024-11-14 18:17:11,216 (client:354) INFO: {'Role': 'Client #8', 'Round': 42, 'Results_raw': {'train_loss': 22.977167, 'val_loss': 26.405405, 'test_loss': 22.156771}} 2024-11-14 18:17:53,597 (client:354) INFO: {'Role': 'Client #7', 'Round': 42, 'Results_raw': {'train_loss': 19.392823, 'val_loss': 19.467964, 'test_loss': 17.997347}} 2024-11-14 18:18:36,070 (client:354) INFO: {'Role': 'Client #4', 'Round': 42, 'Results_raw': {'train_loss': 24.893518, 'val_loss': 21.708619, 'test_loss': 22.481911}} 2024-11-14 18:19:16,563 (client:354) INFO: {'Role': 'Client #5', 'Round': 42, 'Results_raw': {'train_loss': 20.378203, 'val_loss': 20.133802, 'test_loss': 21.139478}} 2024-11-14 18:19:56,913 (client:354) INFO: {'Role': 'Client #2', 'Round': 42, 'Results_raw': {'train_loss': 23.509469, 'val_loss': 25.384695, 'test_loss': 24.239047}} 2024-11-14 18:20:37,465 (client:354) INFO: {'Role': 'Client #9', 'Round': 42, 'Results_raw': {'train_loss': 25.271397, 'val_loss': 29.208048, 'test_loss': 24.982611}} 2024-11-14 18:21:19,026 (client:354) INFO: {'Role': 'Client #1', 'Round': 42, 'Results_raw': {'train_loss': 31.724479, 'val_loss': 30.913516, 'test_loss': 30.323611}} 2024-11-14 18:21:19,029 (server:615) INFO: {'Role': 'Server #', 'Round': 41, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.949036), 'test_loss': np.float64(105420.606677), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.502832), 'val_loss': np.float64(110889.967932), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.949036), 'test_loss': np.float64(105420.606677), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.502832), 'val_loss': np.float64(110889.967932), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.296418), 'test_avg_loss_bottom_decile': np.float64(25.218582), 'test_avg_loss_top_decile': np.float64(39.751843), 'test_avg_loss_min': np.float64(23.923714), 'test_avg_loss_max': np.float64(39.751843), 'test_avg_loss_bottom10%': np.float64(23.923714), 'test_avg_loss_top10%': np.float64(39.751843), 'test_avg_loss_cos1': np.float64(0.989866), 'test_avg_loss_entropy': np.float64(2.292569), 'test_loss_std': np.float64(15123.391022), 'test_loss_bottom_decile': np.float64(88769.408752), 'test_loss_top_decile': np.float64(139926.487549), 'test_loss_min': np.float64(84211.471741), 'test_loss_max': np.float64(139926.487549), 'test_loss_bottom10%': np.float64(84211.471741), 'test_loss_top10%': np.float64(139926.487549), 'test_loss_cos1': np.float64(0.989866), 'test_loss_entropy': np.float64(2.292569), 'val_avg_loss_std': np.float64(4.488585), 'val_avg_loss_bottom_decile': np.float64(26.704854), 'val_avg_loss_top_decile': np.float64(40.773823), 'val_avg_loss_min': np.float64(25.2779), 'val_avg_loss_max': np.float64(40.773823), 'val_avg_loss_bottom10%': np.float64(25.2779), 'val_avg_loss_top10%': np.float64(40.773823), 'val_avg_loss_cos1': np.float64(0.990001), 'val_avg_loss_entropy': np.float64(2.292638), 'val_loss_std': np.float64(15799.820838), 'val_loss_bottom_decile': np.float64(94001.086426), 'val_loss_top_decile': np.float64(143523.85791), 'val_loss_min': np.float64(88978.208923), 'val_loss_max': np.float64(143523.85791), 'val_loss_bottom10%': np.float64(88978.208923), 'val_loss_top10%': np.float64(143523.85791), 'val_loss_cos1': np.float64(0.990001), 'val_loss_entropy': np.float64(2.292638)}} 2024-11-14 18:21:19,057 (server:353) INFO: Server: Starting evaluation at the end of round 42. 2024-11-14 18:21:19,058 (server:359) INFO: ----------- Starting a new training round (Round #43) ------------- 2024-11-14 18:23:26,993 (client:354) INFO: {'Role': 'Client #9', 'Round': 43, 'Results_raw': {'train_loss': 25.274892, 'val_loss': 29.298479, 'test_loss': 24.678001}} 2024-11-14 18:24:09,167 (client:354) INFO: {'Role': 'Client #3', 'Round': 43, 'Results_raw': {'train_loss': 26.514465, 'val_loss': 25.896802, 'test_loss': 26.049292}} 2024-11-14 18:24:48,480 (client:354) INFO: {'Role': 'Client #2', 'Round': 43, 'Results_raw': {'train_loss': 23.558365, 'val_loss': 25.190865, 'test_loss': 24.029058}} 2024-11-14 18:25:29,380 (client:354) INFO: {'Role': 'Client #1', 'Round': 43, 'Results_raw': {'train_loss': 31.680466, 'val_loss': 30.441436, 'test_loss': 29.888304}} 2024-11-14 18:26:11,517 (client:354) INFO: {'Role': 'Client #10', 'Round': 43, 'Results_raw': {'train_loss': 22.493701, 'val_loss': 21.754802, 'test_loss': 21.928853}} 2024-11-14 18:26:53,389 (client:354) INFO: {'Role': 'Client #6', 'Round': 43, 'Results_raw': {'train_loss': 18.212726, 'val_loss': 19.423765, 'test_loss': 19.832648}} 2024-11-14 18:27:35,602 (client:354) INFO: {'Role': 'Client #8', 'Round': 43, 'Results_raw': {'train_loss': 22.893161, 'val_loss': 25.731661, 'test_loss': 21.882644}} 2024-11-14 18:28:16,754 (client:354) INFO: {'Role': 'Client #7', 'Round': 43, 'Results_raw': {'train_loss': 19.384267, 'val_loss': 19.526607, 'test_loss': 18.066393}} 2024-11-14 18:28:58,148 (client:354) INFO: {'Role': 'Client #4', 'Round': 43, 'Results_raw': {'train_loss': 24.829665, 'val_loss': 21.767355, 'test_loss': 22.745785}} 2024-11-14 18:29:39,188 (client:354) INFO: {'Role': 'Client #5', 'Round': 43, 'Results_raw': {'train_loss': 20.369749, 'val_loss': 20.335778, 'test_loss': 22.455879}} 2024-11-14 18:29:39,192 (server:615) INFO: {'Role': 'Server #', 'Round': 42, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.625554), 'test_loss': np.float64(104281.949664), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.192098), 'val_loss': np.float64(109796.183807), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.625554), 'test_loss': np.float64(104281.949664), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.192098), 'val_loss': np.float64(109796.183807), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.283031), 'test_avg_loss_bottom_decile': np.float64(24.723691), 'test_avg_loss_top_decile': np.float64(39.450567), 'test_avg_loss_min': np.float64(23.74002), 'test_avg_loss_max': np.float64(39.450567), 'test_avg_loss_bottom10%': np.float64(23.74002), 'test_avg_loss_top10%': np.float64(39.450567), 'test_avg_loss_cos1': np.float64(0.98971), 'test_avg_loss_entropy': np.float64(2.292423), 'test_loss_std': np.float64(15076.270871), 'test_loss_bottom_decile': np.float64(87027.391846), 'test_loss_top_decile': np.float64(138865.996338), 'test_loss_min': np.float64(83564.871338), 'test_loss_max': np.float64(138865.996338), 'test_loss_bottom10%': np.float64(83564.871338), 'test_loss_top10%': np.float64(138865.996338), 'test_loss_cos1': np.float64(0.98971), 'test_loss_entropy': np.float64(2.292423), 'val_avg_loss_std': np.float64(4.445947), 'val_avg_loss_bottom_decile': np.float64(26.214645), 'val_avg_loss_top_decile': np.float64(40.444842), 'val_avg_loss_min': np.float64(25.132828), 'val_avg_loss_max': np.float64(40.444842), 'val_avg_loss_bottom10%': np.float64(25.132828), 'val_avg_loss_top10%': np.float64(40.444842), 'val_avg_loss_cos1': np.float64(0.989994), 'val_avg_loss_entropy': np.float64(2.292629), 'val_loss_std': np.float64(15649.734411), 'val_loss_bottom_decile': np.float64(92275.55127), 'val_loss_top_decile': np.float64(142365.844971), 'val_loss_min': np.float64(88467.55304), 'val_loss_max': np.float64(142365.844971), 'val_loss_bottom10%': np.float64(88467.55304), 'val_loss_top10%': np.float64(142365.844971), 'val_loss_cos1': np.float64(0.989994), 'val_loss_entropy': np.float64(2.292629)}} 2024-11-14 18:29:39,221 (server:353) INFO: Server: Starting evaluation at the end of round 43. 2024-11-14 18:29:39,222 (server:359) INFO: ----------- Starting a new training round (Round #44) ------------- 2024-11-14 18:31:46,079 (client:354) INFO: {'Role': 'Client #1', 'Round': 44, 'Results_raw': {'train_loss': 31.608483, 'val_loss': 30.76995, 'test_loss': 30.186417}} 2024-11-14 18:32:27,523 (client:354) INFO: {'Role': 'Client #8', 'Round': 44, 'Results_raw': {'train_loss': 22.894519, 'val_loss': 27.088856, 'test_loss': 23.311632}} 2024-11-14 18:33:07,514 (client:354) INFO: {'Role': 'Client #4', 'Round': 44, 'Results_raw': {'train_loss': 24.772961, 'val_loss': 21.868257, 'test_loss': 22.518798}} 2024-11-14 18:33:49,506 (client:354) INFO: {'Role': 'Client #7', 'Round': 44, 'Results_raw': {'train_loss': 19.35886, 'val_loss': 19.413527, 'test_loss': 18.02302}} 2024-11-14 18:34:31,564 (client:354) INFO: {'Role': 'Client #6', 'Round': 44, 'Results_raw': {'train_loss': 18.188181, 'val_loss': 19.356191, 'test_loss': 19.835437}} 2024-11-14 18:35:13,762 (client:354) INFO: {'Role': 'Client #5', 'Round': 44, 'Results_raw': {'train_loss': 20.291652, 'val_loss': 20.243002, 'test_loss': 23.154235}} 2024-11-14 18:35:55,605 (client:354) INFO: {'Role': 'Client #10', 'Round': 44, 'Results_raw': {'train_loss': 22.573581, 'val_loss': 21.686508, 'test_loss': 21.86656}} 2024-11-14 18:36:37,566 (client:354) INFO: {'Role': 'Client #9', 'Round': 44, 'Results_raw': {'train_loss': 25.231158, 'val_loss': 29.767395, 'test_loss': 24.743037}} 2024-11-14 18:37:19,552 (client:354) INFO: {'Role': 'Client #2', 'Round': 44, 'Results_raw': {'train_loss': 23.467342, 'val_loss': 25.430283, 'test_loss': 24.386359}} 2024-11-14 18:38:00,674 (client:354) INFO: {'Role': 'Client #3', 'Round': 44, 'Results_raw': {'train_loss': 26.487217, 'val_loss': 26.013344, 'test_loss': 26.157831}} 2024-11-14 18:38:00,678 (server:615) INFO: {'Role': 'Server #', 'Round': 43, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.804805), 'test_loss': np.float64(104912.913879), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.370417), 'val_loss': np.float64(110423.867847), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.804805), 'test_loss': np.float64(104912.913879), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.370417), 'val_loss': np.float64(110423.867847), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.34432), 'test_avg_loss_bottom_decile': np.float64(25.1798), 'test_avg_loss_top_decile': np.float64(39.860432), 'test_avg_loss_min': np.float64(23.779493), 'test_avg_loss_max': np.float64(39.860432), 'test_avg_loss_bottom10%': np.float64(23.779493), 'test_avg_loss_top10%': np.float64(39.860432), 'test_avg_loss_cos1': np.float64(0.989543), 'test_avg_loss_entropy': np.float64(2.292281), 'test_loss_std': np.float64(15292.007151), 'test_loss_bottom_decile': np.float64(88632.894958), 'test_loss_top_decile': np.float64(140308.718994), 'test_loss_min': np.float64(83703.816467), 'test_loss_max': np.float64(140308.718994), 'test_loss_bottom10%': np.float64(83703.816467), 'test_loss_top10%': np.float64(140308.718994), 'test_loss_cos1': np.float64(0.989543), 'test_loss_entropy': np.float64(2.292281), 'val_avg_loss_std': np.float64(4.534773), 'val_avg_loss_bottom_decile': np.float64(26.667607), 'val_avg_loss_top_decile': np.float64(40.907341), 'val_avg_loss_min': np.float64(25.144618), 'val_avg_loss_max': np.float64(40.907341), 'val_avg_loss_bottom10%': np.float64(25.144618), 'val_avg_loss_top10%': np.float64(40.907341), 'val_avg_loss_cos1': np.float64(0.989713), 'val_avg_loss_entropy': np.float64(2.292372), 'val_loss_std': np.float64(15962.399476), 'val_loss_bottom_decile': np.float64(93869.975281), 'val_loss_top_decile': np.float64(143993.840698), 'val_loss_min': np.float64(88509.055725), 'val_loss_max': np.float64(143993.840698), 'val_loss_bottom10%': np.float64(88509.055725), 'val_loss_top10%': np.float64(143993.840698), 'val_loss_cos1': np.float64(0.989713), 'val_loss_entropy': np.float64(2.292372)}} 2024-11-14 18:38:00,715 (server:353) INFO: Server: Starting evaluation at the end of round 44. 2024-11-14 18:38:00,715 (server:359) INFO: ----------- Starting a new training round (Round #45) ------------- 2024-11-14 18:40:11,807 (client:354) INFO: {'Role': 'Client #4', 'Round': 45, 'Results_raw': {'train_loss': 24.670246, 'val_loss': 22.070998, 'test_loss': 22.968528}} 2024-11-14 18:40:53,719 (client:354) INFO: {'Role': 'Client #7', 'Round': 45, 'Results_raw': {'train_loss': 19.335715, 'val_loss': 19.443618, 'test_loss': 18.167239}} 2024-11-14 18:41:36,437 (client:354) INFO: {'Role': 'Client #5', 'Round': 45, 'Results_raw': {'train_loss': 20.257073, 'val_loss': 20.811845, 'test_loss': 22.644837}} 2024-11-14 18:42:19,185 (client:354) INFO: {'Role': 'Client #10', 'Round': 45, 'Results_raw': {'train_loss': 22.490579, 'val_loss': 21.66237, 'test_loss': 22.125269}} 2024-11-14 18:43:04,186 (client:354) INFO: {'Role': 'Client #9', 'Round': 45, 'Results_raw': {'train_loss': 25.170889, 'val_loss': 29.225193, 'test_loss': 24.93354}} 2024-11-14 18:43:48,983 (client:354) INFO: {'Role': 'Client #2', 'Round': 45, 'Results_raw': {'train_loss': 23.475748, 'val_loss': 25.336156, 'test_loss': 24.193961}} 2024-11-14 18:44:30,711 (client:354) INFO: {'Role': 'Client #1', 'Round': 45, 'Results_raw': {'train_loss': 31.610396, 'val_loss': 30.772623, 'test_loss': 30.272938}} 2024-11-14 18:45:12,321 (client:354) INFO: {'Role': 'Client #8', 'Round': 45, 'Results_raw': {'train_loss': 22.838969, 'val_loss': 26.569529, 'test_loss': 22.209954}} 2024-11-14 18:45:54,366 (client:354) INFO: {'Role': 'Client #6', 'Round': 45, 'Results_raw': {'train_loss': 18.218181, 'val_loss': 19.571924, 'test_loss': 19.831587}} 2024-11-14 18:46:35,502 (client:354) INFO: {'Role': 'Client #3', 'Round': 45, 'Results_raw': {'train_loss': 26.533662, 'val_loss': 26.095111, 'test_loss': 26.053813}} 2024-11-14 18:46:35,504 (server:615) INFO: {'Role': 'Server #', 'Round': 44, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.718224), 'test_loss': np.float64(104608.148676), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.279787), 'val_loss': np.float64(110104.851465), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.718224), 'test_loss': np.float64(104608.148676), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.279787), 'val_loss': np.float64(110104.851465), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.374623), 'test_avg_loss_bottom_decile': np.float64(24.953744), 'test_avg_loss_top_decile': np.float64(39.808359), 'test_avg_loss_min': np.float64(23.629064), 'test_avg_loss_max': np.float64(39.808359), 'test_avg_loss_bottom10%': np.float64(23.629064), 'test_avg_loss_top10%': np.float64(39.808359), 'test_avg_loss_cos1': np.float64(0.989339), 'test_avg_loss_entropy': np.float64(2.292066), 'test_loss_std': np.float64(15398.672709), 'test_loss_bottom_decile': np.float64(87837.179199), 'test_loss_top_decile': np.float64(140125.424927), 'test_loss_min': np.float64(83174.305603), 'test_loss_max': np.float64(140125.424927), 'test_loss_bottom10%': np.float64(83174.305603), 'test_loss_top10%': np.float64(140125.424927), 'test_loss_cos1': np.float64(0.989339), 'test_loss_entropy': np.float64(2.292066), 'val_avg_loss_std': np.float64(4.559736), 'val_avg_loss_bottom_decile': np.float64(26.454688), 'val_avg_loss_top_decile': np.float64(40.820252), 'val_avg_loss_min': np.float64(25.013624), 'val_avg_loss_max': np.float64(40.820252), 'val_avg_loss_bottom10%': np.float64(25.013624), 'val_avg_loss_top10%': np.float64(40.820252), 'val_avg_loss_cos1': np.float64(0.989542), 'val_avg_loss_entropy': np.float64(2.292188), 'val_loss_std': np.float64(16050.270571), 'val_loss_bottom_decile': np.float64(93120.502808), 'val_loss_top_decile': np.float64(143687.287109), 'val_loss_min': np.float64(88047.957397), 'val_loss_max': np.float64(143687.287109), 'val_loss_bottom10%': np.float64(88047.957397), 'val_loss_top10%': np.float64(143687.287109), 'val_loss_cos1': np.float64(0.989542), 'val_loss_entropy': np.float64(2.292188)}} 2024-11-14 18:46:35,534 (server:353) INFO: Server: Starting evaluation at the end of round 45. 2024-11-14 18:46:35,535 (server:359) INFO: ----------- Starting a new training round (Round #46) ------------- 2024-11-14 18:48:44,722 (client:354) INFO: {'Role': 'Client #10', 'Round': 46, 'Results_raw': {'train_loss': 22.486736, 'val_loss': 21.665481, 'test_loss': 21.964975}} 2024-11-14 18:49:24,615 (client:354) INFO: {'Role': 'Client #8', 'Round': 46, 'Results_raw': {'train_loss': 22.823196, 'val_loss': 26.469785, 'test_loss': 21.634241}} 2024-11-14 18:50:06,772 (client:354) INFO: {'Role': 'Client #4', 'Round': 46, 'Results_raw': {'train_loss': 24.689765, 'val_loss': 21.981791, 'test_loss': 23.086329}} 2024-11-14 18:50:48,954 (client:354) INFO: {'Role': 'Client #5', 'Round': 46, 'Results_raw': {'train_loss': 20.266521, 'val_loss': 20.325277, 'test_loss': 21.643484}} 2024-11-14 18:51:31,121 (client:354) INFO: {'Role': 'Client #3', 'Round': 46, 'Results_raw': {'train_loss': 26.495265, 'val_loss': 26.108513, 'test_loss': 26.280826}} 2024-11-14 18:52:12,812 (client:354) INFO: {'Role': 'Client #2', 'Round': 46, 'Results_raw': {'train_loss': 23.496682, 'val_loss': 25.513733, 'test_loss': 24.315092}} 2024-11-14 18:52:54,499 (client:354) INFO: {'Role': 'Client #6', 'Round': 46, 'Results_raw': {'train_loss': 18.25314, 'val_loss': 19.598191, 'test_loss': 20.013408}} 2024-11-14 18:53:37,819 (client:354) INFO: {'Role': 'Client #7', 'Round': 46, 'Results_raw': {'train_loss': 19.33885, 'val_loss': 19.321829, 'test_loss': 17.843493}} 2024-11-14 18:54:19,005 (client:354) INFO: {'Role': 'Client #1', 'Round': 46, 'Results_raw': {'train_loss': 31.625056, 'val_loss': 30.361484, 'test_loss': 29.879184}} 2024-11-14 18:55:01,509 (client:354) INFO: {'Role': 'Client #9', 'Round': 46, 'Results_raw': {'train_loss': 25.057378, 'val_loss': 30.073327, 'test_loss': 25.084484}} 2024-11-14 18:55:01,512 (server:615) INFO: {'Role': 'Server #', 'Round': 45, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.759461), 'test_loss': np.float64(104753.303918), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.299128), 'val_loss': np.float64(110172.929156), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.759461), 'test_loss': np.float64(104753.303918), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.299128), 'val_loss': np.float64(110172.929156), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.222109), 'test_avg_loss_bottom_decile': np.float64(25.292533), 'test_avg_loss_top_decile': np.float64(39.453999), 'test_avg_loss_min': np.float64(23.971347), 'test_avg_loss_max': np.float64(39.453999), 'test_avg_loss_bottom10%': np.float64(23.971347), 'test_avg_loss_top10%': np.float64(39.453999), 'test_avg_loss_cos1': np.float64(0.990085), 'test_avg_loss_entropy': np.float64(2.292811), 'test_loss_std': np.float64(14861.824665), 'test_loss_bottom_decile': np.float64(89029.716797), 'test_loss_top_decile': np.float64(138878.075928), 'test_loss_min': np.float64(84379.143005), 'test_loss_max': np.float64(138878.075928), 'test_loss_bottom10%': np.float64(84379.143005), 'test_loss_top10%': np.float64(138878.075928), 'test_loss_cos1': np.float64(0.990085), 'test_loss_entropy': np.float64(2.292811), 'val_avg_loss_std': np.float64(4.408297), 'val_avg_loss_bottom_decile': np.float64(26.7372), 'val_avg_loss_top_decile': np.float64(40.441652), 'val_avg_loss_min': np.float64(25.328317), 'val_avg_loss_max': np.float64(40.441652), 'val_avg_loss_bottom10%': np.float64(25.328317), 'val_avg_loss_top10%': np.float64(40.441652), 'val_avg_loss_cos1': np.float64(0.990227), 'val_avg_loss_entropy': np.float64(2.292885), 'val_loss_std': np.float64(15517.205347), 'val_loss_bottom_decile': np.float64(94114.945435), 'val_loss_top_decile': np.float64(142354.613892), 'val_loss_min': np.float64(89155.675293), 'val_loss_max': np.float64(142354.613892), 'val_loss_bottom10%': np.float64(89155.675293), 'val_loss_top10%': np.float64(142354.613892), 'val_loss_cos1': np.float64(0.990227), 'val_loss_entropy': np.float64(2.292885)}} 2024-11-14 18:55:01,550 (server:353) INFO: Server: Starting evaluation at the end of round 46. 2024-11-14 18:55:01,551 (server:359) INFO: ----------- Starting a new training round (Round #47) ------------- 2024-11-14 18:57:06,671 (client:354) INFO: {'Role': 'Client #7', 'Round': 47, 'Results_raw': {'train_loss': 19.290829, 'val_loss': 19.402397, 'test_loss': 18.145678}} 2024-11-14 18:57:47,979 (client:354) INFO: {'Role': 'Client #5', 'Round': 47, 'Results_raw': {'train_loss': 20.145897, 'val_loss': 20.294459, 'test_loss': 22.243854}} 2024-11-14 18:58:30,101 (client:354) INFO: {'Role': 'Client #3', 'Round': 47, 'Results_raw': {'train_loss': 26.471835, 'val_loss': 26.120524, 'test_loss': 26.190894}} 2024-11-14 18:59:12,236 (client:354) INFO: {'Role': 'Client #8', 'Round': 47, 'Results_raw': {'train_loss': 22.808187, 'val_loss': 26.065676, 'test_loss': 22.005813}} 2024-11-14 18:59:54,525 (client:354) INFO: {'Role': 'Client #2', 'Round': 47, 'Results_raw': {'train_loss': 23.46132, 'val_loss': 25.481853, 'test_loss': 24.378056}} 2024-11-14 19:00:37,084 (client:354) INFO: {'Role': 'Client #4', 'Round': 47, 'Results_raw': {'train_loss': 24.72582, 'val_loss': 22.390484, 'test_loss': 23.390813}} 2024-11-14 19:01:19,353 (client:354) INFO: {'Role': 'Client #1', 'Round': 47, 'Results_raw': {'train_loss': 31.621395, 'val_loss': 30.504359, 'test_loss': 30.185949}} 2024-11-14 19:02:00,992 (client:354) INFO: {'Role': 'Client #10', 'Round': 47, 'Results_raw': {'train_loss': 22.499137, 'val_loss': 21.746561, 'test_loss': 22.007005}} 2024-11-14 19:02:42,794 (client:354) INFO: {'Role': 'Client #9', 'Round': 47, 'Results_raw': {'train_loss': 25.073416, 'val_loss': 29.179088, 'test_loss': 25.074736}} 2024-11-14 19:03:24,706 (client:354) INFO: {'Role': 'Client #6', 'Round': 47, 'Results_raw': {'train_loss': 18.180355, 'val_loss': 19.425064, 'test_loss': 19.97785}} 2024-11-14 19:03:24,710 (server:615) INFO: {'Role': 'Server #', 'Round': 46, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.705635), 'test_loss': np.float64(104563.835547), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.250286), 'val_loss': np.float64(110001.00636), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.705635), 'test_loss': np.float64(104563.835547), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.250286), 'val_loss': np.float64(110001.00636), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.167353), 'test_avg_loss_bottom_decile': np.float64(25.450956), 'test_avg_loss_top_decile': np.float64(39.333651), 'test_avg_loss_min': np.float64(24.057304), 'test_avg_loss_max': np.float64(39.333651), 'test_avg_loss_bottom10%': np.float64(24.057304), 'test_avg_loss_top10%': np.float64(39.333651), 'test_avg_loss_cos1': np.float64(0.990303), 'test_avg_loss_entropy': np.float64(2.293042), 'test_loss_std': np.float64(14669.080873), 'test_loss_bottom_decile': np.float64(89587.365906), 'test_loss_top_decile': np.float64(138454.452393), 'test_loss_min': np.float64(84681.711792), 'test_loss_max': np.float64(138454.452393), 'test_loss_bottom10%': np.float64(84681.711792), 'test_loss_top10%': np.float64(138454.452393), 'test_loss_cos1': np.float64(0.990303), 'test_loss_entropy': np.float64(2.293042), 'val_avg_loss_std': np.float64(4.337685), 'val_avg_loss_bottom_decile': np.float64(26.947512), 'val_avg_loss_top_decile': np.float64(40.258538), 'val_avg_loss_min': np.float64(25.419656), 'val_avg_loss_max': np.float64(40.258538), 'val_avg_loss_bottom10%': np.float64(25.419656), 'val_avg_loss_top10%': np.float64(40.258538), 'val_avg_loss_cos1': np.float64(0.990504), 'val_avg_loss_entropy': np.float64(2.293169), 'val_loss_std': np.float64(15268.650288), 'val_loss_bottom_decile': np.float64(94855.240662), 'val_loss_top_decile': np.float64(141710.053345), 'val_loss_min': np.float64(89477.190063), 'val_loss_max': np.float64(141710.053345), 'val_loss_bottom10%': np.float64(89477.190063), 'val_loss_top10%': np.float64(141710.053345), 'val_loss_cos1': np.float64(0.990504), 'val_loss_entropy': np.float64(2.293169)}} 2024-11-14 19:03:24,746 (server:353) INFO: Server: Starting evaluation at the end of round 47. 2024-11-14 19:03:24,747 (server:359) INFO: ----------- Starting a new training round (Round #48) ------------- 2024-11-14 19:05:36,126 (client:354) INFO: {'Role': 'Client #7', 'Round': 48, 'Results_raw': {'train_loss': 19.280722, 'val_loss': 19.296463, 'test_loss': 17.906614}} 2024-11-14 19:06:17,715 (client:354) INFO: {'Role': 'Client #4', 'Round': 48, 'Results_raw': {'train_loss': 24.716392, 'val_loss': 21.874541, 'test_loss': 22.649049}} 2024-11-14 19:06:59,609 (client:354) INFO: {'Role': 'Client #8', 'Round': 48, 'Results_raw': {'train_loss': 22.787202, 'val_loss': 25.891805, 'test_loss': 21.972911}} 2024-11-14 19:07:41,360 (client:354) INFO: {'Role': 'Client #5', 'Round': 48, 'Results_raw': {'train_loss': 20.123399, 'val_loss': 20.044465, 'test_loss': 21.592863}} 2024-11-14 19:08:23,752 (client:354) INFO: {'Role': 'Client #6', 'Round': 48, 'Results_raw': {'train_loss': 18.193433, 'val_loss': 19.204933, 'test_loss': 19.326348}} 2024-11-14 19:09:04,870 (client:354) INFO: {'Role': 'Client #9', 'Round': 48, 'Results_raw': {'train_loss': 25.087948, 'val_loss': 30.080987, 'test_loss': 25.038419}} 2024-11-14 19:09:46,434 (client:354) INFO: {'Role': 'Client #2', 'Round': 48, 'Results_raw': {'train_loss': 23.380394, 'val_loss': 25.227629, 'test_loss': 24.191216}} 2024-11-14 19:10:28,577 (client:354) INFO: {'Role': 'Client #10', 'Round': 48, 'Results_raw': {'train_loss': 22.441853, 'val_loss': 21.595103, 'test_loss': 21.764806}} 2024-11-14 19:11:11,635 (client:354) INFO: {'Role': 'Client #3', 'Round': 48, 'Results_raw': {'train_loss': 26.357705, 'val_loss': 26.065474, 'test_loss': 26.230051}} 2024-11-14 19:11:55,416 (client:354) INFO: {'Role': 'Client #1', 'Round': 48, 'Results_raw': {'train_loss': 31.562604, 'val_loss': 30.369484, 'test_loss': 30.216292}} 2024-11-14 19:11:55,419 (server:615) INFO: {'Role': 'Server #', 'Round': 47, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.70124), 'test_loss': np.float64(104548.364801), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.239311), 'val_loss': np.float64(109962.375208), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.70124), 'test_loss': np.float64(104548.364801), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.239311), 'val_loss': np.float64(109962.375208), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.27083), 'test_avg_loss_bottom_decile': np.float64(24.843656), 'test_avg_loss_top_decile': np.float64(39.3882), 'test_avg_loss_min': np.float64(23.686001), 'test_avg_loss_max': np.float64(39.3882), 'test_avg_loss_bottom10%': np.float64(23.686001), 'test_avg_loss_top10%': np.float64(39.3882), 'test_avg_loss_cos1': np.float64(0.989819), 'test_avg_loss_entropy': np.float64(2.292508), 'test_loss_std': np.float64(15033.321307), 'test_loss_bottom_decile': np.float64(87449.670166), 'test_loss_top_decile': np.float64(138646.464111), 'test_loss_min': np.float64(83374.721863), 'test_loss_max': np.float64(138646.464111), 'test_loss_bottom10%': np.float64(83374.721863), 'test_loss_top10%': np.float64(138646.464111), 'test_loss_cos1': np.float64(0.989819), 'test_loss_entropy': np.float64(2.292508), 'val_avg_loss_std': np.float64(4.447748), 'val_avg_loss_bottom_decile': np.float64(26.31993), 'val_avg_loss_top_decile': np.float64(40.327206), 'val_avg_loss_min': np.float64(25.062382), 'val_avg_loss_max': np.float64(40.327206), 'val_avg_loss_bottom10%': np.float64(25.062382), 'val_avg_loss_top10%': np.float64(40.327206), 'val_avg_loss_cos1': np.float64(0.990016), 'val_avg_loss_entropy': np.float64(2.292639), 'val_loss_std': np.float64(15656.071535), 'val_loss_bottom_decile': np.float64(92646.152649), 'val_loss_top_decile': np.float64(141951.765625), 'val_loss_min': np.float64(88219.585754), 'val_loss_max': np.float64(141951.765625), 'val_loss_bottom10%': np.float64(88219.585754), 'val_loss_top10%': np.float64(141951.765625), 'val_loss_cos1': np.float64(0.990016), 'val_loss_entropy': np.float64(2.292639)}} 2024-11-14 19:11:55,461 (server:353) INFO: Server: Starting evaluation at the end of round 48. 2024-11-14 19:11:55,461 (server:359) INFO: ----------- Starting a new training round (Round #49) ------------- 2024-11-14 19:14:02,138 (client:354) INFO: {'Role': 'Client #10', 'Round': 49, 'Results_raw': {'train_loss': 22.402085, 'val_loss': 21.969218, 'test_loss': 22.448843}} 2024-11-14 19:14:44,032 (client:354) INFO: {'Role': 'Client #3', 'Round': 49, 'Results_raw': {'train_loss': 26.351864, 'val_loss': 26.291932, 'test_loss': 26.416062}} 2024-11-14 19:15:26,425 (client:354) INFO: {'Role': 'Client #2', 'Round': 49, 'Results_raw': {'train_loss': 23.397075, 'val_loss': 25.372485, 'test_loss': 24.154399}} 2024-11-14 19:16:08,976 (client:354) INFO: {'Role': 'Client #6', 'Round': 49, 'Results_raw': {'train_loss': 18.200472, 'val_loss': 19.491504, 'test_loss': 19.354707}} 2024-11-14 19:16:51,735 (client:354) INFO: {'Role': 'Client #7', 'Round': 49, 'Results_raw': {'train_loss': 19.3673, 'val_loss': 19.197694, 'test_loss': 17.813217}} 2024-11-14 19:17:34,118 (client:354) INFO: {'Role': 'Client #4', 'Round': 49, 'Results_raw': {'train_loss': 24.621477, 'val_loss': 21.26921, 'test_loss': 22.006678}} 2024-11-14 19:18:15,334 (client:354) INFO: {'Role': 'Client #5', 'Round': 49, 'Results_raw': {'train_loss': 20.142146, 'val_loss': 20.180001, 'test_loss': 22.848034}} 2024-11-14 19:18:56,670 (client:354) INFO: {'Role': 'Client #9', 'Round': 49, 'Results_raw': {'train_loss': 25.077257, 'val_loss': 29.793102, 'test_loss': 25.280637}} 2024-11-14 19:19:39,436 (client:354) INFO: {'Role': 'Client #8', 'Round': 49, 'Results_raw': {'train_loss': 22.693917, 'val_loss': 25.996413, 'test_loss': 21.898109}} 2024-11-14 19:20:21,450 (client:354) INFO: {'Role': 'Client #1', 'Round': 49, 'Results_raw': {'train_loss': 31.44646, 'val_loss': 30.54242, 'test_loss': 29.894334}} 2024-11-14 19:20:21,453 (server:615) INFO: {'Role': 'Server #', 'Round': 48, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.647143), 'test_loss': np.float64(104357.944434), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.169627), 'val_loss': np.float64(109717.085321), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.647143), 'test_loss': np.float64(104357.944434), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.169627), 'val_loss': np.float64(109717.085321), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.322702), 'test_avg_loss_bottom_decile': np.float64(24.9178), 'test_avg_loss_top_decile': np.float64(39.632956), 'test_avg_loss_min': np.float64(23.740132), 'test_avg_loss_max': np.float64(39.632956), 'test_avg_loss_bottom10%': np.float64(23.740132), 'test_avg_loss_top10%': np.float64(39.632956), 'test_avg_loss_cos1': np.float64(0.989537), 'test_avg_loss_entropy': np.float64(2.292269), 'test_loss_std': np.float64(15215.909474), 'test_loss_bottom_decile': np.float64(87710.655945), 'test_loss_top_decile': np.float64(139508.005981), 'test_loss_min': np.float64(83565.264221), 'test_loss_max': np.float64(139508.005981), 'test_loss_bottom10%': np.float64(83565.264221), 'test_loss_top10%': np.float64(139508.005981), 'test_loss_cos1': np.float64(0.989537), 'test_loss_entropy': np.float64(2.292269), 'val_avg_loss_std': np.float64(4.510888), 'val_avg_loss_bottom_decile': np.float64(26.387132), 'val_avg_loss_top_decile': np.float64(40.592457), 'val_avg_loss_min': np.float64(25.05808), 'val_avg_loss_max': np.float64(40.592457), 'val_avg_loss_bottom10%': np.float64(25.05808), 'val_avg_loss_top10%': np.float64(40.592457), 'val_avg_loss_cos1': np.float64(0.98969), 'val_avg_loss_entropy': np.float64(2.292339), 'val_loss_std': np.float64(15878.327003), 'val_loss_bottom_decile': np.float64(92882.703735), 'val_loss_top_decile': np.float64(142885.450317), 'val_loss_min': np.float64(88204.441895), 'val_loss_max': np.float64(142885.450317), 'val_loss_bottom10%': np.float64(88204.441895), 'val_loss_top10%': np.float64(142885.450317), 'val_loss_cos1': np.float64(0.98969), 'val_loss_entropy': np.float64(2.292339)}} 2024-11-14 19:20:21,488 (server:353) INFO: Server: Starting evaluation at the end of round 49. 2024-11-14 19:20:21,488 (server:359) INFO: ----------- Starting a new training round (Round #50) ------------- 2024-11-14 19:22:26,767 (client:354) INFO: {'Role': 'Client #8', 'Round': 50, 'Results_raw': {'train_loss': 22.683904, 'val_loss': 27.997954, 'test_loss': 22.954596}} 2024-11-14 19:23:08,038 (client:354) INFO: {'Role': 'Client #1', 'Round': 50, 'Results_raw': {'train_loss': 31.473896, 'val_loss': 30.652881, 'test_loss': 30.05007}} 2024-11-14 19:23:49,532 (client:354) INFO: {'Role': 'Client #9', 'Round': 50, 'Results_raw': {'train_loss': 24.978228, 'val_loss': 29.469251, 'test_loss': 25.08639}} 2024-11-14 19:24:31,188 (client:354) INFO: {'Role': 'Client #10', 'Round': 50, 'Results_raw': {'train_loss': 22.433752, 'val_loss': 21.841656, 'test_loss': 21.97224}} 2024-11-14 19:25:13,460 (client:354) INFO: {'Role': 'Client #3', 'Round': 50, 'Results_raw': {'train_loss': 26.424923, 'val_loss': 26.034457, 'test_loss': 26.129376}} 2024-11-14 19:25:55,634 (client:354) INFO: {'Role': 'Client #5', 'Round': 50, 'Results_raw': {'train_loss': 20.117883, 'val_loss': 20.288252, 'test_loss': 22.187176}} 2024-11-14 19:26:37,128 (client:354) INFO: {'Role': 'Client #7', 'Round': 50, 'Results_raw': {'train_loss': 19.202415, 'val_loss': 19.26165, 'test_loss': 17.910985}} 2024-11-14 19:27:18,370 (client:354) INFO: {'Role': 'Client #2', 'Round': 50, 'Results_raw': {'train_loss': 23.337796, 'val_loss': 25.207788, 'test_loss': 24.112277}} 2024-11-14 19:28:00,218 (client:354) INFO: {'Role': 'Client #6', 'Round': 50, 'Results_raw': {'train_loss': 18.152492, 'val_loss': 19.65296, 'test_loss': 19.603147}} 2024-11-14 19:28:42,392 (client:354) INFO: {'Role': 'Client #4', 'Round': 50, 'Results_raw': {'train_loss': 24.693839, 'val_loss': 21.581379, 'test_loss': 22.98157}} 2024-11-14 19:28:42,395 (server:615) INFO: {'Role': 'Server #', 'Round': 49, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.556041), 'test_loss': np.float64(104037.262915), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.102507), 'val_loss': np.float64(109480.823993), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.556041), 'test_loss': np.float64(104037.262915), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.102507), 'val_loss': np.float64(109480.823993), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.373971), 'test_avg_loss_bottom_decile': np.float64(24.695692), 'test_avg_loss_top_decile': np.float64(39.614234), 'test_avg_loss_min': np.float64(23.399274), 'test_avg_loss_max': np.float64(39.614234), 'test_avg_loss_bottom10%': np.float64(23.399274), 'test_avg_loss_top10%': np.float64(39.614234), 'test_avg_loss_cos1': np.float64(0.989226), 'test_avg_loss_entropy': np.float64(2.291942), 'test_loss_std': np.float64(15396.378676), 'test_loss_bottom_decile': np.float64(86928.834473), 'test_loss_top_decile': np.float64(139442.102417), 'test_loss_min': np.float64(82365.446167), 'test_loss_max': np.float64(139442.102417), 'test_loss_bottom10%': np.float64(82365.446167), 'test_loss_top10%': np.float64(139442.102417), 'test_loss_cos1': np.float64(0.989226), 'test_loss_entropy': np.float64(2.291942), 'val_avg_loss_std': np.float64(4.56289), 'val_avg_loss_bottom_decile': np.float64(26.156298), 'val_avg_loss_top_decile': np.float64(40.531943), 'val_avg_loss_min': np.float64(24.713719), 'val_avg_loss_max': np.float64(40.531943), 'val_avg_loss_bottom10%': np.float64(24.713719), 'val_avg_loss_top10%': np.float64(40.531943), 'val_avg_loss_cos1': np.float64(0.989409), 'val_avg_loss_entropy': np.float64(2.292033), 'val_loss_std': np.float64(16061.372556), 'val_loss_bottom_decile': np.float64(92070.169495), 'val_loss_top_decile': np.float64(142672.438599), 'val_loss_min': np.float64(86992.291687), 'val_loss_max': np.float64(142672.438599), 'val_loss_bottom10%': np.float64(86992.291687), 'val_loss_top10%': np.float64(142672.438599), 'val_loss_cos1': np.float64(0.989409), 'val_loss_entropy': np.float64(2.292033)}} 2024-11-14 19:28:42,430 (server:353) INFO: Server: Starting evaluation at the end of round 50. 2024-11-14 19:28:42,431 (server:359) INFO: ----------- Starting a new training round (Round #51) ------------- 2024-11-14 19:30:45,589 (client:354) INFO: {'Role': 'Client #8', 'Round': 51, 'Results_raw': {'train_loss': 22.672873, 'val_loss': 27.042815, 'test_loss': 22.425087}} 2024-11-14 19:31:27,479 (client:354) INFO: {'Role': 'Client #1', 'Round': 51, 'Results_raw': {'train_loss': 31.394566, 'val_loss': 30.377543, 'test_loss': 29.927421}} 2024-11-14 19:32:09,920 (client:354) INFO: {'Role': 'Client #4', 'Round': 51, 'Results_raw': {'train_loss': 24.677448, 'val_loss': 21.409127, 'test_loss': 22.118864}} 2024-11-14 19:32:54,330 (client:354) INFO: {'Role': 'Client #10', 'Round': 51, 'Results_raw': {'train_loss': 22.413347, 'val_loss': 21.802224, 'test_loss': 22.002969}} 2024-11-14 19:33:37,073 (client:354) INFO: {'Role': 'Client #3', 'Round': 51, 'Results_raw': {'train_loss': 26.420305, 'val_loss': 25.964956, 'test_loss': 26.105827}} 2024-11-14 19:34:20,275 (client:354) INFO: {'Role': 'Client #2', 'Round': 51, 'Results_raw': {'train_loss': 23.354213, 'val_loss': 25.776838, 'test_loss': 24.618932}} 2024-11-14 19:35:03,232 (client:354) INFO: {'Role': 'Client #7', 'Round': 51, 'Results_raw': {'train_loss': 19.241763, 'val_loss': 19.241484, 'test_loss': 17.744652}} 2024-11-14 19:35:46,775 (client:354) INFO: {'Role': 'Client #5', 'Round': 51, 'Results_raw': {'train_loss': 20.137377, 'val_loss': 20.026471, 'test_loss': 21.486747}} 2024-11-14 19:36:29,930 (client:354) INFO: {'Role': 'Client #9', 'Round': 51, 'Results_raw': {'train_loss': 24.977391, 'val_loss': 29.478353, 'test_loss': 25.086424}} 2024-11-14 19:37:12,882 (client:354) INFO: {'Role': 'Client #6', 'Round': 51, 'Results_raw': {'train_loss': 18.118742, 'val_loss': 19.124486, 'test_loss': 19.475632}} 2024-11-14 19:37:12,893 (server:615) INFO: {'Role': 'Server #', 'Round': 50, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.342888), 'test_loss': np.float64(103286.96723), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.897779), 'val_loss': np.float64(108760.180878), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.342888), 'test_loss': np.float64(103286.96723), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.897779), 'val_loss': np.float64(108760.180878), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.312934), 'test_avg_loss_bottom_decile': np.float64(24.555601), 'test_avg_loss_top_decile': np.float64(39.225838), 'test_avg_loss_min': np.float64(23.348347), 'test_avg_loss_max': np.float64(39.225838), 'test_avg_loss_bottom10%': np.float64(23.348347), 'test_avg_loss_top10%': np.float64(39.225838), 'test_avg_loss_cos1': np.float64(0.98937), 'test_avg_loss_entropy': np.float64(2.292087), 'test_loss_std': np.float64(15181.527741), 'test_loss_bottom_decile': np.float64(86435.716919), 'test_loss_top_decile': np.float64(138074.950317), 'test_loss_min': np.float64(82186.181091), 'test_loss_max': np.float64(138074.950317), 'test_loss_bottom10%': np.float64(82186.181091), 'test_loss_top10%': np.float64(138074.950317), 'test_loss_cos1': np.float64(0.98937), 'test_loss_entropy': np.float64(2.292087), 'val_avg_loss_std': np.float64(4.502964), 'val_avg_loss_bottom_decile': np.float64(26.047128), 'val_avg_loss_top_decile': np.float64(40.161512), 'val_avg_loss_min': np.float64(24.668565), 'val_avg_loss_max': np.float64(40.161512), 'val_avg_loss_bottom10%': np.float64(24.668565), 'val_avg_loss_top10%': np.float64(40.161512), 'val_avg_loss_cos1': np.float64(0.989547), 'val_avg_loss_entropy': np.float64(2.292174), 'val_loss_std': np.float64(15850.434895), 'val_loss_bottom_decile': np.float64(91685.891357), 'val_loss_top_decile': np.float64(141368.522217), 'val_loss_min': np.float64(86833.347351), 'val_loss_max': np.float64(141368.522217), 'val_loss_bottom10%': np.float64(86833.347351), 'val_loss_top10%': np.float64(141368.522217), 'val_loss_cos1': np.float64(0.989547), 'val_loss_entropy': np.float64(2.292174)}} 2024-11-14 19:37:12,932 (server:353) INFO: Server: Starting evaluation at the end of round 51. 2024-11-14 19:37:12,932 (server:359) INFO: ----------- Starting a new training round (Round #52) ------------- 2024-11-14 19:39:18,160 (client:354) INFO: {'Role': 'Client #10', 'Round': 52, 'Results_raw': {'train_loss': 22.394217, 'val_loss': 21.626115, 'test_loss': 21.910691}} 2024-11-14 19:40:02,095 (client:354) INFO: {'Role': 'Client #1', 'Round': 52, 'Results_raw': {'train_loss': 31.357758, 'val_loss': 30.330348, 'test_loss': 30.153489}} 2024-11-14 19:40:45,697 (client:354) INFO: {'Role': 'Client #5', 'Round': 52, 'Results_raw': {'train_loss': 20.079531, 'val_loss': 20.396176, 'test_loss': 21.685786}} 2024-11-14 19:41:29,791 (client:354) INFO: {'Role': 'Client #4', 'Round': 52, 'Results_raw': {'train_loss': 24.593392, 'val_loss': 21.919062, 'test_loss': 22.827549}} 2024-11-14 19:42:13,306 (client:354) INFO: {'Role': 'Client #7', 'Round': 52, 'Results_raw': {'train_loss': 19.284102, 'val_loss': 19.349692, 'test_loss': 18.012236}} 2024-11-14 19:42:56,349 (client:354) INFO: {'Role': 'Client #9', 'Round': 52, 'Results_raw': {'train_loss': 24.955365, 'val_loss': 29.660499, 'test_loss': 25.04696}} 2024-11-14 19:43:42,901 (client:354) INFO: {'Role': 'Client #3', 'Round': 52, 'Results_raw': {'train_loss': 26.342847, 'val_loss': 25.987519, 'test_loss': 26.07006}} 2024-11-14 19:44:29,791 (client:354) INFO: {'Role': 'Client #6', 'Round': 52, 'Results_raw': {'train_loss': 18.166978, 'val_loss': 19.346578, 'test_loss': 20.125521}} 2024-11-14 19:45:16,458 (client:354) INFO: {'Role': 'Client #2', 'Round': 52, 'Results_raw': {'train_loss': 23.331581, 'val_loss': 25.204119, 'test_loss': 24.220249}} 2024-11-14 19:46:02,525 (client:354) INFO: {'Role': 'Client #8', 'Round': 52, 'Results_raw': {'train_loss': 22.732848, 'val_loss': 26.860163, 'test_loss': 21.538018}} 2024-11-14 19:46:02,528 (server:615) INFO: {'Role': 'Server #', 'Round': 51, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.603734), 'test_loss': np.float64(104205.1422), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.144519), 'val_loss': np.float64(109628.706812), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.603734), 'test_loss': np.float64(104205.1422), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.144519), 'val_loss': np.float64(109628.706812), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.284002), 'test_avg_loss_bottom_decile': np.float64(24.944886), 'test_avg_loss_top_decile': np.float64(39.449489), 'test_avg_loss_min': np.float64(23.570332), 'test_avg_loss_max': np.float64(39.449489), 'test_avg_loss_bottom10%': np.float64(23.570332), 'test_avg_loss_top10%': np.float64(39.449489), 'test_avg_loss_cos1': np.float64(0.989691), 'test_avg_loss_entropy': np.float64(2.292407), 'test_loss_std': np.float64(15079.688514), 'test_loss_bottom_decile': np.float64(87805.998962), 'test_loss_top_decile': np.float64(138862.201538), 'test_loss_min': np.float64(82967.569336), 'test_loss_max': np.float64(138862.201538), 'test_loss_bottom10%': np.float64(82967.569336), 'test_loss_top10%': np.float64(138862.201538), 'test_loss_cos1': np.float64(0.989691), 'test_loss_entropy': np.float64(2.292407), 'val_avg_loss_std': np.float64(4.472279), 'val_avg_loss_bottom_decile': np.float64(26.444806), 'val_avg_loss_top_decile': np.float64(40.349966), 'val_avg_loss_min': np.float64(24.904298), 'val_avg_loss_max': np.float64(40.349966), 'val_avg_loss_bottom10%': np.float64(24.904298), 'val_avg_loss_top10%': np.float64(40.349966), 'val_avg_loss_cos1': np.float64(0.989847), 'val_avg_loss_entropy': np.float64(2.292476), 'val_loss_std': np.float64(15742.420564), 'val_loss_bottom_decile': np.float64(93085.716492), 'val_loss_top_decile': np.float64(142031.879639), 'val_loss_min': np.float64(87663.130615), 'val_loss_max': np.float64(142031.879639), 'val_loss_bottom10%': np.float64(87663.130615), 'val_loss_top10%': np.float64(142031.879639), 'val_loss_cos1': np.float64(0.989847), 'val_loss_entropy': np.float64(2.292476)}} 2024-11-14 19:46:02,565 (server:353) INFO: Server: Starting evaluation at the end of round 52. 2024-11-14 19:46:02,566 (server:359) INFO: ----------- Starting a new training round (Round #53) ------------- 2024-11-14 19:48:12,300 (client:354) INFO: {'Role': 'Client #9', 'Round': 53, 'Results_raw': {'train_loss': 24.895765, 'val_loss': 29.179997, 'test_loss': 24.786255}} 2024-11-14 19:48:59,284 (client:354) INFO: {'Role': 'Client #1', 'Round': 53, 'Results_raw': {'train_loss': 31.444814, 'val_loss': 30.450869, 'test_loss': 30.158248}} 2024-11-14 19:49:43,941 (client:354) INFO: {'Role': 'Client #2', 'Round': 53, 'Results_raw': {'train_loss': 23.305449, 'val_loss': 25.316106, 'test_loss': 24.096434}} 2024-11-14 19:50:27,925 (client:354) INFO: {'Role': 'Client #6', 'Round': 53, 'Results_raw': {'train_loss': 18.103422, 'val_loss': 19.550231, 'test_loss': 19.992796}} 2024-11-14 19:51:10,683 (client:354) INFO: {'Role': 'Client #3', 'Round': 53, 'Results_raw': {'train_loss': 26.275519, 'val_loss': 25.994595, 'test_loss': 25.990856}} 2024-11-14 19:51:53,017 (client:354) INFO: {'Role': 'Client #4', 'Round': 53, 'Results_raw': {'train_loss': 24.591789, 'val_loss': 21.619987, 'test_loss': 22.541348}} 2024-11-14 19:52:35,752 (client:354) INFO: {'Role': 'Client #10', 'Round': 53, 'Results_raw': {'train_loss': 22.294485, 'val_loss': 21.845617, 'test_loss': 22.082106}} 2024-11-14 19:53:19,058 (client:354) INFO: {'Role': 'Client #8', 'Round': 53, 'Results_raw': {'train_loss': 22.644641, 'val_loss': 27.898117, 'test_loss': 22.297642}} 2024-11-14 19:54:05,032 (client:354) INFO: {'Role': 'Client #7', 'Round': 53, 'Results_raw': {'train_loss': 19.176491, 'val_loss': 19.337342, 'test_loss': 17.925859}} 2024-11-14 19:54:48,813 (client:354) INFO: {'Role': 'Client #5', 'Round': 53, 'Results_raw': {'train_loss': 20.136129, 'val_loss': 20.332903, 'test_loss': 22.597781}} 2024-11-14 19:54:48,817 (server:615) INFO: {'Role': 'Server #', 'Round': 52, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.598395), 'test_loss': np.float64(104186.349121), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.151004), 'val_loss': np.float64(109651.534619), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.598395), 'test_loss': np.float64(104186.349121), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.151004), 'val_loss': np.float64(109651.534619), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.296379), 'test_avg_loss_bottom_decile': np.float64(24.886374), 'test_avg_loss_top_decile': np.float64(39.471835), 'test_avg_loss_min': np.float64(23.478279), 'test_avg_loss_max': np.float64(39.471835), 'test_avg_loss_bottom10%': np.float64(23.478279), 'test_avg_loss_top10%': np.float64(39.471835), 'test_avg_loss_cos1': np.float64(0.989629), 'test_avg_loss_entropy': np.float64(2.292333), 'test_loss_std': np.float64(15123.255369), 'test_loss_bottom_decile': np.float64(87600.035828), 'test_loss_top_decile': np.float64(138940.857788), 'test_loss_min': np.float64(82643.543518), 'test_loss_max': np.float64(138940.857788), 'test_loss_bottom10%': np.float64(82643.543518), 'test_loss_top10%': np.float64(138940.857788), 'test_loss_cos1': np.float64(0.989629), 'test_loss_entropy': np.float64(2.292333), 'val_avg_loss_std': np.float64(4.484187), 'val_avg_loss_bottom_decile': np.float64(26.344352), 'val_avg_loss_top_decile': np.float64(40.427074), 'val_avg_loss_min': np.float64(24.854999), 'val_avg_loss_max': np.float64(40.427074), 'val_avg_loss_bottom10%': np.float64(24.854999), 'val_avg_loss_top10%': np.float64(40.427074), 'val_avg_loss_cos1': np.float64(0.989797), 'val_avg_loss_entropy': np.float64(2.292424), 'val_loss_std': np.float64(15784.338991), 'val_loss_bottom_decile': np.float64(92732.118774), 'val_loss_top_decile': np.float64(142303.300293), 'val_loss_min': np.float64(87489.597046), 'val_loss_max': np.float64(142303.300293), 'val_loss_bottom10%': np.float64(87489.597046), 'val_loss_top10%': np.float64(142303.300293), 'val_loss_cos1': np.float64(0.989797), 'val_loss_entropy': np.float64(2.292424)}} 2024-11-14 19:54:48,863 (server:353) INFO: Server: Starting evaluation at the end of round 53. 2024-11-14 19:54:48,864 (server:359) INFO: ----------- Starting a new training round (Round #54) ------------- 2024-11-14 19:57:00,058 (client:354) INFO: {'Role': 'Client #5', 'Round': 54, 'Results_raw': {'train_loss': 20.058732, 'val_loss': 20.796252, 'test_loss': 23.071376}} 2024-11-14 19:57:43,841 (client:354) INFO: {'Role': 'Client #4', 'Round': 54, 'Results_raw': {'train_loss': 24.442993, 'val_loss': 21.646503, 'test_loss': 22.884947}} 2024-11-14 19:58:28,395 (client:354) INFO: {'Role': 'Client #2', 'Round': 54, 'Results_raw': {'train_loss': 23.294478, 'val_loss': 25.508698, 'test_loss': 24.237236}} 2024-11-14 19:59:12,582 (client:354) INFO: {'Role': 'Client #9', 'Round': 54, 'Results_raw': {'train_loss': 24.898827, 'val_loss': 29.821344, 'test_loss': 24.932784}} 2024-11-14 19:59:55,739 (client:354) INFO: {'Role': 'Client #7', 'Round': 54, 'Results_raw': {'train_loss': 19.169278, 'val_loss': 19.343484, 'test_loss': 17.971227}} 2024-11-14 20:00:38,688 (client:354) INFO: {'Role': 'Client #10', 'Round': 54, 'Results_raw': {'train_loss': 22.349517, 'val_loss': 21.9289, 'test_loss': 21.949765}} 2024-11-14 20:01:23,401 (client:354) INFO: {'Role': 'Client #3', 'Round': 54, 'Results_raw': {'train_loss': 26.288943, 'val_loss': 26.007991, 'test_loss': 26.171338}} 2024-11-14 20:02:10,409 (client:354) INFO: {'Role': 'Client #8', 'Round': 54, 'Results_raw': {'train_loss': 22.614998, 'val_loss': 27.423135, 'test_loss': 22.087677}} 2024-11-14 20:02:54,787 (client:354) INFO: {'Role': 'Client #1', 'Round': 54, 'Results_raw': {'train_loss': 31.299012, 'val_loss': 30.528518, 'test_loss': 30.280471}} 2024-11-14 20:03:45,528 (client:354) INFO: {'Role': 'Client #6', 'Round': 54, 'Results_raw': {'train_loss': 18.053057, 'val_loss': 19.414746, 'test_loss': 19.87031}} 2024-11-14 20:03:45,531 (server:615) INFO: {'Role': 'Server #', 'Round': 53, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.628435), 'test_loss': np.float64(104292.092194), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.170076), 'val_loss': np.float64(109718.666779), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.628435), 'test_loss': np.float64(104292.092194), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.170076), 'val_loss': np.float64(109718.666779), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.29033), 'test_avg_loss_bottom_decile': np.float64(24.946691), 'test_avg_loss_top_decile': np.float64(39.558906), 'test_avg_loss_min': np.float64(23.602286), 'test_avg_loss_max': np.float64(39.558906), 'test_avg_loss_bottom10%': np.float64(23.602286), 'test_avg_loss_top10%': np.float64(39.558906), 'test_avg_loss_cos1': np.float64(0.989678), 'test_avg_loss_entropy': np.float64(2.292404), 'test_loss_std': np.float64(15101.96268), 'test_loss_bottom_decile': np.float64(87812.350586), 'test_loss_top_decile': np.float64(139247.347412), 'test_loss_min': np.float64(83080.048035), 'test_loss_max': np.float64(139247.347412), 'test_loss_bottom10%': np.float64(83080.048035), 'test_loss_top10%': np.float64(139247.347412), 'test_loss_cos1': np.float64(0.989678), 'test_loss_entropy': np.float64(2.292404), 'val_avg_loss_std': np.float64(4.478221), 'val_avg_loss_bottom_decile': np.float64(26.419937), 'val_avg_loss_top_decile': np.float64(40.457421), 'val_avg_loss_min': np.float64(24.93695), 'val_avg_loss_max': np.float64(40.457421), 'val_avg_loss_bottom10%': np.float64(24.93695), 'val_avg_loss_top10%': np.float64(40.457421), 'val_avg_loss_cos1': np.float64(0.989836), 'val_avg_loss_entropy': np.float64(2.29247), 'val_loss_std': np.float64(15763.339529), 'val_loss_bottom_decile': np.float64(92998.177063), 'val_loss_top_decile': np.float64(142410.122559), 'val_loss_min': np.float64(87778.06488), 'val_loss_max': np.float64(142410.122559), 'val_loss_bottom10%': np.float64(87778.06488), 'val_loss_top10%': np.float64(142410.122559), 'val_loss_cos1': np.float64(0.989836), 'val_loss_entropy': np.float64(2.29247)}} 2024-11-14 20:03:45,565 (server:353) INFO: Server: Starting evaluation at the end of round 54. 2024-11-14 20:03:45,565 (server:359) INFO: ----------- Starting a new training round (Round #55) ------------- 2024-11-14 20:05:59,392 (client:354) INFO: {'Role': 'Client #5', 'Round': 55, 'Results_raw': {'train_loss': 20.0304, 'val_loss': 20.566909, 'test_loss': 22.84663}} 2024-11-14 20:06:42,738 (client:354) INFO: {'Role': 'Client #2', 'Round': 55, 'Results_raw': {'train_loss': 23.361195, 'val_loss': 25.297694, 'test_loss': 24.282446}} 2024-11-14 20:07:26,018 (client:354) INFO: {'Role': 'Client #3', 'Round': 55, 'Results_raw': {'train_loss': 26.273263, 'val_loss': 26.234502, 'test_loss': 26.209004}} 2024-11-14 20:08:09,481 (client:354) INFO: {'Role': 'Client #7', 'Round': 55, 'Results_raw': {'train_loss': 19.379204, 'val_loss': 19.488647, 'test_loss': 18.133}} 2024-11-14 20:08:52,824 (client:354) INFO: {'Role': 'Client #6', 'Round': 55, 'Results_raw': {'train_loss': 18.105011, 'val_loss': 19.427653, 'test_loss': 19.695268}} 2024-11-14 20:09:36,643 (client:354) INFO: {'Role': 'Client #9', 'Round': 55, 'Results_raw': {'train_loss': 24.874598, 'val_loss': 30.06804, 'test_loss': 24.959121}} 2024-11-14 20:10:20,349 (client:354) INFO: {'Role': 'Client #1', 'Round': 55, 'Results_raw': {'train_loss': 31.38713, 'val_loss': 30.965753, 'test_loss': 30.473554}} 2024-11-14 20:11:04,214 (client:354) INFO: {'Role': 'Client #10', 'Round': 55, 'Results_raw': {'train_loss': 22.362517, 'val_loss': 21.54531, 'test_loss': 21.657756}} 2024-11-14 20:11:46,822 (client:354) INFO: {'Role': 'Client #4', 'Round': 55, 'Results_raw': {'train_loss': 24.531152, 'val_loss': 21.630022, 'test_loss': 22.82623}} 2024-11-14 20:12:28,907 (client:354) INFO: {'Role': 'Client #8', 'Round': 55, 'Results_raw': {'train_loss': 22.539247, 'val_loss': 25.881557, 'test_loss': 22.030007}} 2024-11-14 20:12:28,910 (server:615) INFO: {'Role': 'Server #', 'Round': 54, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.524929), 'test_loss': np.float64(103927.748755), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.078591), 'val_loss': np.float64(109396.641376), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.524929), 'test_loss': np.float64(103927.748755), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.078591), 'val_loss': np.float64(109396.641376), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.335434), 'test_avg_loss_bottom_decile': np.float64(24.62388), 'test_avg_loss_top_decile': np.float64(39.486051), 'test_avg_loss_min': np.float64(23.55982), 'test_avg_loss_max': np.float64(39.486051), 'test_avg_loss_bottom10%': np.float64(23.55982), 'test_avg_loss_top10%': np.float64(39.486051), 'test_avg_loss_cos1': np.float64(0.98939), 'test_avg_loss_entropy': np.float64(2.292114), 'test_loss_std': np.float64(15260.72685), 'test_loss_bottom_decile': np.float64(86676.058167), 'test_loss_top_decile': np.float64(138990.900757), 'test_loss_min': np.float64(82930.565491), 'test_loss_max': np.float64(138990.900757), 'test_loss_bottom10%': np.float64(82930.565491), 'test_loss_top10%': np.float64(138990.900757), 'test_loss_cos1': np.float64(0.98939), 'test_loss_entropy': np.float64(2.292114), 'val_avg_loss_std': np.float64(4.539719), 'val_avg_loss_bottom_decile': np.float64(26.09464), 'val_avg_loss_top_decile': np.float64(40.383708), 'val_avg_loss_min': np.float64(24.890759), 'val_avg_loss_max': np.float64(40.383708), 'val_avg_loss_bottom10%': np.float64(24.890759), 'val_avg_loss_top10%': np.float64(40.383708), 'val_avg_loss_cos1': np.float64(0.989499), 'val_avg_loss_entropy': np.float64(2.292127), 'val_loss_std': np.float64(15979.810446), 'val_loss_bottom_decile': np.float64(91853.134338), 'val_loss_top_decile': np.float64(142150.650635), 'val_loss_min': np.float64(87615.470215), 'val_loss_max': np.float64(142150.650635), 'val_loss_bottom10%': np.float64(87615.470215), 'val_loss_top10%': np.float64(142150.650635), 'val_loss_cos1': np.float64(0.989499), 'val_loss_entropy': np.float64(2.292127)}} 2024-11-14 20:12:28,938 (server:353) INFO: Server: Starting evaluation at the end of round 55. 2024-11-14 20:12:28,938 (server:359) INFO: ----------- Starting a new training round (Round #56) ------------- 2024-11-14 20:14:42,150 (client:354) INFO: {'Role': 'Client #5', 'Round': 56, 'Results_raw': {'train_loss': 20.000862, 'val_loss': 19.986112, 'test_loss': 21.618186}} 2024-11-14 20:15:26,609 (client:354) INFO: {'Role': 'Client #10', 'Round': 56, 'Results_raw': {'train_loss': 22.170765, 'val_loss': 21.786801, 'test_loss': 22.085396}} 2024-11-14 20:16:10,284 (client:354) INFO: {'Role': 'Client #7', 'Round': 56, 'Results_raw': {'train_loss': 19.150456, 'val_loss': 19.296769, 'test_loss': 17.88837}} 2024-11-14 20:16:53,923 (client:354) INFO: {'Role': 'Client #8', 'Round': 56, 'Results_raw': {'train_loss': 22.530082, 'val_loss': 27.643502, 'test_loss': 22.633581}} 2024-11-14 20:17:37,503 (client:354) INFO: {'Role': 'Client #9', 'Round': 56, 'Results_raw': {'train_loss': 24.937734, 'val_loss': 29.93276, 'test_loss': 25.065433}} 2024-11-14 20:18:21,620 (client:354) INFO: {'Role': 'Client #4', 'Round': 56, 'Results_raw': {'train_loss': 24.554145, 'val_loss': 21.886879, 'test_loss': 23.392414}} 2024-11-14 20:19:05,243 (client:354) INFO: {'Role': 'Client #3', 'Round': 56, 'Results_raw': {'train_loss': 26.23356, 'val_loss': 26.037482, 'test_loss': 26.176154}} 2024-11-14 20:19:48,434 (client:354) INFO: {'Role': 'Client #1', 'Round': 56, 'Results_raw': {'train_loss': 31.269283, 'val_loss': 30.199929, 'test_loss': 29.905995}} 2024-11-14 20:20:30,436 (client:354) INFO: {'Role': 'Client #2', 'Round': 56, 'Results_raw': {'train_loss': 23.261982, 'val_loss': 25.595847, 'test_loss': 24.200833}} 2024-11-14 20:21:13,156 (client:354) INFO: {'Role': 'Client #6', 'Round': 56, 'Results_raw': {'train_loss': 18.058384, 'val_loss': 19.354628, 'test_loss': 19.482245}} 2024-11-14 20:21:13,159 (server:615) INFO: {'Role': 'Server #', 'Round': 55, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.366259), 'test_loss': np.float64(103369.232648), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.90772), 'val_loss': np.float64(108795.175464), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.366259), 'test_loss': np.float64(103369.232648), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.90772), 'val_loss': np.float64(108795.175464), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.24564), 'test_avg_loss_bottom_decile': np.float64(24.670901), 'test_avg_loss_top_decile': np.float64(39.120374), 'test_avg_loss_min': np.float64(23.598882), 'test_avg_loss_max': np.float64(39.120374), 'test_avg_loss_bottom10%': np.float64(23.598882), 'test_avg_loss_top10%': np.float64(39.120374), 'test_avg_loss_cos1': np.float64(0.98971), 'test_avg_loss_entropy': np.float64(2.292437), 'test_loss_std': np.float64(14944.652427), 'test_loss_bottom_decile': np.float64(86841.572205), 'test_loss_top_decile': np.float64(137703.717041), 'test_loss_min': np.float64(83068.064697), 'test_loss_max': np.float64(137703.717041), 'test_loss_bottom10%': np.float64(83068.064697), 'test_loss_top10%': np.float64(137703.717041), 'test_loss_cos1': np.float64(0.98971), 'test_loss_entropy': np.float64(2.292437), 'val_avg_loss_std': np.float64(4.438126), 'val_avg_loss_bottom_decile': np.float64(26.123083), 'val_avg_loss_top_decile': np.float64(40.019694), 'val_avg_loss_min': np.float64(24.925735), 'val_avg_loss_max': np.float64(40.019694), 'val_avg_loss_bottom10%': np.float64(24.925735), 'val_avg_loss_top10%': np.float64(40.019694), 'val_avg_loss_cos1': np.float64(0.989847), 'val_avg_loss_entropy': np.float64(2.292483), 'val_loss_std': np.float64(15622.205055), 'val_loss_bottom_decile': np.float64(91953.251404), 'val_loss_top_decile': np.float64(140869.322998), 'val_loss_min': np.float64(87738.585571), 'val_loss_max': np.float64(140869.322998), 'val_loss_bottom10%': np.float64(87738.585571), 'val_loss_top10%': np.float64(140869.322998), 'val_loss_cos1': np.float64(0.989847), 'val_loss_entropy': np.float64(2.292483)}} 2024-11-14 20:21:13,191 (server:353) INFO: Server: Starting evaluation at the end of round 56. 2024-11-14 20:21:13,191 (server:359) INFO: ----------- Starting a new training round (Round #57) ------------- 2024-11-14 20:23:27,192 (client:354) INFO: {'Role': 'Client #2', 'Round': 57, 'Results_raw': {'train_loss': 23.243866, 'val_loss': 25.51306, 'test_loss': 24.308649}} 2024-11-14 20:24:14,554 (client:354) INFO: {'Role': 'Client #10', 'Round': 57, 'Results_raw': {'train_loss': 22.242392, 'val_loss': 21.651618, 'test_loss': 21.930011}} 2024-11-14 20:25:00,849 (client:354) INFO: {'Role': 'Client #5', 'Round': 57, 'Results_raw': {'train_loss': 19.983286, 'val_loss': 20.259783, 'test_loss': 21.203154}} 2024-11-14 20:25:47,147 (client:354) INFO: {'Role': 'Client #7', 'Round': 57, 'Results_raw': {'train_loss': 19.155677, 'val_loss': 19.345795, 'test_loss': 17.868086}} 2024-11-14 20:26:33,678 (client:354) INFO: {'Role': 'Client #4', 'Round': 57, 'Results_raw': {'train_loss': 24.464669, 'val_loss': 22.204097, 'test_loss': 23.380909}} 2024-11-14 20:27:20,415 (client:354) INFO: {'Role': 'Client #6', 'Round': 57, 'Results_raw': {'train_loss': 18.020433, 'val_loss': 19.454211, 'test_loss': 19.628412}} 2024-11-14 20:28:06,608 (client:354) INFO: {'Role': 'Client #8', 'Round': 57, 'Results_raw': {'train_loss': 22.534994, 'val_loss': 26.439485, 'test_loss': 21.960739}} 2024-11-14 20:28:49,868 (client:354) INFO: {'Role': 'Client #1', 'Round': 57, 'Results_raw': {'train_loss': 31.301816, 'val_loss': 30.389281, 'test_loss': 29.976282}} 2024-11-14 20:29:31,888 (client:354) INFO: {'Role': 'Client #9', 'Round': 57, 'Results_raw': {'train_loss': 24.913577, 'val_loss': 29.748308, 'test_loss': 25.274769}} 2024-11-14 20:30:14,977 (client:354) INFO: {'Role': 'Client #3', 'Round': 57, 'Results_raw': {'train_loss': 26.243445, 'val_loss': 26.063845, 'test_loss': 26.206211}} 2024-11-14 20:30:14,980 (server:615) INFO: {'Role': 'Server #', 'Round': 56, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.230677), 'test_loss': np.float64(102891.984332), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.790434), 'val_loss': np.float64(108382.328363), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.230677), 'test_loss': np.float64(102891.984332), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.790434), 'val_loss': np.float64(108382.328363), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.294621), 'test_avg_loss_bottom_decile': np.float64(24.261513), 'test_avg_loss_top_decile': np.float64(39.13674), 'test_avg_loss_min': np.float64(23.41564), 'test_avg_loss_max': np.float64(39.13674), 'test_avg_loss_bottom10%': np.float64(23.41564), 'test_avg_loss_top10%': np.float64(39.13674), 'test_avg_loss_cos1': np.float64(0.989379), 'test_avg_loss_entropy': np.float64(2.292105), 'test_loss_std': np.float64(15117.067026), 'test_loss_bottom_decile': np.float64(85400.527039), 'test_loss_top_decile': np.float64(137761.32373), 'test_loss_min': np.float64(82423.052734), 'test_loss_max': np.float64(137761.32373), 'test_loss_bottom10%': np.float64(82423.052734), 'test_loss_top10%': np.float64(137761.32373), 'test_loss_cos1': np.float64(0.989379), 'test_loss_entropy': np.float64(2.292105), 'val_avg_loss_std': np.float64(4.480445), 'val_avg_loss_bottom_decile': np.float64(25.729224), 'val_avg_loss_top_decile': np.float64(40.00877), 'val_avg_loss_min': np.float64(24.723991), 'val_avg_loss_max': np.float64(40.00877), 'val_avg_loss_bottom10%': np.float64(24.723991), 'val_avg_loss_top10%': np.float64(40.00877), 'val_avg_loss_cos1': np.float64(0.989578), 'val_avg_loss_entropy': np.float64(2.292201), 'val_loss_std': np.float64(15771.164777), 'val_loss_bottom_decile': np.float64(90566.869202), 'val_loss_top_decile': np.float64(140830.869141), 'val_loss_min': np.float64(87028.447388), 'val_loss_max': np.float64(140830.869141), 'val_loss_bottom10%': np.float64(87028.447388), 'val_loss_top10%': np.float64(140830.869141), 'val_loss_cos1': np.float64(0.989578), 'val_loss_entropy': np.float64(2.292201)}} 2024-11-14 20:30:15,015 (server:353) INFO: Server: Starting evaluation at the end of round 57. 2024-11-14 20:30:15,015 (server:359) INFO: ----------- Starting a new training round (Round #58) ------------- 2024-11-14 20:32:28,235 (client:354) INFO: {'Role': 'Client #6', 'Round': 58, 'Results_raw': {'train_loss': 18.05977, 'val_loss': 19.596762, 'test_loss': 19.705734}} 2024-11-14 20:33:12,520 (client:354) INFO: {'Role': 'Client #8', 'Round': 58, 'Results_raw': {'train_loss': 22.476505, 'val_loss': 27.494985, 'test_loss': 22.688496}} 2024-11-14 20:33:54,929 (client:354) INFO: {'Role': 'Client #10', 'Round': 58, 'Results_raw': {'train_loss': 22.231395, 'val_loss': 21.978305, 'test_loss': 22.348004}} 2024-11-14 20:34:38,266 (client:354) INFO: {'Role': 'Client #9', 'Round': 58, 'Results_raw': {'train_loss': 24.834469, 'val_loss': 29.27219, 'test_loss': 24.750004}} 2024-11-14 20:35:21,533 (client:354) INFO: {'Role': 'Client #4', 'Round': 58, 'Results_raw': {'train_loss': 24.452949, 'val_loss': 21.603481, 'test_loss': 22.80571}} 2024-11-14 20:36:05,129 (client:354) INFO: {'Role': 'Client #2', 'Round': 58, 'Results_raw': {'train_loss': 23.20806, 'val_loss': 25.576125, 'test_loss': 24.410725}} 2024-11-14 20:36:50,355 (client:354) INFO: {'Role': 'Client #3', 'Round': 58, 'Results_raw': {'train_loss': 26.207111, 'val_loss': 26.012506, 'test_loss': 26.181005}} 2024-11-14 20:37:34,421 (client:354) INFO: {'Role': 'Client #5', 'Round': 58, 'Results_raw': {'train_loss': 20.084821, 'val_loss': 20.236879, 'test_loss': 22.432792}} 2024-11-14 20:38:19,459 (client:354) INFO: {'Role': 'Client #7', 'Round': 58, 'Results_raw': {'train_loss': 19.125951, 'val_loss': 19.214774, 'test_loss': 17.857155}} 2024-11-14 20:39:06,227 (client:354) INFO: {'Role': 'Client #1', 'Round': 58, 'Results_raw': {'train_loss': 31.202109, 'val_loss': 31.220078, 'test_loss': 30.794792}} 2024-11-14 20:39:06,231 (server:615) INFO: {'Role': 'Server #', 'Round': 57, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.239079), 'test_loss': np.float64(102921.557361), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.764442), 'val_loss': np.float64(108290.834436), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.239079), 'test_loss': np.float64(102921.557361), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.764442), 'val_loss': np.float64(108290.834436), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.309922), 'test_avg_loss_bottom_decile': np.float64(24.462029), 'test_avg_loss_top_decile': np.float64(39.19351), 'test_avg_loss_min': np.float64(23.368679), 'test_avg_loss_max': np.float64(39.19351), 'test_avg_loss_bottom10%': np.float64(23.368679), 'test_avg_loss_top10%': np.float64(39.19351), 'test_avg_loss_cos1': np.float64(0.98931), 'test_avg_loss_entropy': np.float64(2.292043), 'test_loss_std': np.float64(15170.927011), 'test_loss_bottom_decile': np.float64(86106.341614), 'test_loss_top_decile': np.float64(137961.154053), 'test_loss_min': np.float64(82257.75116), 'test_loss_max': np.float64(137961.154053), 'test_loss_bottom10%': np.float64(82257.75116), 'test_loss_top10%': np.float64(137961.154053), 'test_loss_cos1': np.float64(0.98931), 'test_loss_entropy': np.float64(2.292043), 'val_avg_loss_std': np.float64(4.481667), 'val_avg_loss_bottom_decile': np.float64(25.916078), 'val_avg_loss_top_decile': np.float64(40.081598), 'val_avg_loss_min': np.float64(24.656812), 'val_avg_loss_max': np.float64(40.081598), 'val_avg_loss_bottom10%': np.float64(24.656812), 'val_avg_loss_top10%': np.float64(40.081598), 'val_avg_loss_cos1': np.float64(0.989555), 'val_avg_loss_entropy': np.float64(2.292189), 'val_loss_std': np.float64(15775.46856), 'val_loss_bottom_decile': np.float64(91224.594116), 'val_loss_top_decile': np.float64(141087.226562), 'val_loss_min': np.float64(86791.97876), 'val_loss_max': np.float64(141087.226562), 'val_loss_bottom10%': np.float64(86791.97876), 'val_loss_top10%': np.float64(141087.226562), 'val_loss_cos1': np.float64(0.989555), 'val_loss_entropy': np.float64(2.292189)}} 2024-11-14 20:39:06,268 (server:353) INFO: Server: Starting evaluation at the end of round 58. 2024-11-14 20:39:06,269 (server:359) INFO: ----------- Starting a new training round (Round #59) ------------- 2024-11-14 20:41:17,757 (client:354) INFO: {'Role': 'Client #1', 'Round': 59, 'Results_raw': {'train_loss': 31.2109, 'val_loss': 30.44521, 'test_loss': 30.034498}} 2024-11-14 20:42:01,464 (client:354) INFO: {'Role': 'Client #10', 'Round': 59, 'Results_raw': {'train_loss': 22.345122, 'val_loss': 22.156597, 'test_loss': 22.259428}} 2024-11-14 20:42:44,990 (client:354) INFO: {'Role': 'Client #5', 'Round': 59, 'Results_raw': {'train_loss': 20.04908, 'val_loss': 20.223985, 'test_loss': 21.60817}} 2024-11-14 20:43:28,911 (client:354) INFO: {'Role': 'Client #3', 'Round': 59, 'Results_raw': {'train_loss': 26.161683, 'val_loss': 25.838801, 'test_loss': 25.943517}} 2024-11-14 20:44:13,173 (client:354) INFO: {'Role': 'Client #8', 'Round': 59, 'Results_raw': {'train_loss': 22.468633, 'val_loss': 26.557843, 'test_loss': 21.412698}} 2024-11-14 20:44:57,259 (client:354) INFO: {'Role': 'Client #2', 'Round': 59, 'Results_raw': {'train_loss': 23.18339, 'val_loss': 25.459841, 'test_loss': 24.324294}} 2024-11-14 20:45:42,717 (client:354) INFO: {'Role': 'Client #6', 'Round': 59, 'Results_raw': {'train_loss': 18.047741, 'val_loss': 19.168108, 'test_loss': 19.638384}} 2024-11-14 20:46:25,782 (client:354) INFO: {'Role': 'Client #7', 'Round': 59, 'Results_raw': {'train_loss': 19.132142, 'val_loss': 19.384809, 'test_loss': 17.958713}} 2024-11-14 20:47:07,783 (client:354) INFO: {'Role': 'Client #9', 'Round': 59, 'Results_raw': {'train_loss': 24.769493, 'val_loss': 29.422049, 'test_loss': 24.866169}} 2024-11-14 20:47:51,971 (client:354) INFO: {'Role': 'Client #4', 'Round': 59, 'Results_raw': {'train_loss': 24.437722, 'val_loss': 21.844632, 'test_loss': 23.138826}} 2024-11-14 20:47:51,973 (server:615) INFO: {'Role': 'Server #', 'Round': 58, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.241375), 'test_loss': np.float64(102929.640686), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.787826), 'val_loss': np.float64(108373.146399), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.241375), 'test_loss': np.float64(102929.640686), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.787826), 'val_loss': np.float64(108373.146399), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.237467), 'test_avg_loss_bottom_decile': np.float64(24.585972), 'test_avg_loss_top_decile': np.float64(39.006797), 'test_avg_loss_min': np.float64(23.38546), 'test_avg_loss_max': np.float64(39.006797), 'test_avg_loss_bottom10%': np.float64(23.38546), 'test_avg_loss_top10%': np.float64(39.006797), 'test_avg_loss_cos1': np.float64(0.989663), 'test_avg_loss_entropy': np.float64(2.292388), 'test_loss_std': np.float64(14915.88428), 'test_loss_bottom_decile': np.float64(86542.622009), 'test_loss_top_decile': np.float64(137303.927124), 'test_loss_min': np.float64(82316.818542), 'test_loss_max': np.float64(137303.927124), 'test_loss_bottom10%': np.float64(82316.818542), 'test_loss_top10%': np.float64(137303.927124), 'test_loss_cos1': np.float64(0.989663), 'test_loss_entropy': np.float64(2.292388), 'val_avg_loss_std': np.float64(4.397434), 'val_avg_loss_bottom_decile': np.float64(26.045094), 'val_avg_loss_top_decile': np.float64(39.903981), 'val_avg_loss_min': np.float64(24.693808), 'val_avg_loss_max': np.float64(39.903981), 'val_avg_loss_bottom10%': np.float64(24.693808), 'val_avg_loss_top10%': np.float64(39.903981), 'val_avg_loss_cos1': np.float64(0.989953), 'val_avg_loss_entropy': np.float64(2.29258), 'val_loss_std': np.float64(15478.968847), 'val_loss_bottom_decile': np.float64(91678.732422), 'val_loss_top_decile': np.float64(140462.011597), 'val_loss_min': np.float64(86922.203857), 'val_loss_max': np.float64(140462.011597), 'val_loss_bottom10%': np.float64(86922.203857), 'val_loss_top10%': np.float64(140462.011597), 'val_loss_cos1': np.float64(0.989953), 'val_loss_entropy': np.float64(2.29258)}} 2024-11-14 20:47:52,014 (server:353) INFO: Server: Starting evaluation at the end of round 59. 2024-11-14 20:47:52,014 (server:359) INFO: ----------- Starting a new training round (Round #60) ------------- 2024-11-14 20:50:01,339 (client:354) INFO: {'Role': 'Client #4', 'Round': 60, 'Results_raw': {'train_loss': 24.362722, 'val_loss': 21.639202, 'test_loss': 23.023747}} 2024-11-14 20:50:43,649 (client:354) INFO: {'Role': 'Client #10', 'Round': 60, 'Results_raw': {'train_loss': 22.132006, 'val_loss': 21.865361, 'test_loss': 22.296418}} 2024-11-14 20:51:26,352 (client:354) INFO: {'Role': 'Client #1', 'Round': 60, 'Results_raw': {'train_loss': 31.234911, 'val_loss': 30.307481, 'test_loss': 30.063453}} 2024-11-14 20:52:10,280 (client:354) INFO: {'Role': 'Client #9', 'Round': 60, 'Results_raw': {'train_loss': 24.731772, 'val_loss': 29.426186, 'test_loss': 24.584249}} 2024-11-14 20:52:54,413 (client:354) INFO: {'Role': 'Client #5', 'Round': 60, 'Results_raw': {'train_loss': 19.898079, 'val_loss': 20.356808, 'test_loss': 20.496421}} 2024-11-14 20:53:40,166 (client:354) INFO: {'Role': 'Client #3', 'Round': 60, 'Results_raw': {'train_loss': 26.141917, 'val_loss': 25.957682, 'test_loss': 26.098855}} 2024-11-14 20:54:24,023 (client:354) INFO: {'Role': 'Client #7', 'Round': 60, 'Results_raw': {'train_loss': 19.061238, 'val_loss': 19.44491, 'test_loss': 18.110086}} 2024-11-14 20:55:07,294 (client:354) INFO: {'Role': 'Client #2', 'Round': 60, 'Results_raw': {'train_loss': 23.076886, 'val_loss': 25.346779, 'test_loss': 24.11185}} 2024-11-14 20:55:52,166 (client:354) INFO: {'Role': 'Client #6', 'Round': 60, 'Results_raw': {'train_loss': 18.049065, 'val_loss': 19.310829, 'test_loss': 19.393788}} 2024-11-14 20:56:37,034 (client:354) INFO: {'Role': 'Client #8', 'Round': 60, 'Results_raw': {'train_loss': 22.478013, 'val_loss': 27.347097, 'test_loss': 21.662299}} 2024-11-14 20:56:37,039 (server:615) INFO: {'Role': 'Server #', 'Round': 59, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.511832), 'test_loss': np.float64(103881.650238), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.036882), 'val_loss': np.float64(109249.825909), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.511832), 'test_loss': np.float64(103881.650238), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(31.036882), 'val_loss': np.float64(109249.825909), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.286611), 'test_avg_loss_bottom_decile': np.float64(24.810233), 'test_avg_loss_top_decile': np.float64(39.353667), 'test_avg_loss_min': np.float64(23.578558), 'test_avg_loss_max': np.float64(39.353667), 'test_avg_loss_bottom10%': np.float64(23.578558), 'test_avg_loss_top10%': np.float64(39.353667), 'test_avg_loss_cos1': np.float64(0.989615), 'test_avg_loss_entropy': np.float64(2.292329), 'test_loss_std': np.float64(15088.871162), 'test_loss_bottom_decile': np.float64(87332.020203), 'test_loss_top_decile': np.float64(138524.907959), 'test_loss_min': np.float64(82996.523376), 'test_loss_max': np.float64(138524.907959), 'test_loss_bottom10%': np.float64(82996.523376), 'test_loss_top10%': np.float64(138524.907959), 'test_loss_cos1': np.float64(0.989615), 'test_loss_entropy': np.float64(2.292329), 'val_avg_loss_std': np.float64(4.473392), 'val_avg_loss_bottom_decile': np.float64(26.281343), 'val_avg_loss_top_decile': np.float64(40.253322), 'val_avg_loss_min': np.float64(24.860609), 'val_avg_loss_max': np.float64(40.253322), 'val_avg_loss_bottom10%': np.float64(24.860609), 'val_avg_loss_top10%': np.float64(40.253322), 'val_avg_loss_cos1': np.float64(0.989772), 'val_avg_loss_entropy': np.float64(2.292401), 'val_loss_std': np.float64(15746.340726), 'val_loss_bottom_decile': np.float64(92510.327026), 'val_loss_top_decile': np.float64(141691.692993), 'val_loss_min': np.float64(87509.343689), 'val_loss_max': np.float64(141691.692993), 'val_loss_bottom10%': np.float64(87509.343689), 'val_loss_top10%': np.float64(141691.692993), 'val_loss_cos1': np.float64(0.989772), 'val_loss_entropy': np.float64(2.292401)}} 2024-11-14 20:56:37,067 (server:353) INFO: Server: Starting evaluation at the end of round 60. 2024-11-14 20:56:37,068 (server:359) INFO: ----------- Starting a new training round (Round #61) ------------- 2024-11-14 20:58:51,574 (client:354) INFO: {'Role': 'Client #6', 'Round': 61, 'Results_raw': {'train_loss': 18.018665, 'val_loss': 19.780469, 'test_loss': 19.850914}} 2024-11-14 20:59:36,010 (client:354) INFO: {'Role': 'Client #2', 'Round': 61, 'Results_raw': {'train_loss': 23.170417, 'val_loss': 25.522498, 'test_loss': 24.437555}} 2024-11-14 21:00:21,058 (client:354) INFO: {'Role': 'Client #8', 'Round': 61, 'Results_raw': {'train_loss': 22.418398, 'val_loss': 27.1813, 'test_loss': 22.80895}} 2024-11-14 21:01:08,049 (client:354) INFO: {'Role': 'Client #4', 'Round': 61, 'Results_raw': {'train_loss': 24.396027, 'val_loss': 21.720242, 'test_loss': 22.622607}} 2024-11-14 21:01:56,311 (client:354) INFO: {'Role': 'Client #5', 'Round': 61, 'Results_raw': {'train_loss': 19.925604, 'val_loss': 20.649628, 'test_loss': 23.105901}} 2024-11-14 21:02:43,489 (client:354) INFO: {'Role': 'Client #7', 'Round': 61, 'Results_raw': {'train_loss': 19.131982, 'val_loss': 19.341657, 'test_loss': 18.01664}} 2024-11-14 21:03:30,671 (client:354) INFO: {'Role': 'Client #9', 'Round': 61, 'Results_raw': {'train_loss': 24.798301, 'val_loss': 30.14595, 'test_loss': 25.266422}} 2024-11-14 21:04:15,520 (client:354) INFO: {'Role': 'Client #10', 'Round': 61, 'Results_raw': {'train_loss': 22.211089, 'val_loss': 22.161723, 'test_loss': 22.259307}} 2024-11-14 21:05:00,441 (client:354) INFO: {'Role': 'Client #1', 'Round': 61, 'Results_raw': {'train_loss': 31.194781, 'val_loss': 30.499316, 'test_loss': 30.012025}} 2024-11-14 21:05:46,789 (client:354) INFO: {'Role': 'Client #3', 'Round': 61, 'Results_raw': {'train_loss': 26.166744, 'val_loss': 25.982322, 'test_loss': 26.175264}} 2024-11-14 21:05:46,793 (server:615) INFO: {'Role': 'Server #', 'Round': 60, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.359487), 'test_loss': np.float64(103345.39469), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.889522), 'val_loss': np.float64(108731.118805), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.359487), 'test_loss': np.float64(103345.39469), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.889522), 'val_loss': np.float64(108731.118805), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.344679), 'test_avg_loss_bottom_decile': np.float64(24.569693), 'test_avg_loss_top_decile': np.float64(39.381078), 'test_avg_loss_min': np.float64(23.36948), 'test_avg_loss_max': np.float64(39.381078), 'test_avg_loss_bottom10%': np.float64(23.36948), 'test_avg_loss_top10%': np.float64(39.381078), 'test_avg_loss_cos1': np.float64(0.989227), 'test_avg_loss_entropy': np.float64(2.291957), 'test_loss_std': np.float64(15293.268515), 'test_loss_bottom_decile': np.float64(86485.320801), 'test_loss_top_decile': np.float64(138621.393921), 'test_loss_min': np.float64(82260.569275), 'test_loss_max': np.float64(138621.393921), 'test_loss_bottom10%': np.float64(82260.569275), 'test_loss_top10%': np.float64(138621.393921), 'test_loss_cos1': np.float64(0.989227), 'test_loss_entropy': np.float64(2.291957), 'val_avg_loss_std': np.float64(4.518093), 'val_avg_loss_bottom_decile': np.float64(26.063093), 'val_avg_loss_top_decile': np.float64(40.274691), 'val_avg_loss_min': np.float64(24.641979), 'val_avg_loss_max': np.float64(40.274691), 'val_avg_loss_bottom10%': np.float64(24.641979), 'val_avg_loss_top10%': np.float64(40.274691), 'val_avg_loss_cos1': np.float64(0.989472), 'val_avg_loss_entropy': np.float64(2.292107), 'val_loss_std': np.float64(15903.687513), 'val_loss_bottom_decile': np.float64(91742.085815), 'val_loss_top_decile': np.float64(141766.911011), 'val_loss_min': np.float64(86739.766479), 'val_loss_max': np.float64(141766.911011), 'val_loss_bottom10%': np.float64(86739.766479), 'val_loss_top10%': np.float64(141766.911011), 'val_loss_cos1': np.float64(0.989472), 'val_loss_entropy': np.float64(2.292107)}} 2024-11-14 21:05:46,829 (server:353) INFO: Server: Starting evaluation at the end of round 61. 2024-11-14 21:05:46,829 (server:359) INFO: ----------- Starting a new training round (Round #62) ------------- 2024-11-14 21:07:58,807 (client:354) INFO: {'Role': 'Client #4', 'Round': 62, 'Results_raw': {'train_loss': 24.401286, 'val_loss': 21.897047, 'test_loss': 23.230978}} 2024-11-14 21:08:41,736 (client:354) INFO: {'Role': 'Client #8', 'Round': 62, 'Results_raw': {'train_loss': 22.416948, 'val_loss': 27.023254, 'test_loss': 22.107735}} 2024-11-14 21:09:24,948 (client:354) INFO: {'Role': 'Client #3', 'Round': 62, 'Results_raw': {'train_loss': 26.126464, 'val_loss': 26.260891, 'test_loss': 26.27927}} 2024-11-14 21:10:08,776 (client:354) INFO: {'Role': 'Client #10', 'Round': 62, 'Results_raw': {'train_loss': 22.250645, 'val_loss': 21.919606, 'test_loss': 22.590243}} 2024-11-14 21:10:52,134 (client:354) INFO: {'Role': 'Client #2', 'Round': 62, 'Results_raw': {'train_loss': 23.173451, 'val_loss': 25.59987, 'test_loss': 24.40409}} 2024-11-14 21:11:36,013 (client:354) INFO: {'Role': 'Client #6', 'Round': 62, 'Results_raw': {'train_loss': 18.055006, 'val_loss': 19.241583, 'test_loss': 19.570743}} 2024-11-14 21:12:18,886 (client:354) INFO: {'Role': 'Client #9', 'Round': 62, 'Results_raw': {'train_loss': 24.667014, 'val_loss': 29.588996, 'test_loss': 24.805081}} 2024-11-14 21:13:00,464 (client:354) INFO: {'Role': 'Client #5', 'Round': 62, 'Results_raw': {'train_loss': 19.888669, 'val_loss': 20.454487, 'test_loss': 21.66726}} 2024-11-14 21:13:44,048 (client:354) INFO: {'Role': 'Client #7', 'Round': 62, 'Results_raw': {'train_loss': 19.132527, 'val_loss': 19.387676, 'test_loss': 17.993668}} 2024-11-14 21:14:27,254 (client:354) INFO: {'Role': 'Client #1', 'Round': 62, 'Results_raw': {'train_loss': 31.096441, 'val_loss': 30.651897, 'test_loss': 30.27012}} 2024-11-14 21:14:27,258 (server:615) INFO: {'Role': 'Server #', 'Round': 61, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.144625), 'test_loss': np.float64(102589.080292), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.696687), 'val_loss': np.float64(108052.338116), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.144625), 'test_loss': np.float64(102589.080292), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.696687), 'val_loss': np.float64(108052.338116), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.327106), 'test_avg_loss_bottom_decile': np.float64(24.287929), 'test_avg_loss_top_decile': np.float64(39.11797), 'test_avg_loss_min': np.float64(23.091083), 'test_avg_loss_max': np.float64(39.11797), 'test_avg_loss_bottom10%': np.float64(23.091083), 'test_avg_loss_top10%': np.float64(39.11797), 'test_avg_loss_cos1': np.float64(0.989157), 'test_avg_loss_entropy': np.float64(2.291876), 'test_loss_std': np.float64(15231.414178), 'test_loss_bottom_decile': np.float64(85493.510254), 'test_loss_top_decile': np.float64(137695.254761), 'test_loss_min': np.float64(81280.613342), 'test_loss_max': np.float64(137695.254761), 'test_loss_bottom10%': np.float64(81280.613342), 'test_loss_top10%': np.float64(137695.254761), 'test_loss_cos1': np.float64(0.989157), 'test_loss_entropy': np.float64(2.291876), 'val_avg_loss_std': np.float64(4.479159), 'val_avg_loss_bottom_decile': np.float64(25.769264), 'val_avg_loss_top_decile': np.float64(39.997357), 'val_avg_loss_min': np.float64(24.414526), 'val_avg_loss_max': np.float64(39.997357), 'val_avg_loss_bottom10%': np.float64(24.414526), 'val_avg_loss_top10%': np.float64(39.997357), 'val_avg_loss_cos1': np.float64(0.989521), 'val_avg_loss_entropy': np.float64(2.292138), 'val_loss_std': np.float64(15766.63864), 'val_loss_bottom_decile': np.float64(90707.809998), 'val_loss_top_decile': np.float64(140790.696533), 'val_loss_min': np.float64(85939.132996), 'val_loss_max': np.float64(140790.696533), 'val_loss_bottom10%': np.float64(85939.132996), 'val_loss_top10%': np.float64(140790.696533), 'val_loss_cos1': np.float64(0.989521), 'val_loss_entropy': np.float64(2.292138)}} 2024-11-14 21:14:27,296 (server:353) INFO: Server: Starting evaluation at the end of round 62. 2024-11-14 21:14:27,297 (server:359) INFO: ----------- Starting a new training round (Round #63) ------------- 2024-11-14 21:16:39,707 (client:354) INFO: {'Role': 'Client #8', 'Round': 63, 'Results_raw': {'train_loss': 22.418845, 'val_loss': 26.600993, 'test_loss': 21.891407}} 2024-11-14 21:17:23,871 (client:354) INFO: {'Role': 'Client #3', 'Round': 63, 'Results_raw': {'train_loss': 26.098648, 'val_loss': 26.057253, 'test_loss': 26.203508}} 2024-11-14 21:18:06,679 (client:354) INFO: {'Role': 'Client #10', 'Round': 63, 'Results_raw': {'train_loss': 22.077805, 'val_loss': 21.599668, 'test_loss': 22.015814}} 2024-11-14 21:18:49,304 (client:354) INFO: {'Role': 'Client #1', 'Round': 63, 'Results_raw': {'train_loss': 31.063138, 'val_loss': 30.27315, 'test_loss': 29.869583}} 2024-11-14 21:19:32,700 (client:354) INFO: {'Role': 'Client #9', 'Round': 63, 'Results_raw': {'train_loss': 24.670688, 'val_loss': 29.320465, 'test_loss': 24.859338}} 2024-11-14 21:20:16,161 (client:354) INFO: {'Role': 'Client #7', 'Round': 63, 'Results_raw': {'train_loss': 19.094479, 'val_loss': 19.43887, 'test_loss': 18.014622}} 2024-11-14 21:20:58,170 (client:354) INFO: {'Role': 'Client #4', 'Round': 63, 'Results_raw': {'train_loss': 24.416092, 'val_loss': 21.829977, 'test_loss': 22.884043}} 2024-11-14 21:21:41,482 (client:354) INFO: {'Role': 'Client #5', 'Round': 63, 'Results_raw': {'train_loss': 19.872585, 'val_loss': 20.307112, 'test_loss': 21.356822}} 2024-11-14 21:22:25,626 (client:354) INFO: {'Role': 'Client #2', 'Round': 63, 'Results_raw': {'train_loss': 23.146238, 'val_loss': 25.250791, 'test_loss': 24.108561}} 2024-11-14 21:23:09,645 (client:354) INFO: {'Role': 'Client #6', 'Round': 63, 'Results_raw': {'train_loss': 18.085066, 'val_loss': 19.368807, 'test_loss': 19.596914}} 2024-11-14 21:23:09,648 (server:615) INFO: {'Role': 'Server #', 'Round': 62, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.431768), 'test_loss': np.float64(103599.824084), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.957289), 'val_loss': np.float64(108969.658472), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.431768), 'test_loss': np.float64(103599.824084), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.957289), 'val_loss': np.float64(108969.658472), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.349835), 'test_avg_loss_bottom_decile': np.float64(24.540237), 'test_avg_loss_top_decile': np.float64(39.553446), 'test_avg_loss_min': np.float64(23.474041), 'test_avg_loss_max': np.float64(39.553446), 'test_avg_loss_bottom10%': np.float64(23.474041), 'test_avg_loss_top10%': np.float64(39.553446), 'test_avg_loss_cos1': np.float64(0.989254), 'test_avg_loss_entropy': np.float64(2.291996), 'test_loss_std': np.float64(15311.420633), 'test_loss_bottom_decile': np.float64(86381.633423), 'test_loss_top_decile': np.float64(139228.131348), 'test_loss_min': np.float64(82628.622681), 'test_loss_max': np.float64(139228.131348), 'test_loss_bottom10%': np.float64(82628.622681), 'test_loss_top10%': np.float64(139228.131348), 'test_loss_cos1': np.float64(0.989254), 'test_loss_entropy': np.float64(2.291996), 'val_avg_loss_std': np.float64(4.517944), 'val_avg_loss_bottom_decile': np.float64(26.019943), 'val_avg_loss_top_decile': np.float64(40.376306), 'val_avg_loss_min': np.float64(24.745193), 'val_avg_loss_max': np.float64(40.376306), 'val_avg_loss_bottom10%': np.float64(24.745193), 'val_avg_loss_top10%': np.float64(40.376306), 'val_avg_loss_cos1': np.float64(0.989518), 'val_avg_loss_entropy': np.float64(2.292152), 'val_loss_std': np.float64(15903.163755), 'val_loss_bottom_decile': np.float64(91590.198669), 'val_loss_top_decile': np.float64(142124.597534), 'val_loss_min': np.float64(87103.080566), 'val_loss_max': np.float64(142124.597534), 'val_loss_bottom10%': np.float64(87103.080566), 'val_loss_top10%': np.float64(142124.597534), 'val_loss_cos1': np.float64(0.989518), 'val_loss_entropy': np.float64(2.292152)}} 2024-11-14 21:23:09,689 (server:353) INFO: Server: Starting evaluation at the end of round 63. 2024-11-14 21:23:09,690 (server:359) INFO: ----------- Starting a new training round (Round #64) ------------- 2024-11-14 21:25:22,745 (client:354) INFO: {'Role': 'Client #10', 'Round': 64, 'Results_raw': {'train_loss': 22.115674, 'val_loss': 21.708671, 'test_loss': 22.139623}} 2024-11-14 21:26:06,043 (client:354) INFO: {'Role': 'Client #9', 'Round': 64, 'Results_raw': {'train_loss': 24.637771, 'val_loss': 29.416958, 'test_loss': 24.988399}} 2024-11-14 21:26:50,321 (client:354) INFO: {'Role': 'Client #1', 'Round': 64, 'Results_raw': {'train_loss': 31.157459, 'val_loss': 30.290378, 'test_loss': 29.970316}} 2024-11-14 21:27:34,189 (client:354) INFO: {'Role': 'Client #5', 'Round': 64, 'Results_raw': {'train_loss': 19.9032, 'val_loss': 20.073162, 'test_loss': 22.131509}} 2024-11-14 21:28:18,583 (client:354) INFO: {'Role': 'Client #2', 'Round': 64, 'Results_raw': {'train_loss': 23.164946, 'val_loss': 25.577653, 'test_loss': 24.388297}} 2024-11-14 21:29:02,215 (client:354) INFO: {'Role': 'Client #7', 'Round': 64, 'Results_raw': {'train_loss': 19.038002, 'val_loss': 19.389708, 'test_loss': 17.936373}} 2024-11-14 21:29:44,154 (client:354) INFO: {'Role': 'Client #6', 'Round': 64, 'Results_raw': {'train_loss': 17.989639, 'val_loss': 19.257024, 'test_loss': 19.010373}} 2024-11-14 21:30:27,306 (client:354) INFO: {'Role': 'Client #8', 'Round': 64, 'Results_raw': {'train_loss': 22.359303, 'val_loss': 25.982177, 'test_loss': 21.296138}} 2024-11-14 21:31:10,741 (client:354) INFO: {'Role': 'Client #3', 'Round': 64, 'Results_raw': {'train_loss': 26.067367, 'val_loss': 26.099568, 'test_loss': 26.151791}} 2024-11-14 21:31:55,071 (client:354) INFO: {'Role': 'Client #4', 'Round': 64, 'Results_raw': {'train_loss': 24.393769, 'val_loss': 21.30532, 'test_loss': 22.170169}} 2024-11-14 21:31:55,074 (server:615) INFO: {'Role': 'Server #', 'Round': 63, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.079856), 'test_loss': np.float64(102361.094318), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.629434), 'val_loss': np.float64(107815.609406), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.079856), 'test_loss': np.float64(102361.094318), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.629434), 'val_loss': np.float64(107815.609406), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.205312), 'test_avg_loss_bottom_decile': np.float64(24.389677), 'test_avg_loss_top_decile': np.float64(38.77902), 'test_avg_loss_min': np.float64(23.251217), 'test_avg_loss_max': np.float64(38.77902), 'test_avg_loss_bottom10%': np.float64(23.251217), 'test_avg_loss_top10%': np.float64(38.77902), 'test_avg_loss_cos1': np.float64(0.989705), 'test_avg_loss_entropy': np.float64(2.292427), 'test_loss_std': np.float64(14802.698402), 'test_loss_bottom_decile': np.float64(85851.661377), 'test_loss_top_decile': np.float64(136502.149536), 'test_loss_min': np.float64(81844.284729), 'test_loss_max': np.float64(136502.149536), 'test_loss_bottom10%': np.float64(81844.284729), 'test_loss_top10%': np.float64(136502.149536), 'test_loss_cos1': np.float64(0.989705), 'test_loss_entropy': np.float64(2.292427), 'val_avg_loss_std': np.float64(4.380346), 'val_avg_loss_bottom_decile': np.float64(25.850114), 'val_avg_loss_top_decile': np.float64(39.667941), 'val_avg_loss_min': np.float64(24.539265), 'val_avg_loss_max': np.float64(39.667941), 'val_avg_loss_bottom10%': np.float64(24.539265), 'val_avg_loss_top10%': np.float64(39.667941), 'val_avg_loss_cos1': np.float64(0.989928), 'val_avg_loss_entropy': np.float64(2.29255), 'val_loss_std': np.float64(15418.819655), 'val_loss_bottom_decile': np.float64(90992.401245), 'val_loss_top_decile': np.float64(139631.153076), 'val_loss_min': np.float64(86378.213501), 'val_loss_max': np.float64(139631.153076), 'val_loss_bottom10%': np.float64(86378.213501), 'val_loss_top10%': np.float64(139631.153076), 'val_loss_cos1': np.float64(0.989928), 'val_loss_entropy': np.float64(2.29255)}} 2024-11-14 21:31:55,108 (server:353) INFO: Server: Starting evaluation at the end of round 64. 2024-11-14 21:31:55,108 (server:359) INFO: ----------- Starting a new training round (Round #65) ------------- 2024-11-14 21:34:06,227 (client:354) INFO: {'Role': 'Client #2', 'Round': 65, 'Results_raw': {'train_loss': 23.142163, 'val_loss': 25.817273, 'test_loss': 24.380023}} 2024-11-14 21:34:50,716 (client:354) INFO: {'Role': 'Client #5', 'Round': 65, 'Results_raw': {'train_loss': 19.804238, 'val_loss': 20.59505, 'test_loss': 22.29633}} 2024-11-14 21:35:34,163 (client:354) INFO: {'Role': 'Client #10', 'Round': 65, 'Results_raw': {'train_loss': 22.108835, 'val_loss': 22.121658, 'test_loss': 22.532301}} 2024-11-14 21:36:17,885 (client:354) INFO: {'Role': 'Client #1', 'Round': 65, 'Results_raw': {'train_loss': 31.026084, 'val_loss': 30.360679, 'test_loss': 29.784415}} 2024-11-14 21:37:01,807 (client:354) INFO: {'Role': 'Client #7', 'Round': 65, 'Results_raw': {'train_loss': 19.026299, 'val_loss': 19.386483, 'test_loss': 17.974646}} 2024-11-14 21:37:46,136 (client:354) INFO: {'Role': 'Client #3', 'Round': 65, 'Results_raw': {'train_loss': 26.033005, 'val_loss': 25.965307, 'test_loss': 26.08597}} 2024-11-14 21:38:27,175 (client:354) INFO: {'Role': 'Client #9', 'Round': 65, 'Results_raw': {'train_loss': 24.603014, 'val_loss': 30.439887, 'test_loss': 25.349469}} 2024-11-14 21:39:10,694 (client:354) INFO: {'Role': 'Client #8', 'Round': 65, 'Results_raw': {'train_loss': 22.394675, 'val_loss': 26.47469, 'test_loss': 21.724752}} 2024-11-14 21:39:55,361 (client:354) INFO: {'Role': 'Client #4', 'Round': 65, 'Results_raw': {'train_loss': 24.363733, 'val_loss': 21.615751, 'test_loss': 22.694763}} 2024-11-14 21:40:40,420 (client:354) INFO: {'Role': 'Client #6', 'Round': 65, 'Results_raw': {'train_loss': 18.03613, 'val_loss': 19.32939, 'test_loss': 19.380488}} 2024-11-14 21:40:40,423 (server:615) INFO: {'Role': 'Server #', 'Round': 64, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.26257), 'test_loss': np.float64(103004.245709), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.810251), 'val_loss': np.float64(108452.084552), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.26257), 'test_loss': np.float64(103004.245709), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.810251), 'val_loss': np.float64(108452.084552), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.400298), 'test_avg_loss_bottom_decile': np.float64(24.234862), 'test_avg_loss_top_decile': np.float64(39.384034), 'test_avg_loss_min': np.float64(23.036419), 'test_avg_loss_max': np.float64(39.384034), 'test_avg_loss_bottom10%': np.float64(23.036419), 'test_avg_loss_top10%': np.float64(39.384034), 'test_avg_loss_cos1': np.float64(0.988882), 'test_avg_loss_entropy': np.float64(2.291588), 'test_loss_std': np.float64(15489.049086), 'test_loss_bottom_decile': np.float64(85306.71344), 'test_loss_top_decile': np.float64(138631.798706), 'test_loss_min': np.float64(81088.195374), 'test_loss_max': np.float64(138631.798706), 'test_loss_bottom10%': np.float64(81088.195374), 'test_loss_top10%': np.float64(138631.798706), 'test_loss_cos1': np.float64(0.988882), 'test_loss_entropy': np.float64(2.291588), 'val_avg_loss_std': np.float64(4.571464), 'val_avg_loss_bottom_decile': np.float64(25.731835), 'val_avg_loss_top_decile': np.float64(40.234166), 'val_avg_loss_min': np.float64(24.353115), 'val_avg_loss_max': np.float64(40.234166), 'val_avg_loss_bottom10%': np.float64(24.353115), 'val_avg_loss_top10%': np.float64(40.234166), 'val_avg_loss_cos1': np.float64(0.989171), 'val_avg_loss_entropy': np.float64(2.291773), 'val_loss_std': np.float64(16091.551978), 'val_loss_bottom_decile': np.float64(90576.059387), 'val_loss_top_decile': np.float64(141624.265869), 'val_loss_min': np.float64(85722.964417), 'val_loss_max': np.float64(141624.265869), 'val_loss_bottom10%': np.float64(85722.964417), 'val_loss_top10%': np.float64(141624.265869), 'val_loss_cos1': np.float64(0.989171), 'val_loss_entropy': np.float64(2.291773)}} 2024-11-14 21:40:40,462 (server:353) INFO: Server: Starting evaluation at the end of round 65. 2024-11-14 21:40:40,463 (server:359) INFO: ----------- Starting a new training round (Round #66) ------------- 2024-11-14 21:42:54,802 (client:354) INFO: {'Role': 'Client #4', 'Round': 66, 'Results_raw': {'train_loss': 24.351731, 'val_loss': 22.094231, 'test_loss': 23.157361}} 2024-11-14 21:43:41,983 (client:354) INFO: {'Role': 'Client #6', 'Round': 66, 'Results_raw': {'train_loss': 17.965178, 'val_loss': 19.25985, 'test_loss': 19.314641}} 2024-11-14 21:44:28,350 (client:354) INFO: {'Role': 'Client #9', 'Round': 66, 'Results_raw': {'train_loss': 24.622972, 'val_loss': 29.620509, 'test_loss': 25.196316}} 2024-11-14 21:45:14,770 (client:354) INFO: {'Role': 'Client #5', 'Round': 66, 'Results_raw': {'train_loss': 19.806568, 'val_loss': 20.732923, 'test_loss': 21.314714}} 2024-11-14 21:45:58,412 (client:354) INFO: {'Role': 'Client #2', 'Round': 66, 'Results_raw': {'train_loss': 23.107079, 'val_loss': 25.609584, 'test_loss': 24.246938}} 2024-11-14 21:46:39,994 (client:354) INFO: {'Role': 'Client #10', 'Round': 66, 'Results_raw': {'train_loss': 22.078315, 'val_loss': 22.013811, 'test_loss': 22.29337}} 2024-11-14 21:47:19,008 (client:354) INFO: {'Role': 'Client #8', 'Round': 66, 'Results_raw': {'train_loss': 22.290927, 'val_loss': 26.33456, 'test_loss': 22.003341}} 2024-11-14 21:48:00,212 (client:354) INFO: {'Role': 'Client #1', 'Round': 66, 'Results_raw': {'train_loss': 31.101518, 'val_loss': 30.282911, 'test_loss': 29.870947}} 2024-11-14 21:48:45,159 (client:354) INFO: {'Role': 'Client #7', 'Round': 66, 'Results_raw': {'train_loss': 19.07055, 'val_loss': 19.342252, 'test_loss': 17.980805}} 2024-11-14 21:49:31,595 (client:354) INFO: {'Role': 'Client #3', 'Round': 66, 'Results_raw': {'train_loss': 26.054828, 'val_loss': 26.06044, 'test_loss': 26.205164}} 2024-11-14 21:49:31,600 (server:615) INFO: {'Role': 'Server #', 'Round': 65, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.169611), 'test_loss': np.float64(102677.030426), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.705643), 'val_loss': np.float64(108083.864154), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.169611), 'test_loss': np.float64(102677.030426), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.705643), 'val_loss': np.float64(108083.864154), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.217705), 'test_avg_loss_bottom_decile': np.float64(24.708152), 'test_avg_loss_top_decile': np.float64(38.963462), 'test_avg_loss_min': np.float64(23.378233), 'test_avg_loss_max': np.float64(38.963462), 'test_avg_loss_bottom10%': np.float64(23.378233), 'test_avg_loss_top10%': np.float64(38.963462), 'test_avg_loss_cos1': np.float64(0.989708), 'test_avg_loss_entropy': np.float64(2.292452), 'test_loss_std': np.float64(14846.320691), 'test_loss_bottom_decile': np.float64(86972.694092), 'test_loss_top_decile': np.float64(137151.385498), 'test_loss_min': np.float64(82291.381714), 'test_loss_max': np.float64(137151.385498), 'test_loss_bottom10%': np.float64(82291.381714), 'test_loss_top10%': np.float64(137151.385498), 'test_loss_cos1': np.float64(0.989708), 'test_loss_entropy': np.float64(2.292452), 'val_avg_loss_std': np.float64(4.38097), 'val_avg_loss_bottom_decile': np.float64(26.179447), 'val_avg_loss_top_decile': np.float64(39.812023), 'val_avg_loss_min': np.float64(24.634344), 'val_avg_loss_max': np.float64(39.812023), 'val_avg_loss_bottom10%': np.float64(24.634344), 'val_avg_loss_top10%': np.float64(39.812023), 'val_avg_loss_cos1': np.float64(0.989975), 'val_avg_loss_entropy': np.float64(2.292614), 'val_loss_std': np.float64(15421.015291), 'val_loss_bottom_decile': np.float64(92151.653076), 'val_loss_top_decile': np.float64(140138.321411), 'val_loss_min': np.float64(86712.892273), 'val_loss_max': np.float64(140138.321411), 'val_loss_bottom10%': np.float64(86712.892273), 'val_loss_top10%': np.float64(140138.321411), 'val_loss_cos1': np.float64(0.989975), 'val_loss_entropy': np.float64(2.292614)}} 2024-11-14 21:49:31,643 (server:353) INFO: Server: Starting evaluation at the end of round 66. 2024-11-14 21:49:31,644 (server:359) INFO: ----------- Starting a new training round (Round #67) ------------- 2024-11-14 21:51:42,008 (client:354) INFO: {'Role': 'Client #3', 'Round': 67, 'Results_raw': {'train_loss': 26.049184, 'val_loss': 26.121187, 'test_loss': 26.135793}} 2024-11-14 21:52:23,292 (client:354) INFO: {'Role': 'Client #9', 'Round': 67, 'Results_raw': {'train_loss': 24.631444, 'val_loss': 29.55034, 'test_loss': 24.884859}} 2024-11-14 21:53:03,498 (client:354) INFO: {'Role': 'Client #5', 'Round': 67, 'Results_raw': {'train_loss': 19.809283, 'val_loss': 20.556837, 'test_loss': 22.081903}} 2024-11-14 21:53:42,860 (client:354) INFO: {'Role': 'Client #7', 'Round': 67, 'Results_raw': {'train_loss': 18.976955, 'val_loss': 19.649333, 'test_loss': 18.10615}} 2024-11-14 21:54:22,547 (client:354) INFO: {'Role': 'Client #4', 'Round': 67, 'Results_raw': {'train_loss': 24.319576, 'val_loss': 21.61151, 'test_loss': 22.634808}} 2024-11-14 21:55:00,833 (client:354) INFO: {'Role': 'Client #1', 'Round': 67, 'Results_raw': {'train_loss': 31.041462, 'val_loss': 30.097802, 'test_loss': 29.933454}} 2024-11-14 21:55:40,431 (client:354) INFO: {'Role': 'Client #6', 'Round': 67, 'Results_raw': {'train_loss': 17.979364, 'val_loss': 19.339118, 'test_loss': 19.455092}} 2024-11-14 21:56:19,890 (client:354) INFO: {'Role': 'Client #10', 'Round': 67, 'Results_raw': {'train_loss': 22.068595, 'val_loss': 21.901427, 'test_loss': 22.205838}} 2024-11-14 21:56:59,549 (client:354) INFO: {'Role': 'Client #2', 'Round': 67, 'Results_raw': {'train_loss': 23.07128, 'val_loss': 25.345005, 'test_loss': 24.1861}} 2024-11-14 21:57:39,263 (client:354) INFO: {'Role': 'Client #8', 'Round': 67, 'Results_raw': {'train_loss': 22.238594, 'val_loss': 26.139542, 'test_loss': 21.936357}} 2024-11-14 21:57:39,267 (server:615) INFO: {'Role': 'Server #', 'Round': 66, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.15751), 'test_loss': np.float64(102634.434833), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.709593), 'val_loss': np.float64(108097.767401), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.15751), 'test_loss': np.float64(102634.434833), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.709593), 'val_loss': np.float64(108097.767401), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.26716), 'test_avg_loss_bottom_decile': np.float64(24.421109), 'test_avg_loss_top_decile': np.float64(39.02207), 'test_avg_loss_min': np.float64(23.259758), 'test_avg_loss_max': np.float64(39.02207), 'test_avg_loss_bottom10%': np.float64(23.259758), 'test_avg_loss_top10%': np.float64(39.02207), 'test_avg_loss_cos1': np.float64(0.98946), 'test_avg_loss_entropy': np.float64(2.292184), 'test_loss_std': np.float64(15020.402872), 'test_loss_bottom_decile': np.float64(85962.303772), 'test_loss_top_decile': np.float64(137357.686646), 'test_loss_min': np.float64(81874.34906), 'test_loss_max': np.float64(137357.686646), 'test_loss_bottom10%': np.float64(81874.34906), 'test_loss_top10%': np.float64(137357.686646), 'test_loss_cos1': np.float64(0.98946), 'test_loss_entropy': np.float64(2.292184), 'val_avg_loss_std': np.float64(4.448822), 'val_avg_loss_bottom_decile': np.float64(25.88793), 'val_avg_loss_top_decile': np.float64(39.892439), 'val_avg_loss_min': np.float64(24.51127), 'val_avg_loss_max': np.float64(39.892439), 'val_avg_loss_bottom10%': np.float64(24.51127), 'val_avg_loss_top10%': np.float64(39.892439), 'val_avg_loss_cos1': np.float64(0.989669), 'val_avg_loss_entropy': np.float64(2.292286), 'val_loss_std': np.float64(15659.852088), 'val_loss_bottom_decile': np.float64(91125.513855), 'val_loss_top_decile': np.float64(140421.385254), 'val_loss_min': np.float64(86279.671204), 'val_loss_max': np.float64(140421.385254), 'val_loss_bottom10%': np.float64(86279.671204), 'val_loss_top10%': np.float64(140421.385254), 'val_loss_cos1': np.float64(0.989669), 'val_loss_entropy': np.float64(2.292286)}} 2024-11-14 21:57:39,303 (server:353) INFO: Server: Starting evaluation at the end of round 67. 2024-11-14 21:57:39,305 (server:359) INFO: ----------- Starting a new training round (Round #68) ------------- 2024-11-14 21:59:38,557 (client:354) INFO: {'Role': 'Client #9', 'Round': 68, 'Results_raw': {'train_loss': 24.53568, 'val_loss': 29.576586, 'test_loss': 24.941452}} 2024-11-14 22:00:17,909 (client:354) INFO: {'Role': 'Client #2', 'Round': 68, 'Results_raw': {'train_loss': 23.018981, 'val_loss': 25.43239, 'test_loss': 24.249919}} 2024-11-14 22:00:57,115 (client:354) INFO: {'Role': 'Client #5', 'Round': 68, 'Results_raw': {'train_loss': 19.906546, 'val_loss': 20.041604, 'test_loss': 22.355856}} 2024-11-14 22:01:37,359 (client:354) INFO: {'Role': 'Client #1', 'Round': 68, 'Results_raw': {'train_loss': 31.124702, 'val_loss': 30.170777, 'test_loss': 29.936777}} 2024-11-14 22:02:17,498 (client:354) INFO: {'Role': 'Client #10', 'Round': 68, 'Results_raw': {'train_loss': 22.002766, 'val_loss': 21.718961, 'test_loss': 21.860521}} 2024-11-14 22:02:55,765 (client:354) INFO: {'Role': 'Client #4', 'Round': 68, 'Results_raw': {'train_loss': 24.298669, 'val_loss': 21.75278, 'test_loss': 22.780447}} 2024-11-14 22:03:35,598 (client:354) INFO: {'Role': 'Client #3', 'Round': 68, 'Results_raw': {'train_loss': 26.034332, 'val_loss': 25.917889, 'test_loss': 25.918539}} 2024-11-14 22:04:15,480 (client:354) INFO: {'Role': 'Client #7', 'Round': 68, 'Results_raw': {'train_loss': 19.054683, 'val_loss': 19.198584, 'test_loss': 17.851579}} 2024-11-14 22:04:55,690 (client:354) INFO: {'Role': 'Client #8', 'Round': 68, 'Results_raw': {'train_loss': 22.326055, 'val_loss': 26.938949, 'test_loss': 22.172211}} 2024-11-14 22:05:35,383 (client:354) INFO: {'Role': 'Client #6', 'Round': 68, 'Results_raw': {'train_loss': 17.902047, 'val_loss': 19.806338, 'test_loss': 20.016612}} 2024-11-14 22:05:35,387 (server:615) INFO: {'Role': 'Server #', 'Round': 67, 'Results_weighted_avg': {'test_avg_loss': np.float64(29.187174), 'test_loss': np.float64(102738.85401), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.719254), 'val_loss': np.float64(108131.77558), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(29.187174), 'test_loss': np.float64(102738.85401), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.719254), 'val_loss': np.float64(108131.77558), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.261614), 'test_avg_loss_bottom_decile': np.float64(24.663142), 'test_avg_loss_top_decile': np.float64(39.125508), 'test_avg_loss_min': np.float64(23.374805), 'test_avg_loss_max': np.float64(39.125508), 'test_avg_loss_bottom10%': np.float64(23.374805), 'test_avg_loss_top10%': np.float64(39.125508), 'test_avg_loss_cos1': np.float64(0.989508), 'test_avg_loss_entropy': np.float64(2.292265), 'test_loss_std': np.float64(15000.880779), 'test_loss_bottom_decile': np.float64(86814.259277), 'test_loss_top_decile': np.float64(137721.787231), 'test_loss_min': np.float64(82279.314148), 'test_loss_max': np.float64(137721.787231), 'test_loss_bottom10%': np.float64(82279.314148), 'test_loss_top10%': np.float64(137721.787231), 'test_loss_cos1': np.float64(0.989508), 'test_loss_entropy': np.float64(2.292265), 'val_avg_loss_std': np.float64(4.430959), 'val_avg_loss_bottom_decile': np.float64(26.139986), 'val_avg_loss_top_decile': np.float64(39.955178), 'val_avg_loss_min': np.float64(24.607566), 'val_avg_loss_max': np.float64(39.955178), 'val_avg_loss_bottom10%': np.float64(24.607566), 'val_avg_loss_top10%': np.float64(39.955178), 'val_avg_loss_cos1': np.float64(0.989757), 'val_avg_loss_entropy': np.float64(2.292401), 'val_loss_std': np.float64(15596.975061), 'val_loss_bottom_decile': np.float64(92012.748962), 'val_loss_top_decile': np.float64(140642.224854), 'val_loss_min': np.float64(86618.631226), 'val_loss_max': np.float64(140642.224854), 'val_loss_bottom10%': np.float64(86618.631226), 'val_loss_top10%': np.float64(140642.224854), 'val_loss_cos1': np.float64(0.989757), 'val_loss_entropy': np.float64(2.292401)}} 2024-11-14 22:05:35,423 (server:353) INFO: Server: Starting evaluation at the end of round 68. 2024-11-14 22:05:35,424 (server:359) INFO: ----------- Starting a new training round (Round #69) ------------- 2024-11-14 22:07:33,385 (client:354) INFO: {'Role': 'Client #10', 'Round': 69, 'Results_raw': {'train_loss': 22.270472, 'val_loss': 21.891255, 'test_loss': 22.074676}} 2024-11-14 22:08:12,978 (client:354) INFO: {'Role': 'Client #8', 'Round': 69, 'Results_raw': {'train_loss': 22.350506, 'val_loss': 26.679906, 'test_loss': 21.842696}} 2024-11-14 22:08:52,806 (client:354) INFO: {'Role': 'Client #6', 'Round': 69, 'Results_raw': {'train_loss': 17.954471, 'val_loss': 19.355742, 'test_loss': 19.39883}} 2024-11-14 22:09:32,674 (client:354) INFO: {'Role': 'Client #5', 'Round': 69, 'Results_raw': {'train_loss': 19.856438, 'val_loss': 20.425228, 'test_loss': 22.815853}} 2024-11-14 22:10:11,341 (client:354) INFO: {'Role': 'Client #9', 'Round': 69, 'Results_raw': {'train_loss': 24.550077, 'val_loss': 29.914484, 'test_loss': 25.058418}} 2024-11-14 22:10:47,327 (client:354) INFO: {'Role': 'Client #7', 'Round': 69, 'Results_raw': {'train_loss': 19.047838, 'val_loss': 19.301337, 'test_loss': 18.015775}} 2024-11-14 22:11:27,838 (client:354) INFO: {'Role': 'Client #2', 'Round': 69, 'Results_raw': {'train_loss': 23.051552, 'val_loss': 25.31128, 'test_loss': 24.070791}} 2024-11-14 22:12:07,298 (client:354) INFO: {'Role': 'Client #4', 'Round': 69, 'Results_raw': {'train_loss': 24.314673, 'val_loss': 21.699059, 'test_loss': 22.956678}} 2024-11-14 22:12:46,972 (client:354) INFO: {'Role': 'Client #1', 'Round': 69, 'Results_raw': {'train_loss': 31.033692, 'val_loss': 30.272162, 'test_loss': 30.139767}} 2024-11-14 22:13:27,543 (client:354) INFO: {'Role': 'Client #3', 'Round': 69, 'Results_raw': {'train_loss': 26.049534, 'val_loss': 26.137748, 'test_loss': 26.208078}} 2024-11-14 22:13:27,547 (server:615) INFO: {'Role': 'Server #', 'Round': 68, 'Results_weighted_avg': {'test_avg_loss': np.float64(28.890094), 'test_loss': np.float64(101693.130634), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.419955), 'val_loss': np.float64(107078.240643), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(28.890094), 'test_loss': np.float64(101693.130634), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.419955), 'val_loss': np.float64(107078.240643), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.275677), 'test_avg_loss_bottom_decile': np.float64(24.067607), 'test_avg_loss_top_decile': np.float64(38.860917), 'test_avg_loss_min': np.float64(23.130011), 'test_avg_loss_max': np.float64(38.860917), 'test_avg_loss_bottom10%': np.float64(23.130011), 'test_avg_loss_top10%': np.float64(38.860917), 'test_avg_loss_cos1': np.float64(0.989225), 'test_avg_loss_entropy': np.float64(2.291977), 'test_loss_std': np.float64(15050.381401), 'test_loss_bottom_decile': np.float64(84717.977783), 'test_loss_top_decile': np.float64(136790.427856), 'test_loss_min': np.float64(81417.639038), 'test_loss_max': np.float64(136790.427856), 'test_loss_bottom10%': np.float64(81417.639038), 'test_loss_top10%': np.float64(136790.427856), 'test_loss_cos1': np.float64(0.989225), 'test_loss_entropy': np.float64(2.291977), 'val_avg_loss_std': np.float64(4.450337), 'val_avg_loss_bottom_decile': np.float64(25.551384), 'val_avg_loss_top_decile': np.float64(39.684042), 'val_avg_loss_min': np.float64(24.424482), 'val_avg_loss_max': np.float64(39.684042), 'val_avg_loss_bottom10%': np.float64(24.424482), 'val_avg_loss_top10%': np.float64(39.684042), 'val_avg_loss_cos1': np.float64(0.989467), 'val_avg_loss_entropy': np.float64(2.292104), 'val_loss_std': np.float64(15665.184745), 'val_loss_bottom_decile': np.float64(89940.872498), 'val_loss_top_decile': np.float64(139687.828735), 'val_loss_min': np.float64(85974.174927), 'val_loss_max': np.float64(139687.828735), 'val_loss_bottom10%': np.float64(85974.174927), 'val_loss_top10%': np.float64(139687.828735), 'val_loss_cos1': np.float64(0.989467), 'val_loss_entropy': np.float64(2.292104)}} 2024-11-14 22:13:27,582 (server:370) INFO: Server: Training is finished! Starting evaluation. 2024-11-14 22:14:43,448 (server:615) INFO: {'Role': 'Server #', 'Round': 69, 'Results_weighted_avg': {'test_avg_loss': np.float64(28.994098), 'test_loss': np.float64(102059.226453), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.541169), 'val_loss': np.float64(107504.913135), 'val_total': np.float64(3520.0)}, 'Results_avg': {'test_avg_loss': np.float64(28.994098), 'test_loss': np.float64(102059.226453), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.541169), 'val_loss': np.float64(107504.913135), 'val_total': np.float64(3520.0)}, 'Results_fairness': {'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(4.166413), 'test_avg_loss_bottom_decile': np.float64(24.510409), 'test_avg_loss_top_decile': np.float64(38.647821), 'test_avg_loss_min': np.float64(23.400457), 'test_avg_loss_max': np.float64(38.647821), 'test_avg_loss_bottom10%': np.float64(23.400457), 'test_avg_loss_top10%': np.float64(38.647821), 'test_avg_loss_cos1': np.float64(0.989833), 'test_avg_loss_entropy': np.float64(2.292581), 'test_loss_std': np.float64(14665.772712), 'test_loss_bottom_decile': np.float64(86276.641357), 'test_loss_top_decile': np.float64(136040.329834), 'test_loss_min': np.float64(82369.609375), 'test_loss_max': np.float64(136040.329834), 'test_loss_bottom10%': np.float64(82369.609375), 'test_loss_top10%': np.float64(136040.329834), 'test_loss_cos1': np.float64(0.989833), 'test_loss_entropy': np.float64(2.292581), 'val_avg_loss_std': np.float64(4.342524), 'val_avg_loss_bottom_decile': np.float64(25.992057), 'val_avg_loss_top_decile': np.float64(39.435845), 'val_avg_loss_min': np.float64(24.681342), 'val_avg_loss_max': np.float64(39.435845), 'val_avg_loss_bottom10%': np.float64(24.681342), 'val_avg_loss_top10%': np.float64(39.435845), 'val_avg_loss_cos1': np.float64(0.990042), 'val_avg_loss_entropy': np.float64(2.292684), 'val_loss_std': np.float64(15285.682954), 'val_loss_bottom_decile': np.float64(91492.039062), 'val_loss_top_decile': np.float64(138814.175293), 'val_loss_min': np.float64(86878.325073), 'val_loss_max': np.float64(138814.175293), 'val_loss_bottom10%': np.float64(86878.325073), 'val_loss_top10%': np.float64(138814.175293), 'val_loss_cos1': np.float64(0.990042), 'val_loss_entropy': np.float64(2.292684)}} 2024-11-14 22:14:43,451 (server:420) INFO: Server: Final evaluation is finished! Starting merging results. 2024-11-14 22:14:43,452 (server:546) INFO: {'Role': 'Server #', 'Round': 'Final', 'Results_raw': {'client_best_individual': {'val_loss': 85722.964417, 'test_avg_loss': 23.036419, 'test_loss': 81088.195374, 'test_total': 3520.0, 'val_avg_loss': 24.353115, 'val_total': 3520.0}, 'client_summarized_weighted_avg': {'val_loss': np.float64(107078.240643), 'test_avg_loss': np.float64(28.890094), 'test_loss': np.float64(101693.130634), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.419955), 'val_total': np.float64(3520.0)}, 'client_summarized_avg': {'val_loss': np.float64(107078.240643), 'test_avg_loss': np.float64(28.890094), 'test_loss': np.float64(101693.130634), 'test_total': np.float64(3520.0), 'val_avg_loss': np.float64(30.419955), 'val_total': np.float64(3520.0)}, 'client_summarized_fairness': {'val_loss_entropy': np.float64(2.290176), 'val_loss_cos1': np.float64(0.987974), 'val_loss_top10%': np.float64(175318.280151), 'val_loss_bottom10%': np.float64(101640.37207), 'val_loss_max': np.float64(175318.280151), 'val_loss_min': np.float64(101640.37207), 'val_loss_top_decile': np.float64(175318.280151), 'val_loss_bottom_decile': np.float64(103173.011597), 'val_loss_std': np.float64(21191.35416), 'test_total': np.float64(3520.0), 'val_total': np.float64(3520.0), 'test_avg_loss_std': np.float64(5.824384), 'test_avg_loss_bottom_decile': np.float64(28.079359), 'test_avg_loss_top_decile': np.float64(48.245355), 'test_avg_loss_min': np.float64(27.516944), 'test_avg_loss_max': np.float64(48.245355), 'test_avg_loss_bottom10%': np.float64(27.516944), 'test_avg_loss_top10%': np.float64(48.245355), 'test_avg_loss_cos1': np.float64(0.987715), 'test_avg_loss_entropy': np.float64(2.289943), 'test_loss_std': np.float64(20501.830224), 'test_loss_bottom_decile': np.float64(98839.344238), 'test_loss_top_decile': np.float64(169823.648682), 'test_loss_min': np.float64(96859.641174), 'test_loss_max': np.float64(169823.648682), 'test_loss_bottom10%': np.float64(96859.641174), 'test_loss_top10%': np.float64(169823.648682), 'test_loss_cos1': np.float64(0.987715), 'test_loss_entropy': np.float64(2.289943), 'val_avg_loss_std': np.float64(6.020271), 'val_avg_loss_bottom_decile': np.float64(29.310515), 'val_avg_loss_top_decile': np.float64(49.80633), 'val_avg_loss_min': np.float64(28.875106), 'val_avg_loss_max': np.float64(49.80633), 'val_avg_loss_bottom10%': np.float64(28.875106), 'val_avg_loss_top10%': np.float64(49.80633), 'val_avg_loss_cos1': np.float64(0.987974), 'val_avg_loss_entropy': np.float64(2.290176)}}} 2024-11-14 22:14:43,454 (server:565) INFO: {'Role': 'Client #1', 'Round': 70, 'Results_raw': {'test_avg_loss': 38.647821, 'test_loss': 136040.329834, 'test_total': 3520, 'val_avg_loss': 39.435845, 'val_loss': 138814.175293, 'val_total': 3520}} 2024-11-14 22:14:43,455 (server:565) INFO: {'Role': 'Client #2', 'Round': 70, 'Results_raw': {'test_avg_loss': 29.181073, 'test_loss': 102717.376953, 'test_total': 3520, 'val_avg_loss': 30.431367, 'val_loss': 107118.413452, 'val_total': 3520}} 2024-11-14 22:14:43,455 (server:565) INFO: {'Role': 'Client #3', 'Round': 70, 'Results_raw': {'test_avg_loss': 31.36094, 'test_loss': 110390.507324, 'test_total': 3520, 'val_avg_loss': 31.704706, 'val_loss': 111600.564209, 'val_total': 3520}} 2024-11-14 22:14:43,456 (server:565) INFO: {'Role': 'Client #4', 'Round': 70, 'Results_raw': {'test_avg_loss': 28.678003, 'test_loss': 100946.569702, 'test_total': 3520, 'val_avg_loss': 28.994566, 'val_loss': 102060.873413, 'val_total': 3520}} 2024-11-14 22:14:43,456 (server:565) INFO: {'Role': 'Client #5', 'Round': 70, 'Results_raw': {'test_avg_loss': 25.334028, 'test_loss': 89175.777832, 'test_total': 3520, 'val_avg_loss': 26.711888, 'val_loss': 94025.844421, 'val_total': 3520}} 2024-11-14 22:14:43,456 (server:565) INFO: {'Role': 'Client #6', 'Round': 70, 'Results_raw': {'test_avg_loss': 23.400457, 'test_loss': 82369.609375, 'test_total': 3520, 'val_avg_loss': 24.681342, 'val_loss': 86878.325073, 'val_total': 3520}} 2024-11-14 22:14:43,457 (server:565) INFO: {'Role': 'Client #7', 'Round': 70, 'Results_raw': {'test_avg_loss': 24.510409, 'test_loss': 86276.641357, 'test_total': 3520, 'val_avg_loss': 25.992057, 'val_loss': 91492.039062, 'val_total': 3520}} 2024-11-14 22:14:43,457 (server:565) INFO: {'Role': 'Client #8', 'Round': 70, 'Results_raw': {'test_avg_loss': 28.289726, 'test_loss': 99579.836182, 'test_total': 3520, 'val_avg_loss': 33.399953, 'val_loss': 117567.833618, 'val_total': 3520}} 2024-11-14 22:14:43,457 (server:565) INFO: {'Role': 'Client #9', 'Round': 70, 'Results_raw': {'test_avg_loss': 32.018855, 'test_loss': 112706.368347, 'test_total': 3520, 'val_avg_loss': 35.450568, 'val_loss': 124785.999329, 'val_total': 3520}} 2024-11-14 22:14:43,458 (server:565) INFO: {'Role': 'Client #10', 'Round': 70, 'Results_raw': {'test_avg_loss': 28.519673, 'test_loss': 100389.24762, 'test_total': 3520, 'val_avg_loss': 28.609393, 'val_loss': 100705.063477, 'val_total': 3520}} 2024-11-14 22:14:43,460 (monitor:173) INFO: In worker #0, the system-related metrics are: {'id': 0, 'fl_end_time_minutes': 615.650005, '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 22:14:43,462 (client:582) INFO: ================= client 1 received finish message ================= 2024-11-14 22:14:43,466 (monitor:173) INFO: In worker #1, the system-related metrics are: {'id': 1, 'fl_end_time_minutes': 615.649833, 'total_model_size': 563454, 'total_flops': 11319874399680.0, '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 22:14:43,466 (client:582) INFO: ================= client 2 received finish message ================= 2024-11-14 22:14:43,470 (monitor:173) INFO: In worker #2, the system-related metrics are: {'id': 2, 'fl_end_time_minutes': 615.649517, 'total_model_size': 563454, 'total_flops': 11319874399680.0, '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 22:14:43,471 (client:582) INFO: ================= client 3 received finish message ================= 2024-11-14 22:14:43,474 (monitor:173) INFO: In worker #3, the system-related metrics are: {'id': 3, 'fl_end_time_minutes': 615.649244, 'total_model_size': 563454, 'total_flops': 11319874399680.0, '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 22:14:43,475 (client:582) INFO: ================= client 4 received finish message ================= 2024-11-14 22:14:43,480 (monitor:173) INFO: In worker #4, the system-related metrics are: {'id': 4, 'fl_end_time_minutes': 615.649073, 'total_model_size': 563454, 'total_flops': 11319874399680.0, '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 22:14:43,480 (client:582) INFO: ================= client 5 received finish message ================= 2024-11-14 22:14:43,484 (monitor:173) INFO: In worker #5, the system-related metrics are: {'id': 5, 'fl_end_time_minutes': 615.648853, 'total_model_size': 563454, 'total_flops': 11319874399680.0, '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 22:14:43,484 (client:582) INFO: ================= client 6 received finish message ================= 2024-11-14 22:14:43,489 (monitor:173) INFO: In worker #6, the system-related metrics are: {'id': 6, 'fl_end_time_minutes': 615.648616, 'total_model_size': 563454, 'total_flops': 11319874399680.0, '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 22:14:43,489 (client:582) INFO: ================= client 7 received finish message ================= 2024-11-14 22:14:43,494 (monitor:173) INFO: In worker #7, the system-related metrics are: {'id': 7, 'fl_end_time_minutes': 615.64837, 'total_model_size': 563454, 'total_flops': 11319874399680.0, '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 22:14:43,494 (client:582) INFO: ================= client 8 received finish message ================= 2024-11-14 22:14:43,498 (monitor:173) INFO: In worker #8, the system-related metrics are: {'id': 8, 'fl_end_time_minutes': 615.648124, 'total_model_size': 563454, 'total_flops': 11319874399680.0, '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 22:14:43,499 (client:582) INFO: ================= client 9 received finish message ================= 2024-11-14 22:14:43,502 (monitor:173) INFO: In worker #9, the system-related metrics are: {'id': 9, 'fl_end_time_minutes': 615.647896, 'total_model_size': 563454, 'total_flops': 11319874399680.0, '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 22:14:43,502 (client:582) INFO: ================= client 10 received finish message ================= 2024-11-14 22:14:43,505 (monitor:173) INFO: In worker #10, the system-related metrics are: {'id': 10, 'fl_end_time_minutes': 615.647655, 'total_model_size': 563454, 'total_flops': 11319874399680.0, '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 22:14:43,506 (monitor:338) INFO: We will compress the file eval_results.raw into a .gz file, and delete the old one 2024-11-14 22:14:43,538 (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(615.648835), 'sys_avg/total_model_size': '500.23K', 'sys_avg/total_flops': '9.36T', '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 22:14:43,538 (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(0.000745), 'sys_std/total_model_size': '158.19K', 'sys_std/total_flops': '2.96T', '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)})