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lunwentest_cifar10_noiid_noper.log
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nohup: ignoring input
cuda:2
Namespace(batch_size=1500, bn_sparsity=0.9, classes_per_user=4, clip_bound=1.8, clipping_style='all-layer', cluster_project_lr=0.05, cluster_temperature=1.0, dataset='CIFAR-10', dataset_dir='/home/chenyannan/fast-differential-privacy-main/examples/image_classification/data', downsample_lr=0.04, epochs=50, epsilon=8, feature_dim=128, global_lr=1.06, image_size=224, instance_project_lr=0.04, instance_temperature=0.5, kl_threshold=0.7, learning_rate=0.0003, linear_sparsity=0.75, local_epoch=3, loss_KL=0.5, mini_bs=125, miu=1, model_path='save/Cifar-10-DPFL-ResNet18-noiid-classes_per_user7-num_class7-noper', momentum=0.3, n_clients=40, num_class=4, r_conv=6, r_proj=16, reload=False, resnet='ResNet18_lora', resnet_lr=0.1, sample_ratio=1, seed=17, smooth_K=6, smooth_loss_radius=2, smooth_step=0, start_epoch=0, test_image_size=256, trans_lr=0.05, weight_decay=1e-05, workers=8)
len label: 60000
tensor([1, 2, 7, 8]) 1500
save/Img-10-pretrain-transform/checkpoint_532.tar
trans_alpha 150528 torch.Size([3, 224, 224])
trans_beta 150528 torch.Size([3, 224, 224])
resnet.conv1.weight 9408 torch.Size([64, 3, 7, 7])
resnet.conv1.lora_A 882 torch.Size([42, 21])
resnet.conv1.lora_B 18816 torch.Size([448, 42])
resnet.bn1.weight 64 torch.Size([64])
resnet.bn1.bias 64 torch.Size([64])
resnet.layer1.0.conv1.weight 36864 torch.Size([64, 64, 3, 3])
resnet.layer1.0.conv1.lora_A 3456 torch.Size([18, 192])
resnet.layer1.0.conv1.lora_B 3456 torch.Size([192, 18])
resnet.layer1.0.bn1.weight 64 torch.Size([64])
resnet.layer1.0.bn1.bias 64 torch.Size([64])
resnet.layer1.0.conv2.weight 36864 torch.Size([64, 64, 3, 3])
resnet.layer1.0.conv2.lora_A 3456 torch.Size([18, 192])
resnet.layer1.0.conv2.lora_B 3456 torch.Size([192, 18])
resnet.layer1.0.bn2.weight 64 torch.Size([64])
resnet.layer1.0.bn2.bias 64 torch.Size([64])
resnet.layer1.1.conv1.weight 36864 torch.Size([64, 64, 3, 3])
resnet.layer1.1.bn1.weight 64 torch.Size([64])
resnet.layer1.1.bn1.bias 64 torch.Size([64])
resnet.layer1.1.conv2.weight 36864 torch.Size([64, 64, 3, 3])
resnet.layer1.1.bn2.weight 64 torch.Size([64])
resnet.layer1.1.bn2.bias 64 torch.Size([64])
resnet.layer2.0.conv1.weight 73728 torch.Size([128, 64, 3, 3])
resnet.layer2.0.conv1.lora_A 3456 torch.Size([18, 192])
resnet.layer2.0.conv1.lora_B 6912 torch.Size([384, 18])
resnet.layer2.0.bn1.weight 128 torch.Size([128])
resnet.layer2.0.bn1.bias 128 torch.Size([128])
resnet.layer2.0.conv2.weight 147456 torch.Size([128, 128, 3, 3])
resnet.layer2.0.conv2.lora_A 6912 torch.Size([18, 384])
resnet.layer2.0.conv2.lora_B 6912 torch.Size([384, 18])
resnet.layer2.0.bn2.weight 128 torch.Size([128])
resnet.layer2.0.bn2.bias 128 torch.Size([128])
resnet.layer2.0.downsample.0.weight 8192 torch.Size([128, 64, 1, 1])
resnet.layer2.0.downsample.0.lora_A 2304 torch.Size([36, 64])
resnet.layer2.0.downsample.0.lora_B 4608 torch.Size([128, 36])
resnet.layer2.0.downsample.1.weight 128 torch.Size([128])
resnet.layer2.0.downsample.1.bias 128 torch.Size([128])
resnet.layer2.1.conv1.weight 147456 torch.Size([128, 128, 3, 3])
resnet.layer2.1.bn1.weight 128 torch.Size([128])
resnet.layer2.1.bn1.bias 128 torch.Size([128])
resnet.layer2.1.conv2.weight 147456 torch.Size([128, 128, 3, 3])
resnet.layer2.1.bn2.weight 128 torch.Size([128])
resnet.layer2.1.bn2.bias 128 torch.Size([128])
resnet.layer3.0.conv1.weight 294912 torch.Size([256, 128, 3, 3])
resnet.layer3.0.conv1.lora_A 6912 torch.Size([18, 384])
resnet.layer3.0.conv1.lora_B 13824 torch.Size([768, 18])
resnet.layer3.0.bn1.weight 256 torch.Size([256])
resnet.layer3.0.bn1.bias 256 torch.Size([256])
resnet.layer3.0.conv2.weight 589824 torch.Size([256, 256, 3, 3])
resnet.layer3.0.conv2.lora_A 13824 torch.Size([18, 768])
resnet.layer3.0.conv2.lora_B 13824 torch.Size([768, 18])
resnet.layer3.0.bn2.weight 256 torch.Size([256])
resnet.layer3.0.bn2.bias 256 torch.Size([256])
resnet.layer3.0.downsample.0.weight 32768 torch.Size([256, 128, 1, 1])
resnet.layer3.0.downsample.0.lora_A 4608 torch.Size([36, 128])
resnet.layer3.0.downsample.0.lora_B 9216 torch.Size([256, 36])
resnet.layer3.0.downsample.1.weight 256 torch.Size([256])
resnet.layer3.0.downsample.1.bias 256 torch.Size([256])
resnet.layer3.1.conv1.weight 589824 torch.Size([256, 256, 3, 3])
resnet.layer3.1.conv1.lora_A 13824 torch.Size([18, 768])
resnet.layer3.1.conv1.lora_B 13824 torch.Size([768, 18])
resnet.layer3.1.bn1.weight 256 torch.Size([256])
resnet.layer3.1.bn1.bias 256 torch.Size([256])
resnet.layer3.1.conv2.weight 589824 torch.Size([256, 256, 3, 3])
resnet.layer3.1.conv2.lora_A 13824 torch.Size([18, 768])
resnet.layer3.1.conv2.lora_B 13824 torch.Size([768, 18])
resnet.layer3.1.bn2.weight 256 torch.Size([256])
resnet.layer3.1.bn2.bias 256 torch.Size([256])
resnet.layer4.0.conv1.weight 1179648 torch.Size([512, 256, 3, 3])
resnet.layer4.0.conv1.lora_A 13824 torch.Size([18, 768])
resnet.layer4.0.conv1.lora_B 27648 torch.Size([1536, 18])
resnet.layer4.0.bn1.weight 512 torch.Size([512])
resnet.layer4.0.bn1.bias 512 torch.Size([512])
resnet.layer4.0.conv2.weight 2359296 torch.Size([512, 512, 3, 3])
resnet.layer4.0.conv2.lora_A 27648 torch.Size([18, 1536])
resnet.layer4.0.conv2.lora_B 27648 torch.Size([1536, 18])
resnet.layer4.0.bn2.weight 512 torch.Size([512])
resnet.layer4.0.bn2.bias 512 torch.Size([512])
resnet.layer4.0.downsample.0.weight 131072 torch.Size([512, 256, 1, 1])
resnet.layer4.0.downsample.0.lora_A 9216 torch.Size([36, 256])
resnet.layer4.0.downsample.0.lora_B 18432 torch.Size([512, 36])
resnet.layer4.0.downsample.1.weight 512 torch.Size([512])
resnet.layer4.0.downsample.1.bias 512 torch.Size([512])
resnet.layer4.1.conv1.weight 2359296 torch.Size([512, 512, 3, 3])
resnet.layer4.1.conv1.lora_A 27648 torch.Size([18, 1536])
resnet.layer4.1.conv1.lora_B 27648 torch.Size([1536, 18])
resnet.layer4.1.bn1.weight 512 torch.Size([512])
resnet.layer4.1.bn1.bias 512 torch.Size([512])
resnet.layer4.1.conv2.weight 2359296 torch.Size([512, 512, 3, 3])
resnet.layer4.1.conv2.lora_A 27648 torch.Size([18, 1536])
resnet.layer4.1.conv2.lora_B 27648 torch.Size([1536, 18])
resnet.layer4.1.bn2.weight 512 torch.Size([512])
resnet.layer4.1.bn2.bias 512 torch.Size([512])
instance_projector.0.weight 262144 torch.Size([512, 512])
instance_projector.0.bias 512 torch.Size([512])
instance_projector.2.weight 65536 torch.Size([128, 512])
instance_projector.2.bias 128 torch.Size([128])
cluster_projector2.0.weight 262144 torch.Size([512, 512])
cluster_projector2.0.bias 512 torch.Size([512])
cluster_projector2.2.weight 2048 torch.Size([4, 512])
cluster_projector2.2.bias 4 torch.Size([4])
sigma: 3.67919921875
Number of total parameters: 12487734
Number of trainable p arameters: 1320822
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.2203 ARI = 0.0380 F = 0.3022 ACC = 0.2461
Norm: tensor(5.2381, device='cuda:2')
Round: 0 User: 0 Train Loss: 18.008
Norm: tensor(5.0843, device='cuda:2')
Round: 0 User: 1 Train Loss: 17.056
Norm: tensor(5.2824, device='cuda:2')
Round: 0 User: 2 Train Loss: 18.464
Norm: tensor(5.0773, device='cuda:2')
Round: 0 User: 3 Train Loss: 17.196
Norm: tensor(5.0698, device='cuda:2')
Round: 0 User: 4 Train Loss: 16.868
Norm: tensor(5.4118, device='cuda:2')
Round: 0 User: 5 Train Loss: 18.557
Norm: tensor(5.4751, device='cuda:2')
Round: 0 User: 6 Train Loss: 18.699
Norm: tensor(5.4644, device='cuda:2')
Round: 0 User: 7 Train Loss: 19.872
Norm: tensor(5.2807, device='cuda:2')
Round: 0 User: 8 Train Loss: 18.001
Norm: tensor(5.3411, device='cuda:2')
Round: 0 User: 9 Train Loss: 17.767
Norm: tensor(5.3940, device='cuda:2')
Round: 0 User: 10 Train Loss: 19.200
Norm: tensor(5.1319, device='cuda:2')
Round: 0 User: 11 Train Loss: 17.405
Norm: tensor(4.9183, device='cuda:2')
Round: 0 User: 12 Train Loss: 16.169
Norm: tensor(5.1917, device='cuda:2')
Round: 0 User: 13 Train Loss: 17.457
Norm: tensor(5.7144, device='cuda:2')
Round: 0 User: 14 Train Loss: 18.980
Norm: tensor(5.3646, device='cuda:2')
Round: 0 User: 15 Train Loss: 19.178
Norm: tensor(5.7316, device='cuda:2')
Round: 0 User: 16 Train Loss: 19.001
Norm: tensor(5.4098, device='cuda:2')
Round: 0 User: 17 Train Loss: 18.097
Norm: tensor(5.6988, device='cuda:2')
Round: 0 User: 18 Train Loss: 19.245
Norm: tensor(5.4035, device='cuda:2')
Round: 0 User: 19 Train Loss: 18.883
Norm: tensor(5.4126, device='cuda:2')
Round: 0 User: 20 Train Loss: 18.803
Norm: tensor(5.4657, device='cuda:2')
Round: 0 User: 21 Train Loss: 17.909
Norm: tensor(5.1524, device='cuda:2')
Round: 0 User: 22 Train Loss: 17.155
Norm: tensor(5.4101, device='cuda:2')
Round: 0 User: 23 Train Loss: 18.282
Norm: tensor(5.3582, device='cuda:2')
Round: 0 User: 24 Train Loss: 19.223
Norm: tensor(5.2449, device='cuda:2')
Round: 0 User: 25 Train Loss: 18.455
Norm: tensor(5.3359, device='cuda:2')
Round: 0 User: 26 Train Loss: 18.073
Norm: tensor(5.3579, device='cuda:2')
Round: 0 User: 27 Train Loss: 18.710
Norm: tensor(5.2735, device='cuda:2')
Round: 0 User: 28 Train Loss: 17.621
Norm: tensor(5.2109, device='cuda:2')
Round: 0 User: 29 Train Loss: 17.477
Norm: tensor(5.2126, device='cuda:2')
Round: 0 User: 30 Train Loss: 17.371
Norm: tensor(5.2113, device='cuda:2')
Round: 0 User: 31 Train Loss: 17.260
Norm: tensor(5.1893, device='cuda:2')
Round: 0 User: 32 Train Loss: 17.609
Norm: tensor(5.4012, device='cuda:2')
Round: 0 User: 33 Train Loss: 18.695
Norm: tensor(5.3009, device='cuda:2')
Round: 0 User: 34 Train Loss: 18.225
Norm: tensor(5.2095, device='cuda:2')
Round: 0 User: 35 Train Loss: 18.306
Norm: tensor(5.3298, device='cuda:2')
Round: 0 User: 36 Train Loss: 18.020
Norm: tensor(5.2403, device='cuda:2')
Round: 0 User: 37 Train Loss: 17.900
Norm: tensor(5.2200, device='cuda:2')
Round: 0 User: 38 Train Loss: 17.989
Norm: tensor(5.2468, device='cuda:2')
Round: 0 User: 39 Train Loss: 18.044
clip_bound 1.6
count 40
updated norm: tensor(1.2432, device='cuda:2')
*********Round: 0 Train Loss: 18.131
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.3564 ARI = 0.2361 F = 0.3547 ACC = 0.4421
Norm: tensor(4.0758, device='cuda:2')
Round: 1 User: 0 Train Loss: 12.663
Norm: tensor(3.9496, device='cuda:2')
Round: 1 User: 1 Train Loss: 12.435
Norm: tensor(4.3103, device='cuda:2')
Round: 1 User: 2 Train Loss: 14.174
Norm: tensor(3.9266, device='cuda:2')
Round: 1 User: 3 Train Loss: 12.285
Norm: tensor(4.0391, device='cuda:2')
Round: 1 User: 4 Train Loss: 12.722
Norm: tensor(4.1905, device='cuda:2')
Round: 1 User: 5 Train Loss: 13.063
Norm: tensor(4.0653, device='cuda:2')
Round: 1 User: 6 Train Loss: 12.631
Norm: tensor(4.1089, device='cuda:2')
Round: 1 User: 7 Train Loss: 13.199
Norm: tensor(4.5024, device='cuda:2')
Round: 1 User: 8 Train Loss: 13.943
Norm: tensor(4.2271, device='cuda:2')
Round: 1 User: 9 Train Loss: 13.841
Norm: tensor(4.1811, device='cuda:2')
Round: 1 User: 10 Train Loss: 12.812
Norm: tensor(4.0409, device='cuda:2')
Round: 1 User: 11 Train Loss: 12.761
Norm: tensor(4.3632, device='cuda:2')
Round: 1 User: 12 Train Loss: 13.840
Norm: tensor(4.3325, device='cuda:2')
Round: 1 User: 13 Train Loss: 13.768
Norm: tensor(4.1395, device='cuda:2')
Round: 1 User: 14 Train Loss: 12.924
Norm: tensor(3.9213, device='cuda:2')
Round: 1 User: 15 Train Loss: 12.413
Norm: tensor(5.0426, device='cuda:2')
Round: 1 User: 16 Train Loss: 17.216
Norm: tensor(4.0648, device='cuda:2')
Round: 1 User: 17 Train Loss: 12.800
Norm: tensor(4.3341, device='cuda:2')
Round: 1 User: 18 Train Loss: 13.888
Norm: tensor(4.0279, device='cuda:2')
Round: 1 User: 19 Train Loss: 12.584
Norm: tensor(4.1423, device='cuda:2')
Round: 1 User: 20 Train Loss: 13.290
Norm: tensor(4.1263, device='cuda:2')
Round: 1 User: 21 Train Loss: 13.162
Norm: tensor(4.0887, device='cuda:2')
Round: 1 User: 22 Train Loss: 12.977
Norm: tensor(4.5742, device='cuda:2')
Round: 1 User: 23 Train Loss: 14.626
Norm: tensor(4.2052, device='cuda:2')
Round: 1 User: 24 Train Loss: 13.239
Norm: tensor(4.0677, device='cuda:2')
Round: 1 User: 25 Train Loss: 12.496
Norm: tensor(4.1507, device='cuda:2')
Round: 1 User: 26 Train Loss: 12.961
Norm: tensor(4.2309, device='cuda:2')
Round: 1 User: 27 Train Loss: 13.372
Norm: tensor(4.0857, device='cuda:2')
Round: 1 User: 28 Train Loss: 12.748
Norm: tensor(4.1807, device='cuda:2')
Round: 1 User: 29 Train Loss: 13.489
Norm: tensor(4.0555, device='cuda:2')
Round: 1 User: 30 Train Loss: 12.503
Norm: tensor(4.0939, device='cuda:2')
Round: 1 User: 31 Train Loss: 12.963
Norm: tensor(4.1053, device='cuda:2')
Round: 1 User: 32 Train Loss: 13.485
Norm: tensor(4.0029, device='cuda:2')
Round: 1 User: 33 Train Loss: 12.867
Norm: tensor(4.1812, device='cuda:2')
Round: 1 User: 34 Train Loss: 12.859
Norm: tensor(4.0460, device='cuda:2')
Round: 1 User: 35 Train Loss: 12.858
Norm: tensor(4.2127, device='cuda:2')
Round: 1 User: 36 Train Loss: 13.475
Norm: tensor(4.1466, device='cuda:2')
Round: 1 User: 37 Train Loss: 12.953
Norm: tensor(4.2623, device='cuda:2')
Round: 1 User: 38 Train Loss: 13.721
Norm: tensor(4.0904, device='cuda:2')
Round: 1 User: 39 Train Loss: 12.810
clip_bound 1.6
count 40
updated norm: tensor(1.0067, device='cuda:2')
*********Round: 1 Train Loss: 13.220
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.4057 ARI = 0.3096 F = 0.3943 ACC = 0.5236
Norm: tensor(3.2936, device='cuda:2')
Round: 2 User: 0 Train Loss: 10.115
Norm: tensor(3.0909, device='cuda:2')
Round: 2 User: 1 Train Loss: 9.674
Norm: tensor(3.6587, device='cuda:2')
Round: 2 User: 2 Train Loss: 11.387
Norm: tensor(3.2833, device='cuda:2')
Round: 2 User: 3 Train Loss: 10.047
Norm: tensor(3.7645, device='cuda:2')
Round: 2 User: 4 Train Loss: 11.952
Norm: tensor(3.3797, device='cuda:2')
Round: 2 User: 5 Train Loss: 10.451
Norm: tensor(3.4181, device='cuda:2')
Round: 2 User: 6 Train Loss: 10.436
Norm: tensor(3.4867, device='cuda:2')
Round: 2 User: 7 Train Loss: 10.309
Norm: tensor(3.3039, device='cuda:2')
Round: 2 User: 8 Train Loss: 10.171
Norm: tensor(3.4967, device='cuda:2')
Round: 2 User: 9 Train Loss: 10.958
Norm: tensor(3.0807, device='cuda:2')
Round: 2 User: 10 Train Loss: 9.558
Norm: tensor(3.1757, device='cuda:2')
Round: 2 User: 11 Train Loss: 9.957
Norm: tensor(3.5764, device='cuda:2')
Round: 2 User: 12 Train Loss: 11.251
Norm: tensor(3.2344, device='cuda:2')
Round: 2 User: 13 Train Loss: 10.181
Norm: tensor(3.1570, device='cuda:2')
Round: 2 User: 14 Train Loss: 9.771
Norm: tensor(2.9713, device='cuda:2')
Round: 2 User: 15 Train Loss: 9.235
Norm: tensor(4.1245, device='cuda:2')
Round: 2 User: 16 Train Loss: 13.720
Norm: tensor(3.2398, device='cuda:2')
Round: 2 User: 17 Train Loss: 9.964
Norm: tensor(3.7103, device='cuda:2')
Round: 2 User: 18 Train Loss: 11.561
Norm: tensor(3.0565, device='cuda:2')
Round: 2 User: 19 Train Loss: 9.494
Norm: tensor(3.2164, device='cuda:2')
Round: 2 User: 20 Train Loss: 9.815
Norm: tensor(3.4840, device='cuda:2')
Round: 2 User: 21 Train Loss: 10.681
Norm: tensor(3.7774, device='cuda:2')
Round: 2 User: 22 Train Loss: 11.314
Norm: tensor(3.8628, device='cuda:2')
Round: 2 User: 23 Train Loss: 12.090
Norm: tensor(3.3053, device='cuda:2')
Round: 2 User: 24 Train Loss: 10.197
Norm: tensor(3.0986, device='cuda:2')
Round: 2 User: 25 Train Loss: 9.530
Norm: tensor(3.6712, device='cuda:2')
Round: 2 User: 26 Train Loss: 11.786
Norm: tensor(3.2892, device='cuda:2')
Round: 2 User: 27 Train Loss: 10.295
Norm: tensor(2.9854, device='cuda:2')
Round: 2 User: 28 Train Loss: 9.366
Norm: tensor(3.2666, device='cuda:2')
Round: 2 User: 29 Train Loss: 10.162
Norm: tensor(3.3342, device='cuda:2')
Round: 2 User: 30 Train Loss: 10.543
Norm: tensor(3.3103, device='cuda:2')
Round: 2 User: 31 Train Loss: 10.589
Norm: tensor(3.5050, device='cuda:2')
Round: 2 User: 32 Train Loss: 10.703
Norm: tensor(3.2279, device='cuda:2')
Round: 2 User: 33 Train Loss: 9.985
Norm: tensor(3.2286, device='cuda:2')
Round: 2 User: 34 Train Loss: 9.920
Norm: tensor(2.9881, device='cuda:2')
Round: 2 User: 35 Train Loss: 9.343
Norm: tensor(3.5298, device='cuda:2')
Round: 2 User: 36 Train Loss: 10.880
Norm: tensor(3.4144, device='cuda:2')
Round: 2 User: 37 Train Loss: 10.419
Norm: tensor(3.1606, device='cuda:2')
Round: 2 User: 38 Train Loss: 9.728
Norm: tensor(3.1956, device='cuda:2')
Round: 2 User: 39 Train Loss: 10.060
clip_bound 1.6
count 40
updated norm: tensor(0.9251, device='cuda:2')
*********Round: 2 Train Loss: 10.440
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.4061 ARI = 0.3232 F = 0.3997 ACC = 0.5423
Norm: tensor(2.5892, device='cuda:2')
Round: 3 User: 0 Train Loss: 8.135
Norm: tensor(2.4252, device='cuda:2')
Round: 3 User: 1 Train Loss: 7.950
Norm: tensor(2.6463, device='cuda:2')
Round: 3 User: 2 Train Loss: 8.404
Norm: tensor(2.5482, device='cuda:2')
Round: 3 User: 3 Train Loss: 8.465
Norm: tensor(3.1235, device='cuda:2')
Round: 3 User: 4 Train Loss: 9.924
Norm: tensor(2.7950, device='cuda:2')
Round: 3 User: 5 Train Loss: 9.037
Norm: tensor(2.6253, device='cuda:2')
Round: 3 User: 6 Train Loss: 8.655
Norm: tensor(2.6101, device='cuda:2')
Round: 3 User: 7 Train Loss: 8.297
Norm: tensor(2.5231, device='cuda:2')
Round: 3 User: 8 Train Loss: 8.261
Norm: tensor(3.3170, device='cuda:2')
Round: 3 User: 9 Train Loss: 11.149
Norm: tensor(2.3344, device='cuda:2')
Round: 3 User: 10 Train Loss: 7.690
Norm: tensor(2.8254, device='cuda:2')
Round: 3 User: 11 Train Loss: 8.801
Norm: tensor(2.8060, device='cuda:2')
Round: 3 User: 12 Train Loss: 8.874
Norm: tensor(3.0433, device='cuda:2')
Round: 3 User: 13 Train Loss: 9.748
Norm: tensor(2.3922, device='cuda:2')
Round: 3 User: 14 Train Loss: 7.790
Norm: tensor(2.3789, device='cuda:2')
Round: 3 User: 15 Train Loss: 7.764
Norm: tensor(4.1609, device='cuda:2')
Round: 3 User: 16 Train Loss: 13.963
Norm: tensor(2.6358, device='cuda:2')
Round: 3 User: 17 Train Loss: 8.302
Norm: tensor(2.7693, device='cuda:2')
Round: 3 User: 18 Train Loss: 8.973
Norm: tensor(2.7077, device='cuda:2')
Round: 3 User: 19 Train Loss: 8.481
Norm: tensor(2.3234, device='cuda:2')
Round: 3 User: 20 Train Loss: 7.611
Norm: tensor(2.8024, device='cuda:2')
Round: 3 User: 21 Train Loss: 9.291
Norm: tensor(2.7936, device='cuda:2')
Round: 3 User: 22 Train Loss: 8.996
Norm: tensor(2.7031, device='cuda:2')
Round: 3 User: 23 Train Loss: 8.750
Norm: tensor(2.5598, device='cuda:2')
Round: 3 User: 24 Train Loss: 8.303
Norm: tensor(2.5935, device='cuda:2')
Round: 3 User: 25 Train Loss: 8.209
Norm: tensor(2.8263, device='cuda:2')
Round: 3 User: 26 Train Loss: 9.341
Norm: tensor(2.5782, device='cuda:2')
Round: 3 User: 27 Train Loss: 8.438
Norm: tensor(2.3405, device='cuda:2')
Round: 3 User: 28 Train Loss: 7.833
Norm: tensor(2.9965, device='cuda:2')
Round: 3 User: 29 Train Loss: 9.837
Norm: tensor(3.0392, device='cuda:2')
Round: 3 User: 30 Train Loss: 9.677
Norm: tensor(2.7932, device='cuda:2')
Round: 3 User: 31 Train Loss: 9.086
Norm: tensor(2.9093, device='cuda:2')
Round: 3 User: 32 Train Loss: 9.226
Norm: tensor(2.6383, device='cuda:2')
Round: 3 User: 33 Train Loss: 8.378
Norm: tensor(2.5810, device='cuda:2')
Round: 3 User: 34 Train Loss: 8.022
Norm: tensor(2.3323, device='cuda:2')
Round: 3 User: 35 Train Loss: 7.813
Norm: tensor(2.7649, device='cuda:2')
Round: 3 User: 36 Train Loss: 8.716
Norm: tensor(3.2949, device='cuda:2')
Round: 3 User: 37 Train Loss: 10.449
Norm: tensor(2.8750, device='cuda:2')
Round: 3 User: 38 Train Loss: 9.127
Norm: tensor(2.8793, device='cuda:2')
Round: 3 User: 39 Train Loss: 9.362
clip_bound 1.6
count 40
updated norm: tensor(0.8199, device='cuda:2')
*********Round: 3 Train Loss: 8.878
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.3981 ARI = 0.3214 F = 0.3995 ACC = 0.5389
Norm: tensor(2.3994, device='cuda:2')
Round: 4 User: 0 Train Loss: 7.977
Norm: tensor(2.0340, device='cuda:2')
Round: 4 User: 1 Train Loss: 7.174
Norm: tensor(2.3258, device='cuda:2')
Round: 4 User: 2 Train Loss: 7.806
Norm: tensor(2.3921, device='cuda:2')
Round: 4 User: 3 Train Loss: 8.037
Norm: tensor(2.8045, device='cuda:2')
Round: 4 User: 4 Train Loss: 9.505
Norm: tensor(2.2236, device='cuda:2')
Round: 4 User: 5 Train Loss: 7.597
Norm: tensor(2.3433, device='cuda:2')
Round: 4 User: 6 Train Loss: 8.004
Norm: tensor(2.6022, device='cuda:2')
Round: 4 User: 7 Train Loss: 8.265
Norm: tensor(2.3745, device='cuda:2')
Round: 4 User: 8 Train Loss: 7.856
Norm: tensor(2.5114, device='cuda:2')
Round: 4 User: 9 Train Loss: 8.676
Norm: tensor(1.9837, device='cuda:2')
Round: 4 User: 10 Train Loss: 7.121
Norm: tensor(2.6135, device='cuda:2')
Round: 4 User: 11 Train Loss: 8.109
Norm: tensor(2.4771, device='cuda:2')
Round: 4 User: 12 Train Loss: 8.134
Norm: tensor(2.2941, device='cuda:2')
Round: 4 User: 13 Train Loss: 8.099
Norm: tensor(2.1620, device='cuda:2')
Round: 4 User: 14 Train Loss: 7.430
Norm: tensor(2.1154, device='cuda:2')
Round: 4 User: 15 Train Loss: 7.245
Norm: tensor(3.0082, device='cuda:2')
Round: 4 User: 16 Train Loss: 10.117
Norm: tensor(2.3526, device='cuda:2')
Round: 4 User: 17 Train Loss: 7.707
Norm: tensor(2.6442, device='cuda:2')
Round: 4 User: 18 Train Loss: 8.677
Norm: tensor(2.3365, device='cuda:2')
Round: 4 User: 19 Train Loss: 7.878
Norm: tensor(2.1124, device='cuda:2')
Round: 4 User: 20 Train Loss: 7.243
Norm: tensor(2.4172, device='cuda:2')
Round: 4 User: 21 Train Loss: 8.070
Norm: tensor(2.3735, device='cuda:2')
Round: 4 User: 22 Train Loss: 7.848
Norm: tensor(2.4823, device='cuda:2')
Round: 4 User: 23 Train Loss: 8.168
Norm: tensor(2.3334, device='cuda:2')
Round: 4 User: 24 Train Loss: 7.870
Norm: tensor(2.4105, device='cuda:2')
Round: 4 User: 25 Train Loss: 8.022
Norm: tensor(2.2300, device='cuda:2')
Round: 4 User: 26 Train Loss: 7.749
Norm: tensor(2.2005, device='cuda:2')
Round: 4 User: 27 Train Loss: 7.566
Norm: tensor(2.1952, device='cuda:2')
Round: 4 User: 28 Train Loss: 7.419
Norm: tensor(2.5188, device='cuda:2')
Round: 4 User: 29 Train Loss: 8.347
Norm: tensor(2.5905, device='cuda:2')
Round: 4 User: 30 Train Loss: 8.556
Norm: tensor(2.1993, device='cuda:2')
Round: 4 User: 31 Train Loss: 7.684
Norm: tensor(2.3619, device='cuda:2')
Round: 4 User: 32 Train Loss: 7.904
Norm: tensor(2.2569, device='cuda:2')
Round: 4 User: 33 Train Loss: 7.593
Norm: tensor(2.1110, device='cuda:2')
Round: 4 User: 34 Train Loss: 7.192
Norm: tensor(1.9665, device='cuda:2')
Round: 4 User: 35 Train Loss: 7.041
Norm: tensor(2.4989, device='cuda:2')
Round: 4 User: 36 Train Loss: 8.138
Norm: tensor(2.7097, device='cuda:2')
Round: 4 User: 37 Train Loss: 8.654
Norm: tensor(2.3434, device='cuda:2')
Round: 4 User: 38 Train Loss: 7.877
Norm: tensor(2.4110, device='cuda:2')
Round: 4 User: 39 Train Loss: 8.223
clip_bound 1.6
count 40
updated norm: tensor(0.7179, device='cuda:2')
*********Round: 4 Train Loss: 7.964
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.3932 ARI = 0.3145 F = 0.3921 ACC = 0.5221
Norm: tensor(2.0751, device='cuda:2')
Round: 5 User: 0 Train Loss: 7.353
Norm: tensor(1.7752, device='cuda:2')
Round: 5 User: 1 Train Loss: 6.752
Norm: tensor(2.4529, device='cuda:2')
Round: 5 User: 2 Train Loss: 8.273
Norm: tensor(2.5276, device='cuda:2')
Round: 5 User: 3 Train Loss: 8.441
Norm: tensor(2.0418, device='cuda:2')
Round: 5 User: 4 Train Loss: 7.518
Norm: tensor(2.1802, device='cuda:2')
Round: 5 User: 5 Train Loss: 7.759
Norm: tensor(2.0419, device='cuda:2')
Round: 5 User: 6 Train Loss: 7.240
Norm: tensor(2.6760, device='cuda:2')
Round: 5 User: 7 Train Loss: 8.373
Norm: tensor(2.1711, device='cuda:2')
Round: 5 User: 8 Train Loss: 7.414
Norm: tensor(2.2251, device='cuda:2')
Round: 5 User: 9 Train Loss: 8.112
Norm: tensor(1.8494, device='cuda:2')
Round: 5 User: 10 Train Loss: 6.940
Norm: tensor(2.1250, device='cuda:2')
Round: 5 User: 11 Train Loss: 7.343
Norm: tensor(2.2310, device='cuda:2')
Round: 5 User: 12 Train Loss: 7.740
Norm: tensor(2.0323, device='cuda:2')
Round: 5 User: 13 Train Loss: 7.572
Norm: tensor(1.9497, device='cuda:2')
Round: 5 User: 14 Train Loss: 7.084
Norm: tensor(1.9135, device='cuda:2')
Round: 5 User: 15 Train Loss: 6.949
Norm: tensor(2.7316, device='cuda:2')
Round: 5 User: 16 Train Loss: 9.331
Norm: tensor(2.1588, device='cuda:2')
Round: 5 User: 17 Train Loss: 7.461
Norm: tensor(2.0404, device='cuda:2')
Round: 5 User: 18 Train Loss: 7.406
Norm: tensor(2.0017, device='cuda:2')
Round: 5 User: 19 Train Loss: 7.180
Norm: tensor(1.9596, device='cuda:2')
Round: 5 User: 20 Train Loss: 7.055
Norm: tensor(1.8572, device='cuda:2')
Round: 5 User: 21 Train Loss: 7.015
Norm: tensor(2.1019, device='cuda:2')
Round: 5 User: 22 Train Loss: 7.374
Norm: tensor(2.4246, device='cuda:2')
Round: 5 User: 23 Train Loss: 8.084
Norm: tensor(2.2607, device='cuda:2')
Round: 5 User: 24 Train Loss: 7.799
Norm: tensor(2.2476, device='cuda:2')
Round: 5 User: 25 Train Loss: 7.771
Norm: tensor(1.9782, device='cuda:2')
Round: 5 User: 26 Train Loss: 7.195
Norm: tensor(1.9934, device='cuda:2')
Round: 5 User: 27 Train Loss: 7.215
Norm: tensor(1.9426, device='cuda:2')
Round: 5 User: 28 Train Loss: 7.004
Norm: tensor(2.2939, device='cuda:2')
Round: 5 User: 29 Train Loss: 7.916
Norm: tensor(2.2368, device='cuda:2')
Round: 5 User: 30 Train Loss: 7.877
Norm: tensor(2.1939, device='cuda:2')
Round: 5 User: 31 Train Loss: 7.792
Norm: tensor(2.4529, device='cuda:2')
Round: 5 User: 32 Train Loss: 8.323
Norm: tensor(2.0598, device='cuda:2')
Round: 5 User: 33 Train Loss: 7.189
Norm: tensor(1.9618, device='cuda:2')
Round: 5 User: 34 Train Loss: 6.981
Norm: tensor(1.8027, device='cuda:2')
Round: 5 User: 35 Train Loss: 6.791
Norm: tensor(2.2583, device='cuda:2')
Round: 5 User: 36 Train Loss: 7.749
Norm: tensor(2.0613, device='cuda:2')
Round: 5 User: 37 Train Loss: 7.382
Norm: tensor(2.3827, device='cuda:2')
Round: 5 User: 38 Train Loss: 8.125
Norm: tensor(2.1257, device='cuda:2')
Round: 5 User: 39 Train Loss: 7.552
clip_bound 1.4
count 40
updated norm: tensor(0.6653, device='cuda:2')
*********Round: 5 Train Loss: 7.561
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.3981 ARI = 0.3161 F = 0.3937 ACC = 0.5242
Norm: tensor(2.0694, device='cuda:2')
Round: 6 User: 0 Train Loss: 7.205
Norm: tensor(1.6981, device='cuda:2')
Round: 6 User: 1 Train Loss: 6.647
Norm: tensor(2.3768, device='cuda:2')
Round: 6 User: 2 Train Loss: 8.218
Norm: tensor(2.0348, device='cuda:2')
Round: 6 User: 3 Train Loss: 7.329
Norm: tensor(2.1841, device='cuda:2')
Round: 6 User: 4 Train Loss: 7.825
Norm: tensor(2.0484, device='cuda:2')
Round: 6 User: 5 Train Loss: 7.373
Norm: tensor(1.8855, device='cuda:2')
Round: 6 User: 6 Train Loss: 7.103
Norm: tensor(2.0833, device='cuda:2')
Round: 6 User: 7 Train Loss: 7.352
Norm: tensor(2.1200, device='cuda:2')
Round: 6 User: 8 Train Loss: 7.394
Norm: tensor(2.0313, device='cuda:2')
Round: 6 User: 9 Train Loss: 7.435
Norm: tensor(1.7524, device='cuda:2')
Round: 6 User: 10 Train Loss: 6.874
Norm: tensor(1.8174, device='cuda:2')
Round: 6 User: 11 Train Loss: 6.868
Norm: tensor(2.0174, device='cuda:2')
Round: 6 User: 12 Train Loss: 7.331
Norm: tensor(2.0031, device='cuda:2')
Round: 6 User: 13 Train Loss: 7.420
Norm: tensor(1.8211, device='cuda:2')
Round: 6 User: 14 Train Loss: 6.842
Norm: tensor(1.8296, device='cuda:2')
Round: 6 User: 15 Train Loss: 6.898
Norm: tensor(2.6895, device='cuda:2')
Round: 6 User: 16 Train Loss: 9.526
Norm: tensor(1.9321, device='cuda:2')
Round: 6 User: 17 Train Loss: 7.169
Norm: tensor(2.0190, device='cuda:2')
Round: 6 User: 18 Train Loss: 7.278
Norm: tensor(2.1296, device='cuda:2')
Round: 6 User: 19 Train Loss: 7.412
Norm: tensor(1.7771, device='cuda:2')
Round: 6 User: 20 Train Loss: 6.762
Norm: tensor(1.9406, device='cuda:2')
Round: 6 User: 21 Train Loss: 7.135
Norm: tensor(1.8448, device='cuda:2')
Round: 6 User: 22 Train Loss: 6.990
Norm: tensor(2.0894, device='cuda:2')
Round: 6 User: 23 Train Loss: 7.588
Norm: tensor(1.9941, device='cuda:2')
Round: 6 User: 24 Train Loss: 7.253
Norm: tensor(2.5916, device='cuda:2')
Round: 6 User: 25 Train Loss: 8.658
Norm: tensor(2.1396, device='cuda:2')
Round: 6 User: 26 Train Loss: 7.674
Norm: tensor(1.9186, device='cuda:2')
Round: 6 User: 27 Train Loss: 7.111
Norm: tensor(1.7591, device='cuda:2')
Round: 6 User: 28 Train Loss: 6.810
Norm: tensor(1.9948, device='cuda:2')
Round: 6 User: 29 Train Loss: 7.279
Norm: tensor(2.1194, device='cuda:2')
Round: 6 User: 30 Train Loss: 7.529
Norm: tensor(1.9553, device='cuda:2')
Round: 6 User: 31 Train Loss: 7.128
Norm: tensor(2.4577, device='cuda:2')
Round: 6 User: 32 Train Loss: 8.381
Norm: tensor(1.8622, device='cuda:2')
Round: 6 User: 33 Train Loss: 6.954
Norm: tensor(1.7194, device='cuda:2')
Round: 6 User: 34 Train Loss: 6.728
Norm: tensor(1.8870, device='cuda:2')
Round: 6 User: 35 Train Loss: 6.896
Norm: tensor(2.0417, device='cuda:2')
Round: 6 User: 36 Train Loss: 7.450
Norm: tensor(2.1253, device='cuda:2')
Round: 6 User: 37 Train Loss: 7.417
Norm: tensor(2.3509, device='cuda:2')
Round: 6 User: 38 Train Loss: 8.093
Norm: tensor(1.9011, device='cuda:2')
Round: 6 User: 39 Train Loss: 7.046
clip_bound 1.4
count 40
updated norm: tensor(0.5329, device='cuda:2')
*********Round: 6 Train Loss: 7.360
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.3957 ARI = 0.3156 F = 0.3939 ACC = 0.5255
Norm: tensor(1.9769, device='cuda:2')
Round: 7 User: 0 Train Loss: 7.160
Norm: tensor(1.6951, device='cuda:2')
Round: 7 User: 1 Train Loss: 6.791
Norm: tensor(2.2362, device='cuda:2')
Round: 7 User: 2 Train Loss: 7.942
Norm: tensor(1.9467, device='cuda:2')
Round: 7 User: 3 Train Loss: 7.158
Norm: tensor(2.1187, device='cuda:2')
Round: 7 User: 4 Train Loss: 7.794
Norm: tensor(2.0716, device='cuda:2')
Round: 7 User: 5 Train Loss: 7.429
Norm: tensor(1.9875, device='cuda:2')
Round: 7 User: 6 Train Loss: 7.250
Norm: tensor(2.0938, device='cuda:2')
Round: 7 User: 7 Train Loss: 7.492
Norm: tensor(1.9658, device='cuda:2')
Round: 7 User: 8 Train Loss: 7.201
Norm: tensor(2.0798, device='cuda:2')
Round: 7 User: 9 Train Loss: 7.548
Norm: tensor(1.6524, device='cuda:2')
Round: 7 User: 10 Train Loss: 6.642
Norm: tensor(1.9102, device='cuda:2')
Round: 7 User: 11 Train Loss: 7.159
Norm: tensor(1.9845, device='cuda:2')
Round: 7 User: 12 Train Loss: 7.400
Norm: tensor(1.8877, device='cuda:2')
Round: 7 User: 13 Train Loss: 7.208
Norm: tensor(1.9706, device='cuda:2')
Round: 7 User: 14 Train Loss: 7.125
Norm: tensor(1.7069, device='cuda:2')
Round: 7 User: 15 Train Loss: 6.639
Norm: tensor(2.5773, device='cuda:2')
Round: 7 User: 16 Train Loss: 9.069
Norm: tensor(1.9262, device='cuda:2')
Round: 7 User: 17 Train Loss: 7.094
Norm: tensor(2.0620, device='cuda:2')
Round: 7 User: 18 Train Loss: 7.531
Norm: tensor(1.8371, device='cuda:2')
Round: 7 User: 19 Train Loss: 6.970
Norm: tensor(1.6441, device='cuda:2')
Round: 7 User: 20 Train Loss: 6.603
Norm: tensor(1.8545, device='cuda:2')
Round: 7 User: 21 Train Loss: 6.969
Norm: tensor(1.8000, device='cuda:2')
Round: 7 User: 22 Train Loss: 6.875
Norm: tensor(2.0528, device='cuda:2')
Round: 7 User: 23 Train Loss: 7.521
Norm: tensor(2.1201, device='cuda:2')
Round: 7 User: 24 Train Loss: 7.511
Norm: tensor(2.3038, device='cuda:2')
Round: 7 User: 25 Train Loss: 7.781
Norm: tensor(1.9620, device='cuda:2')
Round: 7 User: 26 Train Loss: 7.307
Norm: tensor(1.7177, device='cuda:2')
Round: 7 User: 27 Train Loss: 6.737
Norm: tensor(1.7218, device='cuda:2')
Round: 7 User: 28 Train Loss: 6.747
Norm: tensor(2.1609, device='cuda:2')
Round: 7 User: 29 Train Loss: 7.664
Norm: tensor(1.7705, device='cuda:2')
Round: 7 User: 30 Train Loss: 6.881
Norm: tensor(1.9112, device='cuda:2')
Round: 7 User: 31 Train Loss: 7.169
Norm: tensor(2.1760, device='cuda:2')
Round: 7 User: 32 Train Loss: 7.695
Norm: tensor(1.7867, device='cuda:2')
Round: 7 User: 33 Train Loss: 6.891
Norm: tensor(1.6848, device='cuda:2')
Round: 7 User: 34 Train Loss: 6.667
Norm: tensor(1.6766, device='cuda:2')
Round: 7 User: 35 Train Loss: 6.619
Norm: tensor(1.9819, device='cuda:2')
Round: 7 User: 36 Train Loss: 7.354
Norm: tensor(1.9941, device='cuda:2')
Round: 7 User: 37 Train Loss: 7.068
Norm: tensor(2.1818, device='cuda:2')
Round: 7 User: 38 Train Loss: 7.769
Norm: tensor(1.9889, device='cuda:2')
Round: 7 User: 39 Train Loss: 7.299
clip_bound 1.4
count 40
updated norm: tensor(0.5012, device='cuda:2')
*********Round: 7 Train Loss: 7.243
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.4071 ARI = 0.3210 F = 0.3993 ACC = 0.5298
Norm: tensor(1.8472, device='cuda:2')
Round: 8 User: 0 Train Loss: 6.890
Norm: tensor(1.7488, device='cuda:2')
Round: 8 User: 1 Train Loss: 6.886
Norm: tensor(1.9850, device='cuda:2')
Round: 8 User: 2 Train Loss: 7.208
Norm: tensor(1.8210, device='cuda:2')
Round: 8 User: 3 Train Loss: 6.996
Norm: tensor(1.7483, device='cuda:2')
Round: 8 User: 4 Train Loss: 6.991
Norm: tensor(1.8137, device='cuda:2')
Round: 8 User: 5 Train Loss: 7.047
Norm: tensor(1.7425, device='cuda:2')
Round: 8 User: 6 Train Loss: 6.878
Norm: tensor(1.9462, device='cuda:2')
Round: 8 User: 7 Train Loss: 7.270
Norm: tensor(1.8839, device='cuda:2')
Round: 8 User: 8 Train Loss: 7.031
Norm: tensor(1.9507, device='cuda:2')
Round: 8 User: 9 Train Loss: 7.427
Norm: tensor(1.5738, device='cuda:2')
Round: 8 User: 10 Train Loss: 6.496
Norm: tensor(1.7595, device='cuda:2')
Round: 8 User: 11 Train Loss: 6.827
Norm: tensor(2.1603, device='cuda:2')
Round: 8 User: 12 Train Loss: 7.868
Norm: tensor(2.2431, device='cuda:2')
Round: 8 User: 13 Train Loss: 8.214
Norm: tensor(1.8028, device='cuda:2')
Round: 8 User: 14 Train Loss: 6.827
Norm: tensor(1.5936, device='cuda:2')
Round: 8 User: 15 Train Loss: 6.514
Norm: tensor(2.3273, device='cuda:2')
Round: 8 User: 16 Train Loss: 8.583
Norm: tensor(1.9717, device='cuda:2')
Round: 8 User: 17 Train Loss: 7.192
Norm: tensor(1.7766, device='cuda:2')
Round: 8 User: 18 Train Loss: 6.914
Norm: tensor(1.9144, device='cuda:2')
Round: 8 User: 19 Train Loss: 7.001
Norm: tensor(1.5591, device='cuda:2')
Round: 8 User: 20 Train Loss: 6.483
Norm: tensor(1.9081, device='cuda:2')
Round: 8 User: 21 Train Loss: 7.360
Norm: tensor(1.7818, device='cuda:2')
Round: 8 User: 22 Train Loss: 6.955
Norm: tensor(1.7553, device='cuda:2')
Round: 8 User: 23 Train Loss: 7.049
Norm: tensor(1.9102, device='cuda:2')
Round: 8 User: 24 Train Loss: 7.106
Norm: tensor(2.3045, device='cuda:2')
Round: 8 User: 25 Train Loss: 7.810
Norm: tensor(2.1810, device='cuda:2')
Round: 8 User: 26 Train Loss: 7.772
Norm: tensor(1.8014, device='cuda:2')
Round: 8 User: 27 Train Loss: 6.928
Norm: tensor(1.6764, device='cuda:2')
Round: 8 User: 28 Train Loss: 6.706
Norm: tensor(1.8470, device='cuda:2')
Round: 8 User: 29 Train Loss: 7.111
Norm: tensor(1.7913, device='cuda:2')
Round: 8 User: 30 Train Loss: 6.953
Norm: tensor(1.9218, device='cuda:2')
Round: 8 User: 31 Train Loss: 7.114
Norm: tensor(2.1944, device='cuda:2')
Round: 8 User: 32 Train Loss: 7.848
Norm: tensor(1.6687, device='cuda:2')
Round: 8 User: 33 Train Loss: 6.705
Norm: tensor(1.5728, device='cuda:2')
Round: 8 User: 34 Train Loss: 6.540
Norm: tensor(1.6524, device='cuda:2')
Round: 8 User: 35 Train Loss: 6.657
Norm: tensor(1.8314, device='cuda:2')
Round: 8 User: 36 Train Loss: 7.006
Norm: tensor(1.9095, device='cuda:2')
Round: 8 User: 37 Train Loss: 7.111
Norm: tensor(2.1873, device='cuda:2')
Round: 8 User: 38 Train Loss: 7.679
Norm: tensor(1.6061, device='cuda:2')
Round: 8 User: 39 Train Loss: 6.626
clip_bound 1.4
count 40
updated norm: tensor(0.4900, device='cuda:2')
*********Round: 8 Train Loss: 7.114
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.3987 ARI = 0.3167 F = 0.3952 ACC = 0.5274
Norm: tensor(1.8151, device='cuda:2')
Round: 9 User: 0 Train Loss: 6.944
Norm: tensor(1.5454, device='cuda:2')
Round: 9 User: 1 Train Loss: 6.454
Norm: tensor(1.9762, device='cuda:2')
Round: 9 User: 2 Train Loss: 7.337
Norm: tensor(1.8488, device='cuda:2')
Round: 9 User: 3 Train Loss: 7.126
Norm: tensor(1.8308, device='cuda:2')
Round: 9 User: 4 Train Loss: 7.144
Norm: tensor(1.8027, device='cuda:2')
Round: 9 User: 5 Train Loss: 7.002
Norm: tensor(1.7792, device='cuda:2')
Round: 9 User: 6 Train Loss: 6.873
Norm: tensor(2.0474, device='cuda:2')
Round: 9 User: 7 Train Loss: 7.423
Norm: tensor(1.7328, device='cuda:2')
Round: 9 User: 8 Train Loss: 6.810
Norm: tensor(1.7856, device='cuda:2')
Round: 9 User: 9 Train Loss: 7.004
Norm: tensor(1.6464, device='cuda:2')
Round: 9 User: 10 Train Loss: 6.623
Norm: tensor(1.7913, device='cuda:2')
Round: 9 User: 11 Train Loss: 6.820
Norm: tensor(2.1474, device='cuda:2')
Round: 9 User: 12 Train Loss: 7.782
Norm: tensor(1.7791, device='cuda:2')
Round: 9 User: 13 Train Loss: 7.095
Norm: tensor(1.7087, device='cuda:2')
Round: 9 User: 14 Train Loss: 6.729
Norm: tensor(1.5567, device='cuda:2')
Round: 9 User: 15 Train Loss: 6.417
Norm: tensor(2.4379, device='cuda:2')
Round: 9 User: 16 Train Loss: 8.952
Norm: tensor(1.7222, device='cuda:2')
Round: 9 User: 17 Train Loss: 6.741
Norm: tensor(1.9109, device='cuda:2')
Round: 9 User: 18 Train Loss: 7.315
Norm: tensor(1.8358, device='cuda:2')
Round: 9 User: 19 Train Loss: 6.944
Norm: tensor(1.5504, device='cuda:2')
Round: 9 User: 20 Train Loss: 6.398
Norm: tensor(1.7311, device='cuda:2')
Round: 9 User: 21 Train Loss: 6.886
Norm: tensor(1.5881, device='cuda:2')
Round: 9 User: 22 Train Loss: 6.653
Norm: tensor(1.8370, device='cuda:2')
Round: 9 User: 23 Train Loss: 7.186
Norm: tensor(1.9430, device='cuda:2')
Round: 9 User: 24 Train Loss: 7.315
Norm: tensor(2.0384, device='cuda:2')
Round: 9 User: 25 Train Loss: 7.451
Norm: tensor(1.8283, device='cuda:2')
Round: 9 User: 26 Train Loss: 7.101
Norm: tensor(1.7304, device='cuda:2')
Round: 9 User: 27 Train Loss: 6.824
Norm: tensor(1.6364, device='cuda:2')
Round: 9 User: 28 Train Loss: 6.645
Norm: tensor(1.9968, device='cuda:2')
Round: 9 User: 29 Train Loss: 7.476
Norm: tensor(1.7287, device='cuda:2')
Round: 9 User: 30 Train Loss: 6.872
Norm: tensor(1.8585, device='cuda:2')
Round: 9 User: 31 Train Loss: 7.066
Norm: tensor(2.4036, device='cuda:2')
Round: 9 User: 32 Train Loss: 8.234
Norm: tensor(1.7261, device='cuda:2')
Round: 9 User: 33 Train Loss: 6.766
Norm: tensor(1.5760, device='cuda:2')
Round: 9 User: 34 Train Loss: 6.586
Norm: tensor(1.5882, device='cuda:2')
Round: 9 User: 35 Train Loss: 6.532
Norm: tensor(2.0428, device='cuda:2')
Round: 9 User: 36 Train Loss: 7.641
Norm: tensor(1.8543, device='cuda:2')
Round: 9 User: 37 Train Loss: 7.000
Norm: tensor(2.2340, device='cuda:2')
Round: 9 User: 38 Train Loss: 7.859
Norm: tensor(1.8114, device='cuda:2')
Round: 9 User: 39 Train Loss: 6.911
clip_bound 1.4
count 40
updated norm: tensor(0.4697, device='cuda:2')
*********Round: 9 Train Loss: 7.073
### Creating features from model ###
########### begin kmeans ##########
Global NMI = 0.3996 ARI = 0.3147 F = 0.3935 ACC = 0.5252
Norm: tensor(1.7191, device='cuda:2')
Round: 10 User: 0 Train Loss: 6.702
Norm: tensor(1.4289, device='cuda:2')
Round: 10 User: 1 Train Loss: 6.331
Norm: tensor(1.9851, device='cuda:2')
Round: 10 User: 2 Train Loss: 7.404
Norm: tensor(1.8464, device='cuda:2')
Round: 10 User: 3 Train Loss: 7.074
Norm: tensor(1.8645, device='cuda:2')
Round: 10 User: 4 Train Loss: 7.172
Norm: tensor(1.8176, device='cuda:2')
Round: 10 User: 5 Train Loss: 7.131
Norm: tensor(1.6839, device='cuda:2')
Round: 10 User: 6 Train Loss: 6.832
Norm: tensor(1.8935, device='cuda:2')
Round: 10 User: 7 Train Loss: 7.145