Personalized federated learning simulation platform with Non-IID dataset

The origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issue existing in the federated learning setting, a myriad of approaches has been proposed to crack this hard nut. In contrast, the personalized federated learning may take the advantage of the Non-IID data to learn the personalized model for each user. Thanks to @Stonesjtu, this platform can also record the GPU memory usage for the model. By using the package opacus v0.15, I introduce differential privacy into this platform (please refer to ./system/flcore/clients/clientavg.py for details).

Algorithms (updating)

FedAvg — Communication-Efficient Learning of Deep Networks from Decentralized Data AISTATS 2017
FedPer — Federated Learning with Personalization Layers
Per-FadAvg — Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach NeurIPS 2020
pFedMe — Personalized Federated Learning with Moreau Envelopes NeurIPS 2020
FedProx — Federated Optimization for Heterogeneous Networks ICLR 2020
APFL — Adaptive Personalized Federated Learning
FedFomo — Personalized Federated Learning with First Order Model Optimization ICLR 2021
FedMTL — Federated multi-task learning NeurIPS 2017
FedAMP — Personalized Cross-Silo Federated Learning on Non-IID Data AAAI 2021
Ditto — Ditto: Fair and robust federated learning through personalization ICML 2021
FedRep — Exploiting Shared Representations for Personalized Federated Learning ICML 2021
FedPHP — FedPHP: Federated Personalization with Inherited Private Models ECML PKDD 2021
FedBN — FedBN: Federated Learning on Non-IID Features via Local Batch Normalization ICLR 2021
FedRoD — On Bridging Generic and Personalized Federated Learning for Image Classification ICLR 2022

Datasets (updating)

Except for the Synthetic dataset (without update anymore), I currently using six famous datasets: MNIST, Fashion-MNIST, Cifar10, Cifar100, AG_News and Sogou_News, they can be easy split into IID and Non-IID version. Since some codes for generating datasets such as splitting are the same for all datasets, I move these codes into ./utils/dataset_utils.py. Now it is easy to add other datasets to this FL platform. If you need another data set, just write another code to download it and then using the utils.

In Non-IID setting, three situations exist. The first one is the pathological Non-IID setting, the second one is practical Non-IID setting and the third one is feature skew Non-IID. In the pathological Non-IID setting, for example, the data on each client only contains the specific number of labels (maybe only two labels), though the data on all clients contains 10 labels such as MNIST dataset. In the practical Non-IID setting, Dirichlet distribution is utilized (please refer to this paper for details). In the feature skew Non-IID, specific Gaussian noise is added to each client according to their IDs. We can input balance for the iid setting, where the data are uniformly distributed.

MNIST

cd ./dataset
python generate_mnist.py iid - - # for iid and unbalanced setting
# python generate_mnist.py noniid - - # for pathological noniid setting
# python generate_mnist.py noniid - dir # for practical noniid setting
# python generate_mnist.py noniid - noise # for feature skew noniid setting

Cifar10

cd ./dataset
python generate_cifar10.py iid - - # for iid and unbalanced setting
# python generate_cifar10.py noniid - - # for pathological noniid setting
# python generate_cifar10.py noniid - dir # for practical noniid setting
# python generate_cifar10.py noniid - noise # for feature skew noniid setting

Cifar100

cd ./dataset
python generate_cifar100py iid - - # for iid and unbalanced setting
# python generate_cifar100.py noniid - - # for pathological noniid setting
# python generate_cifar100.py noniid - dir # for practical noniid setting
# python generate_cifar100.py noniid - noise # for feature skew noniid setting

Fashion-MNIST

cd ./dataset
python generate_fmnist.py iid - - # for iid and unbalanced setting
# python generate_fmnist.py noniid - - # for pathological noniid setting
# python generate_fmnist.py noniid - dir # for practical noniid setting
# python generate_fmnist.py noniid - noise # for feature skew noniid setting

AG_News

cd ./dataset
python generate_agnews.py iid - - # for iid and unbalanced setting
# python generate_agnews.py noniid - - # for pathological noniid setting
# python generate_agnews.py noniid - dir # for practical noniid setting
# python generate_agnews.py noniid - noise # for feature skew noniid setting

Sogou_News (remains to be tested)

# If ConnectionError raises, please use the given downloaded file in './dataset'. 
cd ./dataset
python generate_sogounews.py iid - - # for iid and unbalanced setting
# python generate_sogounews.py noniid - - # for pathological noniid setting
# python generate_sogounews.py noniid - dir # for practical noniid setting
# python generate_sogounews.py noniid - noise # for feature skew noniid setting

Tiny-ImageNet

# Please download the original data from http://cs231n.stanford.edu/tiny-imagenet-200.zip. 
cd ./dataset
python generate_tiny_imagenet.py iid - - - # for iid and unbalanced setting
# python generate_tiny_imagenet.py iid - - balance # for iid and balanced setting
# python generate_tiny_imagenet.py noniid - - - # for pathological noniid setting
# python generate_tiny_imagenet.py noniid - dir - # for practical noniid setting
# python generate_tiny_imagenet.py noniid - noise - # for feature skew noniid setting

Dataset generating examples

The output of generate_mnist.py iid - -

Original number of samples of each label: [6903, 7877, 6990, 7141, 6824, 6313, 6876, 7293, 6825, 6958]

Client 0     Size of data: 1064  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 0     Samples of labels:  [(0, 101), (1, 128), (2, 136), (3, 123), (4, 79), (5, 85), (6, 107), (7, 127), (8, 74), (9, 104)]
--------------------------------------------------
Client 1     Size of data: 1023  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 1     Samples of labels:  [(0, 76), (1, 132), (2, 107), (3, 79), (4, 94), (5, 110), (6, 90), (7, 110), (8, 92), (9, 133)]
--------------------------------------------------
Client 2     Size of data: 923   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 2     Samples of labels:  [(0, 136), (1, 89), (2, 84), (3, 88), (4, 78), (5, 124), (6, 120), (7, 66), (8, 69), (9, 69)]
--------------------------------------------------

Client 3     Size of data: 906   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 3     Samples of labels:  [(0, 73), (1, 151), (2, 94), (3, 73), (4, 83), (5, 67), (6, 133), (7, 92), (8, 69), (9, 71)]
--------------------------------------------------
Client 4     Size of data: 1045  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 4     Samples of labels:  [(0, 69), (1, 71), (2, 100), (3, 130), (4, 90), (5, 120), (6, 116), (7, 142), (8, 106), (9, 101)]
--------------------------------------------------
Client 5     Size of data: 1026  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 5     Samples of labels:  [(0, 128), (1, 90), (2, 71), (3, 135), (4, 71), (5, 88), (6, 91), (7, 139), (8, 116), (9, 97)]
--------------------------------------------------
Client 6     Size of data: 1033  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 6     Samples of labels:  [(0, 80), (1, 89), (2, 109), (3, 117), (4, 117), (5, 80), (6, 107), (7, 122), (8, 121), (9, 91)]
--------------------------------------------------
Client 7     Size of data: 1043  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 7     Samples of labels:  [(0, 65), (1, 86), (2, 132), (3, 133), (4, 111), (5, 110), (6, 65), (7, 106), (8, 120), (9, 115)]
--------------------------------------------------
Client 8     Size of data: 1019  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 8     Samples of labels:  [(0, 135), (1, 73), (2, 121), (3, 100), (4, 124), (5, 118), (6, 90), (7, 90), (8, 74), (9, 94)]
--------------------------------------------------
Client 9     Size of data: 938   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 9     Samples of labels:  [(0, 70), (1, 131), (2, 77), (3, 85), (4, 98), (5, 79), (6, 94), (7, 85), (8, 112), (9, 107)]
--------------------------------------------------
Client 10    Size of data: 964   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 10    Samples of labels:  [(0, 89), (1, 87), (2, 74), (3, 104), (4, 96), (5, 71), (6, 128), (7, 122), (8, 83), (9, 110)]
--------------------------------------------------
Client 11    Size of data: 955   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 11    Samples of labels:  [(0, 114), (1, 91), (2, 87), (3, 141), (4, 83), (5, 124), (6, 86), (7, 80), (8, 76), (9, 73)]
--------------------------------------------------
Client 12    Size of data: 1015  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 12    Samples of labels:  [(0, 84), (1, 101), (2, 71), (3, 113), (4, 131), (5, 78), (6, 116), (7, 101), (8, 89), (9, 131)]
--------------------------------------------------
Client 13    Size of data: 856   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 13    Samples of labels:  [(0, 82), (1, 121), (2, 88), (3, 111), (4, 88), (5, 77), (6, 67), (7, 75), (8, 80), (9, 67)]
--------------------------------------------------
Client 14    Size of data: 1101  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 14    Samples of labels:  [(0, 75), (1, 147), (2, 138), (3, 141), (4, 102), (5, 79), (6, 134), (7, 86), (8, 68), (9, 131)]
--------------------------------------------------
Client 15    Size of data: 937   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 15    Samples of labels:  [(0, 92), (1, 102), (2, 84), (3, 104), (4, 111), (5, 89), (6, 76), (7, 70), (8, 91), (9, 118)]
--------------------------------------------------
Client 16    Size of data: 978   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 16    Samples of labels:  [(0, 93), (1, 72), (2, 96), (3, 109), (4, 69), (5, 117), (6, 103), (7, 78), (8, 114), (9, 127)]
--------------------------------------------------
Client 17    Size of data: 1016  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 17    Samples of labels:  [(0, 78), (1, 96), (2, 76), (3, 80), (4, 127), (5, 84), (6, 112), (7, 139), (8, 132), (9, 92)]
--------------------------------------------------
Client 18    Size of data: 1042  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 18    Samples of labels:  [(0, 114), (1, 98), (2, 129), (3, 92), (4, 96), (5, 121), (6, 125), (7, 99), (8, 67), (9, 101)]
--------------------------------------------------
Client 19    Size of data: 1178  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 19    Samples of labels:  [(0, 132), (1, 74), (2, 124), (3, 109), (4, 106), (5, 122), (6, 134), (7, 127), (8, 122), (9, 128)]
--------------------------------------------------
Client 20    Size of data: 948   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 20    Samples of labels:  [(0, 77), (1, 87), (2, 88), (3, 131), (4, 130), (5, 85), (6, 77), (7, 96), (8, 76), (9, 101)]
--------------------------------------------------
Client 21    Size of data: 917   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 21    Samples of labels:  [(0, 73), (1, 79), (2, 66), (3, 130), (4, 94), (5, 114), (6, 100), (7, 113), (8, 66), (9, 82)]
--------------------------------------------------
Client 22    Size of data: 1007  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 22    Samples of labels:  [(0, 71), (1, 151), (2, 74), (3, 110), (4, 81), (5, 110), (6, 87), (7, 64), (8, 125), (9, 134)]
--------------------------------------------------
Client 23    Size of data: 990   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 23    Samples of labels:  [(0, 127), (1, 89), (2, 118), (3, 64), (4, 132), (5, 93), (6, 86), (7, 86), (8, 79), (9, 116)]
--------------------------------------------------
Client 24    Size of data: 1137  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 24    Samples of labels:  [(0, 125), (1, 135), (2, 134), (3, 93), (4, 128), (5, 108), (6, 130), (7, 134), (8, 76), (9, 74)]
--------------------------------------------------
Client 25    Size of data: 1119  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 25    Samples of labels:  [(0, 86), (1, 156), (2, 130), (3, 127), (4, 124), (5, 101), (6, 117), (7, 100), (8, 82), (9, 96)]
--------------------------------------------------
Client 26    Size of data: 1059  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 26    Samples of labels:  [(0, 121), (1, 138), (2, 135), (3, 139), (4, 81), (5, 86), (6, 73), (7, 82), (8, 94), (9, 110)]
--------------------------------------------------
Client 27    Size of data: 1042  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 27    Samples of labels:  [(0, 65), (1, 126), (2, 112), (3, 99), (4, 103), (5, 91), (6, 105), (7, 91), (8, 123), (9, 127)]
--------------------------------------------------
Client 28    Size of data: 990   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 28    Samples of labels:  [(0, 64), (1, 110), (2, 118), (3, 117), (4, 99), (5, 118), (6, 121), (7, 92), (8, 69), (9, 82)]
--------------------------------------------------
Client 29    Size of data: 935   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 29    Samples of labels:  [(0, 124), (1, 96), (2, 79), (3, 97), (4, 92), (5, 76), (6, 75), (7, 116), (8, 80), (9, 100)]
--------------------------------------------------
Client 30    Size of data: 952   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 30    Samples of labels:  [(0, 72), (1, 152), (2, 69), (3, 66), (4, 86), (5, 76), (6, 100), (7, 114), (8, 124), (9, 93)]
--------------------------------------------------
Client 31    Size of data: 979   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 31    Samples of labels:  [(0, 77), (1, 87), (2, 81), (3, 112), (4, 102), (5, 120), (6, 80), (7, 110), (8, 107), (9, 103)]
--------------------------------------------------
Client 32    Size of data: 1034  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 32    Samples of labels:  [(0, 111), (1, 119), (2, 106), (3, 118), (4, 105), (5, 123), (6, 94), (7, 71), (8, 95), (9, 92)]
--------------------------------------------------
Client 33    Size of data: 1096  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 33    Samples of labels:  [(0, 136), (1, 129), (2, 84), (3, 96), (4, 134), (5, 90), (6, 121), (7, 80), (8, 108), (9, 118)]
--------------------------------------------------
Client 34    Size of data: 977   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 34    Samples of labels:  [(0, 94), (1, 141), (2, 112), (3, 92), (4, 89), (5, 76), (6, 99), (7, 93), (8, 88), (9, 93)]
--------------------------------------------------
Client 35    Size of data: 1015  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 35    Samples of labels:  [(0, 135), (1, 67), (2, 86), (3, 119), (4, 112), (5, 71), (6, 105), (7, 75), (8, 126), (9, 119)]
--------------------------------------------------
Client 36    Size of data: 871   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 36    Samples of labels:  [(0, 67), (1, 64), (2, 77), (3, 95), (4, 114), (5, 87), (6, 66), (7, 125), (8, 85), (9, 91)]
--------------------------------------------------
Client 37    Size of data: 1098  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 37    Samples of labels:  [(0, 134), (1, 141), (2, 117), (3, 92), (4, 126), (5, 103), (6, 100), (7, 78), (8, 83), (9, 124)]
--------------------------------------------------
Client 38    Size of data: 977   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 38    Samples of labels:  [(0, 85), (1, 70), (2, 74), (3, 138), (4, 108), (5, 125), (6, 110), (7, 94), (8, 97), (9, 76)]
--------------------------------------------------
Client 39    Size of data: 957   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 39    Samples of labels:  [(0, 113), (1, 116), (2, 119), (3, 72), (4, 118), (5, 107), (6, 91), (7, 72), (8, 68), (9, 81)]
--------------------------------------------------
Client 40    Size of data: 1109  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 40    Samples of labels:  [(0, 121), (1, 149), (2, 125), (3, 96), (4, 64), (5, 76), (6, 136), (7, 104), (8, 103), (9, 135)]
--------------------------------------------------
Client 41    Size of data: 993   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 41    Samples of labels:  [(0, 67), (1, 134), (2, 120), (3, 72), (4, 80), (5, 114), (6, 92), (7, 112), (8, 131), (9, 71)]
--------------------------------------------------
Client 42    Size of data: 987   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 42    Samples of labels:  [(0, 132), (1, 66), (2, 85), (3, 141), (4, 83), (5, 102), (6, 66), (7, 94), (8, 98), (9, 120)]
--------------------------------------------------
Client 43    Size of data: 972   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 43    Samples of labels:  [(0, 88), (1, 140), (2, 89), (3, 114), (4, 73), (5, 91), (6, 77), (7, 87), (8, 98), (9, 115)]
--------------------------------------------------
Client 44    Size of data: 1109  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 44    Samples of labels:  [(0, 107), (1, 155), (2, 78), (3, 105), (4, 115), (5, 112), (6, 105), (7, 130), (8, 106), (9, 96)]
--------------------------------------------------
Client 45    Size of data: 1035  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 45    Samples of labels:  [(0, 90), (1, 85), (2, 77), (3, 128), (4, 74), (5, 125), (6, 100), (7, 128), (8, 102), (9, 126)]
--------------------------------------------------
Client 46    Size of data: 1058  Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 46    Samples of labels:  [(0, 116), (1, 139), (2, 107), (3, 88), (4, 132), (5, 69), (6, 104), (7, 76), (8, 112), (9, 115)]
--------------------------------------------------
Client 47    Size of data: 841   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 47    Samples of labels:  [(0, 105), (1, 71), (2, 70), (3, 84), (4, 87), (5, 98), (6, 82), (7, 81), (8, 69), (9, 94)]
--------------------------------------------------
Client 48    Size of data: 980   Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 48    Samples of labels:  [(0, 79), (1, 141), (2, 120), (3, 108), (4, 78), (5, 97), (6, 102), (7, 97), (8, 72), (9, 86)]
--------------------------------------------------
Client 49    Size of data: 20754     Labels:  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
Client 49    Samples of labels:  [(0, 2155), (1, 2515), (2, 2142), (3, 1931), (4, 1926), (5, 1526), (6, 1981), (7, 2442), (8, 2208), (9, 1928)]
--------------------------------------------------
Total number of samples: 70000
The number of train samples: [798, 767, 692, 679, 783, 769, 774, 782, 764, 703, 723, 716, 761, 642, 825, 702, 733, 762, 781, 883, 711, 687, 755, 742, 852, 839, 794, 781, 742, 701, 714, 734, 775, 822, 732, 761, 653, 823, 732, 717, 831, 744, 740, 729, 831, 776, 793, 630, 735, 15565]
The number of test samples: [266, 256, 231, 227, 262, 257, 259, 261, 255, 235, 241, 239, 254, 214, 276, 235, 245, 254, 261, 295, 237, 230, 252, 248, 285, 280, 265, 261, 248, 234, 238, 245, 259, 274, 245, 254, 218, 275, 245, 240, 278, 249, 247, 243, 278, 259, 265, 211, 245, 5189]

Finish generating dataset.

The output of generate_mnist.py noniid - -

Original number of samples of each label: [6903, 7877, 6990, 7141, 6824, 6313, 6876, 7293, 6825, 6958]

Client 0     Size of data: 799   Labels:  [0. 1.]
Client 0     Samples of labels:  [(0, 141), (1, 658)]
--------------------------------------------------
Client 1     Size of data: 687   Labels:  [0. 1.]
Client 1     Samples of labels:  [(0, 106), (1, 581)]
--------------------------------------------------
Client 2     Size of data: 4649  Labels:  [0. 1.]
Client 2     Samples of labels:  [(0, 3903), (1, 746)]
--------------------------------------------------

Client 3     Size of data: 853   Labels:  [0. 1.]
Client 3     Samples of labels:  [(0, 213), (1, 640)]
--------------------------------------------------
Client 4     Size of data: 826   Labels:  [0. 1.]
Client 4     Samples of labels:  [(0, 350), (1, 476)]
--------------------------------------------------
Client 5     Size of data: 1133  Labels:  [0. 1.]
Client 5     Samples of labels:  [(0, 577), (1, 556)]
--------------------------------------------------
Client 6     Size of data: 752   Labels:  [0. 1.]
Client 6     Samples of labels:  [(0, 459), (1, 293)]
--------------------------------------------------
Client 7     Size of data: 523   Labels:  [0. 1.]
Client 7     Samples of labels:  [(0, 304), (1, 219)]
--------------------------------------------------
Client 8     Size of data: 362   Labels:  [0. 1.]
Client 8     Samples of labels:  [(0, 198), (1, 164)]
--------------------------------------------------
Client 9     Size of data: 4196  Labels:  [0. 1.]
Client 9     Samples of labels:  [(0, 652), (1, 3544)]
--------------------------------------------------
Client 10    Size of data: 542   Labels:  [2. 3.]
Client 10    Samples of labels:  [(2, 456), (3, 86)]
--------------------------------------------------
Client 11    Size of data: 275   Labels:  [2. 3.]
Client 11    Samples of labels:  [(2, 140), (3, 135)]
--------------------------------------------------
Client 12    Size of data: 4615  Labels:  [2. 3.]
Client 12    Samples of labels:  [(2, 500), (3, 4115)]
--------------------------------------------------
Client 13    Size of data: 1322  Labels:  [2. 3.]
Client 13    Samples of labels:  [(2, 630), (3, 692)]
--------------------------------------------------
Client 14    Size of data: 930   Labels:  [2. 3.]
Client 14    Samples of labels:  [(2, 523), (3, 407)]
--------------------------------------------------
Client 15    Size of data: 701   Labels:  [2. 3.]
Client 15    Samples of labels:  [(2, 333), (3, 368)]
--------------------------------------------------
Client 16    Size of data: 1062  Labels:  [2. 3.]
Client 16    Samples of labels:  [(2, 525), (3, 537)]
--------------------------------------------------
Client 17    Size of data: 1134  Labels:  [2. 3.]
Client 17    Samples of labels:  [(2, 696), (3, 438)]
--------------------------------------------------
Client 18    Size of data: 707   Labels:  [2. 3.]
Client 18    Samples of labels:  [(2, 611), (3, 96)]
--------------------------------------------------
Client 19    Size of data: 2843  Labels:  [2. 3.]
Client 19    Samples of labels:  [(2, 2576), (3, 267)]
--------------------------------------------------
Client 20    Size of data: 880   Labels:  [4. 5.]
Client 20    Samples of labels:  [(4, 347), (5, 533)]
--------------------------------------------------
Client 21    Size of data: 878   Labels:  [4. 5.]
Client 21    Samples of labels:  [(4, 663), (5, 215)]
--------------------------------------------------
Client 22    Size of data: 3938  Labels:  [4. 5.]
Client 22    Samples of labels:  [(4, 3553), (5, 385)]
--------------------------------------------------
Client 23    Size of data: 1009  Labels:  [4. 5.]
Client 23    Samples of labels:  [(4, 381), (5, 628)]
--------------------------------------------------
Client 24    Size of data: 748   Labels:  [4. 5.]
Client 24    Samples of labels:  [(4, 223), (5, 525)]
--------------------------------------------------
Client 25    Size of data: 2630  Labels:  [4. 5.]
Client 25    Samples of labels:  [(4, 449), (5, 2181)]
--------------------------------------------------
Client 26    Size of data: 627   Labels:  [4. 5.]
Client 26    Samples of labels:  [(4, 194), (5, 433)]
--------------------------------------------------
Client 27    Size of data: 934   Labels:  [4. 5.]
Client 27    Samples of labels:  [(4, 356), (5, 578)]
--------------------------------------------------
Client 28    Size of data: 551   Labels:  [4. 5.]
Client 28    Samples of labels:  [(4, 234), (5, 317)]
--------------------------------------------------
Client 29    Size of data: 942   Labels:  [4. 5.]
Client 29    Samples of labels:  [(4, 424), (5, 518)]
--------------------------------------------------
Client 30    Size of data: 781   Labels:  [6. 7.]
Client 30    Samples of labels:  [(6, 220), (7, 561)]
--------------------------------------------------
Client 31    Size of data: 477   Labels:  [6. 7.]
Client 31    Samples of labels:  [(6, 78), (7, 399)]
--------------------------------------------------
Client 32    Size of data: 846   Labels:  [6. 7.]
Client 32    Samples of labels:  [(6, 576), (7, 270)]
--------------------------------------------------
Client 33    Size of data: 1180  Labels:  [6. 7.]
Client 33    Samples of labels:  [(6, 616), (7, 564)]
--------------------------------------------------
Client 34    Size of data: 4165  Labels:  [6. 7.]
Client 34    Samples of labels:  [(6, 3623), (7, 542)]
--------------------------------------------------
Client 35    Size of data: 885   Labels:  [6. 7.]
Client 35    Samples of labels:  [(6, 637), (7, 248)]
--------------------------------------------------
Client 36    Size of data: 3646  Labels:  [6. 7.]
Client 36    Samples of labels:  [(6, 164), (7, 3482)]
--------------------------------------------------
Client 37    Size of data: 1024  Labels:  [6. 7.]
Client 37    Samples of labels:  [(6, 337), (7, 687)]
--------------------------------------------------
Client 38    Size of data: 480   Labels:  [6. 7.]
Client 38    Samples of labels:  [(6, 278), (7, 202)]
--------------------------------------------------
Client 39    Size of data: 685   Labels:  [6. 7.]
Client 39    Samples of labels:  [(6, 347), (7, 338)]
--------------------------------------------------
Client 40    Size of data: 740   Labels:  [8. 9.]
Client 40    Samples of labels:  [(8, 251), (9, 489)]
--------------------------------------------------
Client 41    Size of data: 4175  Labels:  [8. 9.]
Client 41    Samples of labels:  [(8, 299), (9, 3876)]
--------------------------------------------------
Client 42    Size of data: 683   Labels:  [8. 9.]
Client 42    Samples of labels:  [(8, 164), (9, 519)]
--------------------------------------------------
Client 43    Size of data: 769   Labels:  [8. 9.]
Client 43    Samples of labels:  [(8, 164), (9, 605)]
--------------------------------------------------
Client 44    Size of data: 653   Labels:  [8. 9.]
Client 44    Samples of labels:  [(8, 385), (9, 268)]
--------------------------------------------------
Client 45    Size of data: 726   Labels:  [8. 9.]
Client 45    Samples of labels:  [(8, 636), (9, 90)]
--------------------------------------------------
Client 46    Size of data: 472   Labels:  [8. 9.]
Client 46    Samples of labels:  [(8, 78), (9, 394)]
--------------------------------------------------
Client 47    Size of data: 838   Labels:  [8. 9.]
Client 47    Samples of labels:  [(8, 473), (9, 365)]
--------------------------------------------------
Client 48    Size of data: 883   Labels:  [8. 9.]
Client 48    Samples of labels:  [(8, 677), (9, 206)]
--------------------------------------------------
Client 49    Size of data: 3844  Labels:  [8. 9.]
Client 49    Samples of labels:  [(8, 3698), (9, 146)]
--------------------------------------------------
Total number of samples: 70000
The number of train samples: [599, 515, 3486, 639, 619, 849, 564, 392, 271, 3147, 406, 206, 3461, 991, 697, 525, 796, 850, 530, 2132, 660, 658, 2953, 756, 561, 1972, 470, 700, 413, 706, 585, 357, 634, 885, 3123, 663, 2734, 768, 360, 513, 555, 3131, 512, 576, 489, 544, 354, 628, 662, 2883]
The number of test samples: [200, 172, 1163, 214, 207, 284, 188, 131, 91, 1049, 136, 69, 1154, 331, 233, 176, 266, 284, 177, 711, 220, 220, 985, 253, 187, 658, 157, 234, 138, 236, 196, 120, 212, 295, 1042, 222, 912, 256, 120, 172, 185, 1044, 171, 193, 164, 182, 118, 210, 221, 961]

Finish generating dataset.

The output of generate_mnist.py noniid - dir (alpha = 0.1 for the Dirichlet distribution in ./dataset/utils/dataset_utils.py)

Original number of samples of each label: [6903, 7877, 6990, 7141, 6824, 6313, 6876, 7293, 6825, 6958]

Client 0         Size of data: 1059      Labels:  [1. 3. 4. 6. 8.]
Client 0         Samples of labels:  [(1, 71), (3, 98), (4, 228), (6, 577), (8, 85)]
--------------------------------------------------
Client 1         Size of data: 1138      Labels:  [2. 3. 4. 7. 8.]
Client 1         Samples of labels:  [(2, 198), (3, 138), (4, 201), (7, 515), (8, 86)]
--------------------------------------------------
Client 2         Size of data: 755       Labels:  [0. 1. 3. 7. 8.]
Client 2         Samples of labels:  [(0, 75), (1, 107), (3, 130), (7, 291), (8, 152)]
--------------------------------------------------

Client 3         Size of data: 875       Labels:  [1. 3. 5. 7.]
Client 3         Samples of labels:  [(1, 254), (3, 74), (5, 160), (7, 387)]
--------------------------------------------------
Client 4         Size of data: 4228      Labels:  [0. 2. 4. 5. 7. 8.]
Client 4         Samples of labels:  [(0, 77), (2, 276), (4, 173), (5, 483), (7, 3087), (8, 132)]
--------------------------------------------------
Client 5         Size of data: 800       Labels:  [0. 1. 2. 3. 4. 8.]
Client 5         Samples of labels:  [(0, 140), (1, 269), (2, 120), (3, 94), (4, 77), (8, 100)]
--------------------------------------------------
Client 6         Size of data: 3286      Labels:  [0. 1. 2. 3. 4. 8.]
Client 6         Samples of labels:  [(0, 2434), (1, 213), (2, 281), (3, 132), (4, 117), (8, 109)]
--------------------------------------------------
Client 7         Size of data: 413       Labels:  [2. 3. 4. 8.]
Client 7         Samples of labels:  [(2, 160), (3, 80), (4, 87), (8, 86)]
--------------------------------------------------
Client 8         Size of data: 641       Labels:  [1. 3. 7. 8.]
Client 8         Samples of labels:  [(1, 129), (3, 127), (7, 238), (8, 147)]
--------------------------------------------------
Client 9         Size of data: 3359      Labels:  [0. 2. 3. 6. 8.]
Client 9         Samples of labels:  [(0, 132), (2, 263), (3, 69), (6, 2791), (8, 104)]
--------------------------------------------------
Client 10        Size of data: 461       Labels:  [0. 3. 4. 8.]
Client 10        Samples of labels:  [(0, 171), (3, 96), (4, 103), (8, 91)]
--------------------------------------------------
Client 11        Size of data: 7555      Labels:  [0. 1. 3. 7. 9.]
Client 11        Samples of labels:  [(0, 135), (1, 247), (3, 142), (7, 73), (9, 6958)]
--------------------------------------------------
Client 12        Size of data: 2435      Labels:  [0. 2. 3. 8.]
Client 12        Samples of labels:  [(0, 160), (2, 88), (3, 138), (8, 2049)]
--------------------------------------------------
Client 13        Size of data: 883       Labels:  [3. 5. 7. 8.]
Client 13        Samples of labels:  [(3, 64), (5, 267), (7, 417), (8, 135)]
--------------------------------------------------
Client 14        Size of data: 542       Labels:  [0. 1. 4. 8.]
Client 14        Samples of labels:  [(0, 89), (1, 138), (4, 186), (8, 129)]
--------------------------------------------------
Client 15        Size of data: 1403      Labels:  [0. 1. 2. 3. 4. 5. 7. 8.]
Client 15        Samples of labels:  [(0, 78), (1, 262), (2, 312), (3, 83), (4, 116), (5, 96), (7, 348), (8, 108)]
--------------------------------------------------
Client 16        Size of data: 990       Labels:  [0. 1. 3. 7. 8.]
Client 16        Samples of labels:  [(0, 169), (1, 224), (3, 73), (7, 374), (8, 150)]
--------------------------------------------------
Client 17        Size of data: 296       Labels:  [2. 3. 8.]
Client 17        Samples of labels:  [(2, 74), (3, 143), (8, 79)]
--------------------------------------------------
Client 18        Size of data: 242       Labels:  [0. 3.]
Client 18        Samples of labels:  [(0, 114), (3, 128)]
--------------------------------------------------
Client 19        Size of data: 642       Labels:  [0. 1. 3. 4. 8.]
Client 19        Samples of labels:  [(0, 151), (1, 94), (3, 88), (4, 159), (8, 150)]
--------------------------------------------------
Client 20        Size of data: 852       Labels:  [0. 3. 5. 8.]
Client 20        Samples of labels:  [(0, 177), (3, 126), (5, 470), (8, 79)]
--------------------------------------------------
Client 21        Size of data: 2732      Labels:  [0. 1. 2. 3. 8.]
Client 21        Samples of labels:  [(0, 73), (1, 140), (2, 248), (3, 2119), (8, 152)]
--------------------------------------------------
Client 22        Size of data: 1114      Labels:  [1. 3. 4. 6. 8.]
Client 22        Samples of labels:  [(1, 66), (3, 89), (4, 134), (6, 719), (8, 106)]
--------------------------------------------------
Client 23        Size of data: 503       Labels:  [0. 4. 8.]
Client 23        Samples of labels:  [(0, 143), (4, 214), (8, 146)]
--------------------------------------------------
Client 24        Size of data: 634       Labels:  [2. 3. 4. 5. 8.]
Client 24        Samples of labels:  [(2, 180), (3, 115), (4, 162), (5, 70), (8, 107)]
--------------------------------------------------
Client 25        Size of data: 3779      Labels:  [0. 1. 2. 3. 4. 5. 7. 8.]
Client 25        Samples of labels:  [(0, 76), (1, 192), (2, 205), (3, 108), (4, 2571), (5, 206), (7, 323), (8, 98)]
--------------------------------------------------
Client 26        Size of data: 1243      Labels:  [0. 1. 2. 3. 4. 6. 8.]
Client 26        Samples of labels:  [(0, 158), (1, 116), (2, 141), (3, 92), (4, 152), (6, 472), (8, 112)]
--------------------------------------------------
Client 27        Size of data: 1092      Labels:  [0. 1. 3. 6. 8.]
Client 27        Samples of labels:  [(0, 114), (1, 110), (3, 134), (6, 600), (8, 134)]
--------------------------------------------------
Client 28        Size of data: 494       Labels:  [0. 3. 6. 8.]
Client 28        Samples of labels:  [(0, 69), (3, 81), (6, 229), (8, 115)]
--------------------------------------------------
Client 29        Size of data: 887       Labels:  [0. 1. 3. 6. 8.]
Client 29        Samples of labels:  [(0, 80), (1, 267), (3, 112), (6, 336), (8, 92)]
--------------------------------------------------
Client 30        Size of data: 520       Labels:  [2. 3. 8.]
Client 30        Samples of labels:  [(2, 269), (3, 105), (8, 146)]
--------------------------------------------------
Client 31        Size of data: 1619      Labels:  [0. 1. 2. 3. 4. 7. 8.]
Client 31        Samples of labels:  [(0, 165), (1, 264), (2, 201), (3, 131), (4, 240), (7, 491), (8, 127)]
--------------------------------------------------
Client 32        Size of data: 846       Labels:  [0. 2. 3. 4. 8.]
Client 32        Samples of labels:  [(0, 73), (2, 295), (3, 86), (4, 249), (8, 143)]
--------------------------------------------------
Client 33        Size of data: 1833      Labels:  [0. 1. 3. 4. 6. 7.]
Client 33        Samples of labels:  [(0, 170), (1, 140), (3, 141), (4, 128), (6, 743), (7, 511)]
--------------------------------------------------
Client 34        Size of data: 1080      Labels:  [0. 1. 2. 3. 4. 6. 8.]
Client 34        Samples of labels:  [(0, 92), (1, 84), (2, 160), (3, 145), (4, 94), (6, 409), (8, 96)]
--------------------------------------------------
Client 35        Size of data: 962       Labels:  [0. 1. 3. 5. 8.]
Client 35        Samples of labels:  [(0, 84), (1, 215), (3, 106), (5, 407), (8, 150)]
--------------------------------------------------
Client 36        Size of data: 493       Labels:  [0. 2. 3. 8.]
Client 36        Samples of labels:  [(0, 70), (2, 247), (3, 96), (8, 80)]
--------------------------------------------------
Client 37        Size of data: 468       Labels:  [0. 1. 3. 8.]
Client 37        Samples of labels:  [(0, 128), (1, 141), (3, 124), (8, 75)]
--------------------------------------------------
Client 38        Size of data: 3961      Labels:  [0. 1. 3. 4. 8.]
Client 38        Samples of labels:  [(0, 169), (1, 3440), (3, 83), (4, 204), (8, 65)]
--------------------------------------------------
Client 39        Size of data: 1104      Labels:  [0. 2. 3. 4. 5. 8.]
Client 39        Samples of labels:  [(0, 148), (2, 89), (3, 124), (4, 148), (5, 443), (8, 152)]
--------------------------------------------------
Client 40        Size of data: 613       Labels:  [0. 1. 3. 4. 8.]
Client 40        Samples of labels:  [(0, 139), (1, 70), (3, 102), (4, 167), (8, 135)]
--------------------------------------------------
Client 41        Size of data: 3678      Labels:  [0. 1. 3. 5. 8.]
Client 41        Samples of labels:  [(0, 82), (1, 141), (3, 99), (5, 3292), (8, 64)]
--------------------------------------------------
Client 42        Size of data: 444       Labels:  [0. 2. 3. 8.]
Client 42        Samples of labels:  [(0, 151), (2, 85), (3, 118), (8, 90)]
--------------------------------------------------
Client 43        Size of data: 955       Labels:  [0. 1. 3. 4. 5. 8.]
Client 43        Samples of labels:  [(0, 150), (1, 177), (3, 81), (4, 214), (5, 255), (8, 78)]
--------------------------------------------------
Client 44        Size of data: 486       Labels:  [3. 4. 7. 8.]
Client 44        Samples of labels:  [(3, 102), (4, 125), (7, 144), (8, 115)]
--------------------------------------------------
Client 45        Size of data: 523       Labels:  [0. 3. 4. 5.]
Client 45        Samples of labels:  [(0, 65), (3, 147), (4, 147), (5, 164)]
--------------------------------------------------
Client 46        Size of data: 386       Labels:  [0. 1. 3. 8.]
Client 46        Samples of labels:  [(0, 93), (1, 67), (3, 114), (8, 112)]
--------------------------------------------------
Client 47        Size of data: 794       Labels:  [0. 1. 3. 4. 7. 8.]
Client 47        Samples of labels:  [(0, 136), (1, 100), (3, 150), (4, 233), (7, 94), (8, 81)]
--------------------------------------------------
Client 48        Size of data: 471       Labels:  [0. 3. 4.]
Client 48        Samples of labels:  [(0, 173), (3, 103), (4, 195)]
--------------------------------------------------
Client 49        Size of data: 3431      Labels:  [1. 2. 3. 8.]
Client 49        Samples of labels:  [(1, 139), (2, 3098), (3, 111), (8, 83)]
--------------------------------------------------
Total number of samples: 70000
The number of train samples: [794, 853, 566, 656, 3171, 600, 2464, 309, 480, 2519, 345, 5666, 1826, 662, 406, 1052, 742, 222, 181, 481, 639, 2049, 835, 377, 475, 2834, 932, 819, 370, 665, 390, 1214, 634, 1374, 810, 721, 369, 351, 2970, 828, 459, 2758, 333, 716, 364, 392, 289, 595, 353, 2573]
The number of test samples: [265, 285, 189, 219, 1057, 200, 822, 104, 161, 840, 116, 1889, 609, 221, 136, 351, 248, 74, 61, 161, 213, 683, 279, 126, 159, 945, 311, 273, 124, 222, 130, 405, 212, 459, 270, 241, 124, 117, 991, 276, 154, 920, 111, 239, 122, 131, 97, 199, 118, 858]

Finish generating dataset.

Models

for MNIST and Fashion-MNIST
1. Mclr_Logistic(1*28*28)
2. LeNet()
3. DNN(1*28*28, 100) # non-convex
for Cifar10, Cifar100 and Tiny-ImageNet
1. Mclr_Logistic(3*32*32)
2. FedAvgCNN()
3. DNN(3*32*32, 100) # non-convex
4. ResNet18, Resnet50 and Resnet152
for AG_News and Sogou_News
1. LSTM()
2. fastText() in Bag of Tricks for Efficient Text Classification
3. TextCNN() in Convolutional Neural Networks for Sentence Classification

Environments

With the installed conda, we can run this platform in a conda virtual environment called fl_torch. Note: due to the code updates, some modules are required to install based on the given *.yml.

# current version
conda env create -f env.yml # for Linux

How to start simulating

Build dataset: Datasets

Train and evaluate the model:

cd ./system
python main.py -data mnist -m cnn -algo FedAvg -gr 2500 -did 0 -go cnn # for FedAvg and MNIST

Or you can uncomment the lines you need in ./system/auto_train.sh and run:

cd ./system
sh auto_train.sh

Plot the result test accuracy and training loss curves and save them to figures:
```
python plot.py 
```
Then check the figures in ./figures.

Note: All the hyper-parameters have been tuned for all the algorithms, which are recorded in ./system/auto_train.sh

Practical setting

If you need to simulate FL in a practical setting, which includes client dropout, slow trainers, slow senders, and network TTL, you can set the following parameters to realize it.

-cdr: The dropout rate for total clients. The selected clients will randomly drop at each training round.
-tsr and -ssr: The rates for slow trainers and slow senders among all clients. Once a client was selected as "slow trainers", for example, it will always train slower than the original one. So does "slow senders".
-tth: The threshold for network TTL (ms).

Easy to extend

It is easy to add new datasets or algorithms to this platform.

To add a new dataset into this platform, all you need to do is writing the download code and using the utils the same as ./dataset/generate_mnist.py (you can also consider it as the template).
To add a new algorithm, you can utilize the class server and class client, which are wrote in ./system/flcore/servers/serverbase.py and ./system/flcore/clients/clientbase.py, respectively.
To add a new model, just add it into ./system/flcore/trainmodel/models.py.
If you have an individual optimizer while training, please add it into ./system/flcore/optimizers/fedoptimizer.py

ms116/PFL-Non-IID