PFLlib
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Published
20 hours ago •
TsingZ0
TsingZ0
We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study
Personalized Federated Learning Platform
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
- FedDyn — Federated Learning Based on Dynamic Regularization ICLR 2021
- MOON — Model-Contrastive Federated Learning CVPR 2021
- FedRoD — On Bridging Generic and Personalized Federated Learning for Image Classification ICLR 2022
- FedProto — FedProto: Federated Prototype Learning across Heterogeneous Clients AAAI 2022
Datasets (updating)
I currently using seven famous datasets: MNIST, Fashion-MNIST, Cifar10, Cifar100, AG_News, Sogou_News (If ConnectionError raises, please use the given downloaded file in ./dataset) and Tiny-ImageNet (Please download the original data from http://cs231n.stanford.edu/tiny-imagenet-200.zip), 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 ./dataset/utils/dataset_utils.py. 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, two situations exist. The first one is the pathological Non-IID setting, the second one is practical Non-IID setting. 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). We can input balance for the iid setting, where the data are uniformly distributed.
Examples for MNIST
- MNIST
cd ./dataset python generate_mnist.py iid - - # for iid and unbalanced setting # python generate_mnist.py iid balance - # for iid and balanced setting # python generate_mnist.py noniid - pat # for pathological noniid and unbalanced setting # python generate_mnist.py noniid - dir # for practical noniid and unbalanced setting
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)]
--------------------------------------------------
Show more
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)]
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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)]
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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 - pat
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)]
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Client 2 Size of data: 4649 Labels: [0. 1.]
Client 2 Samples of labels: [(0, 3903), (1, 746)]
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Show more
Client 3 Size of data: 853 Labels: [0. 1.]
Client 3 Samples of labels: [(0, 213), (1, 640)]
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Client 4 Size of data: 826 Labels: [0. 1.]
Client 4 Samples of labels: [(0, 350), (1, 476)]
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Client 5 Size of data: 1133 Labels: [0. 1.]
Client 5 Samples of labels: [(0, 577), (1, 556)]
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Client 6 Size of data: 752 Labels: [0. 1.]
Client 6 Samples of labels: [(0, 459), (1, 293)]
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Client 7 Size of data: 523 Labels: [0. 1.]
Client 7 Samples of labels: [(0, 304), (1, 219)]
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Client 8 Size of data: 362 Labels: [0. 1.]
Client 8 Samples of labels: [(0, 198), (1, 164)]
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Client 9 Size of data: 4196 Labels: [0. 1.]
Client 9 Samples of labels: [(0, 652), (1, 3544)]
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Client 10 Size of data: 542 Labels: [2. 3.]
Client 10 Samples of labels: [(2, 456), (3, 86)]
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Client 11 Size of data: 275 Labels: [2. 3.]
Client 11 Samples of labels: [(2, 140), (3, 135)]
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Client 12 Size of data: 4615 Labels: [2. 3.]
Client 12 Samples of labels: [(2, 500), (3, 4115)]
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Client 13 Size of data: 1322 Labels: [2. 3.]
Client 13 Samples of labels: [(2, 630), (3, 692)]
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Client 14 Size of data: 930 Labels: [2. 3.]
Client 14 Samples of labels: [(2, 523), (3, 407)]
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Client 15 Size of data: 701 Labels: [2. 3.]
Client 15 Samples of labels: [(2, 333), (3, 368)]
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Client 16 Size of data: 1062 Labels: [2. 3.]
Client 16 Samples of labels: [(2, 525), (3, 537)]
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Client 17 Size of data: 1134 Labels: [2. 3.]
Client 17 Samples of labels: [(2, 696), (3, 438)]
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Client 18 Size of data: 707 Labels: [2. 3.]
Client 18 Samples of labels: [(2, 611), (3, 96)]
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Client 19 Size of data: 2843 Labels: [2. 3.]
Client 19 Samples of labels: [(2, 2576), (3, 267)]
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Client 20 Size of data: 880 Labels: [4. 5.]
Client 20 Samples of labels: [(4, 347), (5, 533)]
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Client 21 Size of data: 878 Labels: [4. 5.]
Client 21 Samples of labels: [(4, 663), (5, 215)]
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Client 22 Size of data: 3938 Labels: [4. 5.]
Client 22 Samples of labels: [(4, 3553), (5, 385)]
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Client 23 Size of data: 1009 Labels: [4. 5.]
Client 23 Samples of labels: [(4, 381), (5, 628)]
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Client 24 Size of data: 748 Labels: [4. 5.]
Client 24 Samples of labels: [(4, 223), (5, 525)]
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Client 25 Size of data: 2630 Labels: [4. 5.]
Client 25 Samples of labels: [(4, 449), (5, 2181)]
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Client 26 Size of data: 627 Labels: [4. 5.]
Client 26 Samples of labels: [(4, 194), (5, 433)]
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Client 27 Size of data: 934 Labels: [4. 5.]
Client 27 Samples of labels: [(4, 356), (5, 578)]
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Client 28 Size of data: 551 Labels: [4. 5.]
Client 28 Samples of labels: [(4, 234), (5, 317)]
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Client 29 Size of data: 942 Labels: [4. 5.]
Client 29 Samples of labels: [(4, 424), (5, 518)]
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Client 30 Size of data: 781 Labels: [6. 7.]
Client 30 Samples of labels: [(6, 220), (7, 561)]
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Client 31 Size of data: 477 Labels: [6. 7.]
Client 31 Samples of labels: [(6, 78), (7, 399)]
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Client 32 Size of data: 846 Labels: [6. 7.]
Client 32 Samples of labels: [(6, 576), (7, 270)]
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Client 33 Size of data: 1180 Labels: [6. 7.]
Client 33 Samples of labels: [(6, 616), (7, 564)]
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Client 34 Size of data: 4165 Labels: [6. 7.]
Client 34 Samples of labels: [(6, 3623), (7, 542)]
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Client 35 Size of data: 885 Labels: [6. 7.]
Client 35 Samples of labels: [(6, 637), (7, 248)]
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Client 36 Size of data: 3646 Labels: [6. 7.]
Client 36 Samples of labels: [(6, 164), (7, 3482)]
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Client 37 Size of data: 1024 Labels: [6. 7.]
Client 37 Samples of labels: [(6, 337), (7, 687)]
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Client 38 Size of data: 480 Labels: [6. 7.]
Client 38 Samples of labels: [(6, 278), (7, 202)]
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Client 39 Size of data: 685 Labels: [6. 7.]
Client 39 Samples of labels: [(6, 347), (7, 338)]
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Client 40 Size of data: 740 Labels: [8. 9.]
Client 40 Samples of labels: [(8, 251), (9, 489)]
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Client 41 Size of data: 4175 Labels: [8. 9.]
Client 41 Samples of labels: [(8, 299), (9, 3876)]
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Client 42 Size of data: 683 Labels: [8. 9.]
Client 42 Samples of labels: [(8, 164), (9, 519)]
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Client 43 Size of data: 769 Labels: [8. 9.]
Client 43 Samples of labels: [(8, 164), (9, 605)]
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Client 44 Size of data: 653 Labels: [8. 9.]
Client 44 Samples of labels: [(8, 385), (9, 268)]
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Client 45 Size of data: 726 Labels: [8. 9.]
Client 45 Samples of labels: [(8, 636), (9, 90)]
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Client 46 Size of data: 472 Labels: [8. 9.]
Client 46 Samples of labels: [(8, 78), (9, 394)]
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Client 47 Size of data: 838 Labels: [8. 9.]
Client 47 Samples of labels: [(8, 473), (9, 365)]
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Client 48 Size of data: 883 Labels: [8. 9.]
Client 48 Samples of labels: [(8, 677), (9, 206)]
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Client 49 Size of data: 3844 Labels: [8. 9.]
Client 49 Samples of labels: [(8, 3698), (9, 146)]
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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)]
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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)]
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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)]
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Show more
Client 3 Size of data: 875 Labels: [1. 3. 5. 7.]
Client 3 Samples of labels: [(1, 254), (3, 74), (5, 160), (7, 387)]
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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)]
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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)]
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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)]
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Client 7 Size of data: 413 Labels: [2. 3. 4. 8.]
Client 7 Samples of labels: [(2, 160), (3, 80), (4, 87), (8, 86)]
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Client 8 Size of data: 641 Labels: [1. 3. 7. 8.]
Client 8 Samples of labels: [(1, 129), (3, 127), (7, 238), (8, 147)]
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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)]
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Client 10 Size of data: 461 Labels: [0. 3. 4. 8.]
Client 10 Samples of labels: [(0, 171), (3, 96), (4, 103), (8, 91)]
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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)]
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Client 12 Size of data: 2435 Labels: [0. 2. 3. 8.]
Client 12 Samples of labels: [(0, 160), (2, 88), (3, 138), (8, 2049)]
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Client 13 Size of data: 883 Labels: [3. 5. 7. 8.]
Client 13 Samples of labels: [(3, 64), (5, 267), (7, 417), (8, 135)]
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Client 14 Size of data: 542 Labels: [0. 1. 4. 8.]
Client 14 Samples of labels: [(0, 89), (1, 138), (4, 186), (8, 129)]
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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)]
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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)]
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Client 17 Size of data: 296 Labels: [2. 3. 8.]
Client 17 Samples of labels: [(2, 74), (3, 143), (8, 79)]
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Client 18 Size of data: 242 Labels: [0. 3.]
Client 18 Samples of labels: [(0, 114), (3, 128)]
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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)]
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Client 20 Size of data: 852 Labels: [0. 3. 5. 8.]
Client 20 Samples of labels: [(0, 177), (3, 126), (5, 470), (8, 79)]
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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)]
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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)]
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Client 23 Size of data: 503 Labels: [0. 4. 8.]
Client 23 Samples of labels: [(0, 143), (4, 214), (8, 146)]
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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)]
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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)]
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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)]
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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)]
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Client 28 Size of data: 494 Labels: [0. 3. 6. 8.]
Client 28 Samples of labels: [(0, 69), (3, 81), (6, 229), (8, 115)]
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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)]
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Client 30 Size of data: 520 Labels: [2. 3. 8.]
Client 30 Samples of labels: [(2, 269), (3, 105), (8, 146)]
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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)]
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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)]
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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)]
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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)]
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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)]
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Client 36 Size of data: 493 Labels: [0. 2. 3. 8.]
Client 36 Samples of labels: [(0, 70), (2, 247), (3, 96), (8, 80)]
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Client 37 Size of data: 468 Labels: [0. 1. 3. 8.]
Client 37 Samples of labels: [(0, 128), (1, 141), (3, 124), (8, 75)]
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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)]
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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)]
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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)]
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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)]
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Client 42 Size of data: 444 Labels: [0. 2. 3. 8.]
Client 42 Samples of labels: [(0, 151), (2, 85), (3, 118), (8, 90)]
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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)]
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Client 44 Size of data: 486 Labels: [3. 4. 7. 8.]
Client 44 Samples of labels: [(3, 102), (4, 125), (7, 144), (8, 115)]
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Client 45 Size of data: 523 Labels: [0. 3. 4. 5.]
Client 45 Samples of labels: [(0, 65), (3, 147), (4, 147), (5, 164)]
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Client 46 Size of data: 386 Labels: [0. 1. 3. 8.]
Client 46 Samples of labels: [(0, 93), (1, 67), (3, 114), (8, 112)]
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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)]
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Client 48 Size of data: 471 Labels: [0. 3. 4.]
Client 48 Samples of labels: [(0, 173), (3, 103), (4, 195)]
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Client 49 Size of data: 3431 Labels: [1. 2. 3. 8.]
Client 49 Samples of labels: [(1, 139), (2, 3098), (3, 111), (8, 83)]
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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
- Mclr_Logistic(1*28*28)
- LeNet()
- DNN(1*28*28, 100) # non-convex
-
for Cifar10, Cifar100 and Tiny-ImageNet
- Mclr_Logistic(3*32*32)
- FedAvgCNN()
- DNN(3*32*32, 100) # non-convex
- ResNet18, AlexNet, MobileNet, GoogleNet, etc.
-
for AG_News and Sogou_News
- LSTM()
- fastText() in Bag of Tricks for Efficient Text Classification
- 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 *.yaml.
conda env create -f env_linux.yaml # 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 MNISTOr you can uncomment the lines you need in
./system/auto_train.shand run:cd ./system sh auto_train.sh
Note: The hyper-parameters have not been tuned for the algorithms. The values in ./system/auto_train.sh are just examples. You need to tune the hyper-parameters by yourself.
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. -
-tsrand-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.pyand./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
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