PFLlib: Personalized Federated Learning Algorithm Library

Figure 1: An Example for FedAvg. You can create a scenario using generate_X.py and run an algorithm using main.py, clientX.py, and serverX.py.

We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study.

34 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 14 datasets.
Some experimental results are avalible here.
Refer to this guide to learn how to use it.
This library can simulate scenarios using the 4-layer CNN on Cifar100 for 500 clients on one NVIDIA GeForce RTX 3090 GPU card with only 5.08GB GPU memory cost.
To simultaneously support statistical and model heterogeneity, please refer to our extended project Heterogeneous Federated Learning (HtFL).
As we strive to meet diverse user demands, frequent updates to the project may alter default settings and scenario creation codes, affecting experimental results.

The origin of the statistical heterogeneity phenomenon is the personalization of users, who generate the non-IID (not Independent and Identically Distributed) and unbalanced data. With statistical heterogeneity existing in the FL scenario, a myriad of approaches have been proposed to crack this hard nut. In contrast, the personalized FL (pFL) may take the advantage of the statistically heterogeneious data to learn the personalized model for each user.

Thanks to @Stonesjtu, this library can also record the GPU memory usage for the model. By using the package opacus, we introduce DP (differential privacy) into this library (please refer to ./system/flcore/clients/clientavg.py for example). We also introduce the DLG (Deep Leakage from Gradients) attack and PSNR (Peak Signal-to-Noise Ratio) metric to evaluate the privacy-preserving ability of tFL/pFL algorithms (please refer to ./system/flcore/servers/serveravg.py for example). Now we can train on some clients and evaluate on other new clients by setting args.num_new_clients in ./system/main.py. Note that not all the tFL/pFL algorithms support this feature.

Citation

@article{zhang2023pfllib,
  title={PFLlib: Personalized Federated Learning Algorithm Library},
  author={Zhang, Jianqing and Liu, Yang and Hua, Yang and Wang, Hao and Song, Tao and Xue, Zhengui and Ma, Ruhui and Cao, Jian},
  journal={arXiv preprint arXiv:2312.04992},
  year={2023}
}

Algorithms with code (updating)

Traditional FL (tFL)

FedAvg — Communication-Efficient Learning of Deep Networks from Decentralized Data AISTATS 2017

Update-correction-based tFL
SCAFFOLD - SCAFFOLD: Stochastic Controlled Averaging for Federated Learning ICML 2020

Regularization-based tFL
FedProx — Federated Optimization in Heterogeneous Networks MLsys 2020
FedDyn — Federated Learning Based on Dynamic Regularization ICLR 2021

Model-splitting-based tFL
MOON — Model-Contrastive Federated Learning CVPR 2021

Knowledge-distillation-based tFL
FedGen — Data-Free Knowledge Distillation for Heterogeneous Federated Learning ICML 2021
FedNTD — Preservation of the Global Knowledge by Not-True Distillation in Federated Learning NeurIPS 2022

Personalized FL (pFL)

FedMTL (not MOCHA) — Federated multi-task learning NeurIPS 2017
FedBN — FedBN: Federated Learning on non-IID Features via Local Batch Normalization ICLR 2021

Meta-learning-based pFL
Per-FedAvg — Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach NeurIPS 2020

Regularization-based pFL
pFedMe — Personalized Federated Learning with Moreau Envelopes NeurIPS 2020
Ditto — Ditto: Fair and robust federated learning through personalization ICML 2021

Personalized-aggregation-based pFL
APFL — Adaptive Personalized Federated Learning 2020
FedFomo — Personalized Federated Learning with First Order Model Optimization ICLR 2021
FedAMP — Personalized Cross-Silo Federated Learning on non-IID Data AAAI 2021
FedPHP — FedPHP: Federated Personalization with Inherited Private Models ECML PKDD 2021
APPLE — Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning IJCAI 2022
FedALA — FedALA: Adaptive Local Aggregation for Personalized Federated Learning AAAI 2023

Model-splitting-based pFL
FedPer — Federated Learning with Personalization Layers 2019
LG-FedAvg — Think Locally, Act Globally: Federated Learning with Local and Global Representations 2020
FedRep — Exploiting Shared Representations for Personalized Federated Learning ICML 2021
FedRoD — On Bridging Generic and Personalized Federated Learning for Image Classification ICLR 2022
FedBABU — Fedbabu: Towards enhanced representation for federated image classification ICLR 2022
FedGC — Federated Learning for Face Recognition with Gradient Correction AAAI 2022
FedCP — FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy KDD 2023
GPFL — GPFL: Simultaneously Learning Generic and Personalized Feature Information for Personalized Federated Learning ICCV 2023
FedGH — FedGH: Heterogeneous Federated Learning with Generalized Global Header ACM MM 2023
DBE — Eliminating Domain Bias for Federated Learning in Representation Space NeurIPS 2023

Knowledge-distillation-based pFL
FedDistill — Federated Knowledge Distillation 2020
FML — Federated Mutual Learning 2020
FedKD — Communication-efficient federated learning via knowledge distillation Nature Communications 2022
FedProto — FedProto: Federated Prototype Learning across Heterogeneous Clients AAAI 2022
FedPCL (w/o pre-trained models) — Federated learning from pre-trained models: A contrastive learning approach NeurIPS 2022
FedPAC — Personalized Federated Learning with Feature Alignment and Classifier Collaboration ICLR 2023

Datasets and scenarios (updating)

For the label skew scenario, we introduce 8 famous datasets: MNIST, Fashion-MNIST, Cifar10, Cifar100, AG_News, Sogou_News, and Tiny-ImageNet (fetch raw data from this site), 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, we move these codes into ./dataset/utils/dataset_utils.py. In non-IID scenario, 2 situations exist. The first one is the pathological non-IID scenario, the second one is practical non-IID scenario. In the pathological non-IID scenario, for example, the data on each client only contains the specific number of labels (maybe only 2 labels), though the data on all clients contains 10 labels such as MNIST dataset. In the practical non-IID scenario, Dirichlet distribution is utilized (please refer to this paper for details). We can input balance for the iid scenario, where the data are uniformly distributed.

For the feature shift scenario, we use 3 datasets that are widely used in Domain Adaptation: AmazonReview (fetch raw data from this site), Digit5 (fetch raw data from this site), and DomainNet.

For the real-world (or IoT) scenario, we also introduce 3 naturally separated datasets: Omniglot (20 clients, 50 labels), HAR (Human Activity Recognition) (30 clients, 6 labels), PAMAP2 (9 clients, 12 labels). For the details of datasets and FL algorithms in IoT, please refer to my FL-IoT repo.

If you need another data set, just write another code to download it and then using the utils.

Examples for MNIST

MNIST

cd ./dataset
python generate_mnist.py iid - - # for iid and unbalanced scenario
# python generate_mnist.py iid balance - # for iid and balanced scenario
# python generate_mnist.py noniid - pat # for pathological noniid and unbalanced scenario
# python generate_mnist.py noniid - dir # for practical noniid and unbalanced scenario

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 - 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)]
--------------------------------------------------
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, AlexNet, MobileNet, GoogleNet, etc.
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
4. TransformerModel() in Attention is all you need
for AmazonReview
1. AmazonMLP() in Curriculum manager for source selection in multi-source domain adaptation
for Omniglot
1. FedAvgCNN()
for HAR and PAMAP
1. HARCNN() in Convolutional neural networks for human activity recognition using mobile sensors

Environments

Install CUDA.

Install conda and activate conda.

conda env create -f env_cuda_latest.yaml # You may need to downgrade the torch using pip to match CUDA version

How to start simulating (examples for FedAvg)

Create proper environments (see Environments).

Download this project to an appropriate location using git.

git clone https://github.com/TsingZ0/PFLlib.git

Build evaluation scenarios (see Datasets and scenarios (updating)).

Run evaluation:

cd ./system
python main.py -data mnist -m cnn -algo FedAvg -gr 2000 -did 0 # using the MNIST dataset, the FedAvg algorithm, and the 4-layer CNN model

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

cd ./system
sh examples.sh

Note: The hyper-parameters have not been tuned for the algorithms. The values in ./system/examples.sh are just examples. You need to tune the hyper-parameters by yourself.

Practical situations

If you need to simulate FL under practical situations, 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 algorithms and datasets to this library.

To add a new dataset into this library, all you need to do is writing the download code and using the utils which is similar to ./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 a new optimizer while training, please add it into ./system/flcore/optimizers/fedoptimizer.py
The evaluation platform is also convenient for users to bulid a new platform for specific applications, such as our FL-IoT and HtFL.

Experimental results

If you are interested in the experimental results (e.g., the accuracy) of above algorithms, you can find some results in our accepted FL papers (i.e., FedALA, FedCP, GPFL, and DBE) listed as follows that also use this library. Please note that this developing project may not be able to reproduce the results on these papers, since some basic settings may change due to the requests of the community. For example, we previously set shuffle=False in clientbase.py

@inproceedings{zhang2023fedala,
  title={Fedala: Adaptive local aggregation for personalized federated learning},
  author={Zhang, Jianqing and Hua, Yang and Wang, Hao and Song, Tao and Xue, Zhengui and Ma, Ruhui and Guan, Haibing},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={9},
  pages={11237--11244},
  year={2023}
}

@inproceedings{Zhang2023fedcp,
  author = {Zhang, Jianqing and Hua, Yang and Wang, Hao and Song, Tao and Xue, Zhengui and Ma, Ruhui and Guan, Haibing},
  title = {FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy},
  year = {2023},
  booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}
}

@inproceedings{zhang2023gpfl,
  title={GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning},
  author={Zhang, Jianqing and Hua, Yang and Wang, Hao and Song, Tao and Xue, Zhengui and Ma, Ruhui and Cao, Jian and Guan, Haibing},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={5041--5051},
  year={2023}
}

@inproceedings{
  zhang2023eliminating,
  title={Eliminating Domain Bias for Federated Learning in Representation Space},
  author={Jianqing Zhang and Yang Hua and Jian Cao and Hao Wang and Tao Song and Zhengui XUE and Ruhui Ma and Haibing Guan},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023},
  url={https://openreview.net/forum?id=nO5i1XdUS0}
}

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dataset		dataset
system		system
.gitattributes		.gitattributes
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
env_cuda_latest.yaml		env_cuda_latest.yaml
structure.png		structure.png

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PFLlib: Personalized Federated Learning Algorithm Library

Algorithms with code (updating)

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Personalized FL (pFL)

Datasets and scenarios (updating)

Examples for MNIST

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Easy to extend

Experimental results

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PFLlib: Personalized Federated Learning Algorithm Library

Algorithms with code (updating)

Traditional FL (tFL)

Personalized FL (pFL)

Datasets and scenarios (updating)

Examples for MNIST

Models

Environments

How to start simulating (examples for FedAvg)

Practical situations

Easy to extend

Experimental results

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