TsingZ0/PFLlib
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license: GPL-2.0
Language: Python .
37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 23 datasets.
最后发布版本: v0.1.10 ( 2024-11-11 15:39:01)
PFLlib: Personalized Federated Learning Library
🎯We create a beginner-friendly algorithm library and evaluation platform for those new to federated learning. Join us in expanding the FL community by contributing your algorithms, datasets, and metrics to this project.
👏 We will change the license to Apache-2.0 in the next release.
🔥 Three new datasets have been added, two of which address real-world scenarios: (1) tumor tissue patches from breast cancer metastases in lymph node sections sourced from different hospitals, and (2) wildlife photos captured by different camera traps. The third dataset focuses on the label-skew scenario: chest X-ray images from hospitals for COVID-19. These datasets are also compatible with our HtFLlib
Figure 1: An Example for FedAvg. You can create a scenario using generate_DATA.py
and run an algorithm using main.py
, clientNAME.py
, and serverNAME.py
. For a new algorithm, you only need to add new features in clientNAME.py
and serverNAME.py
.
🎯If you find our repository useful, please cite the corresponding paper:
@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}
}
Key Features
-
37 traditional FL (tFL) and personalized FL (pFL) algorithms, 3 scenarios, and 23 datasets.
-
Some experimental results are avalible in its paper and here.
-
Refer to this guide to learn how to use it.
-
The evaluation platform 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.
-
We provide privacy evaluation and systematical research supprot.
-
You can now train on some clients and evaluate performance on new clients by setting
args.num_new_clients
in./system/main.py
. Please note that not all tFL/pFL algorithms support this feature. -
PFLlib primarily focuses on data (statistical) heterogeneity. For algorithms and an evaluation platform that address both data and model heterogeneity, please refer to our extended project Heterogeneous Federated Learning (HtFLlib).
-
As we strive to meet diverse user demands, frequent updates to the project may alter default settings and scenario creation codes, affecting experimental results.
-
Closed issues may help you a lot when errors arise.
-
When submitting pull requests, please provide sufficient instructions and examples in the comment box.
The origin of the data heterogeneity phenomenon is the characteristics of users, who generate non-IID (not Independent and Identically Distributed) and unbalanced data. With data 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 advantage of the statistically heterogeneous data to learn the personalized model for each user.
Algorithms with code (updating)
Traditional FL (tFL)
Basic 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
-
FedLC — Federated Learning With Label Distribution Skew via Logits Calibration ICML 2022
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)
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
-
FedDBE — Eliminating Domain Bias for Federated Learning in Representation Space NeurIPS 2023
-
FedCAC — Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration ICCV 2023
-
PFL-DA — Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D Printing Technometrics 2023
Other pFL
-
FedMTL (not MOCHA) — Federated multi-task learning NeurIPS 2017
-
FedBN — FedBN: Federated Learning on non-IID Features via Local Batch Normalization ICLR 2021
Knowledge-distillation-based pFL (more in HtFLlib)
-
FedDistill (FD) — Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data 2018
-
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)
We support 3 types of scenarios with various datasets and move the common dataset splitting code into ./dataset/utils
for easy extension. If you need another data set, just write another code to download it and then use the utils.
label skew scenario
For the label skew scenario, we introduce 15 famous datasets:
- MNIST
- EMNIST
- FEMNIST
- Fashion-MNIST
- Cifar10
- Cifar100
- AG News
- Sogou News
- Tiny-ImageNet
- Country211
- Flowers102
- GTSRB
- Shakespeare
- Stanford Cars
- COVIDx (chest X-ray images from hospitals for covid-19)
The datasets can be easily split into IID and non-IID versions. In the non-IID scenario, we distinguish between two types of distribution:
-
Pathological non-IID: In this case, each client only holds a subset of the labels, for example, just 2 out of 10 labels from the MNIST dataset, even though the overall dataset contains all 10 labels. This leads to a highly skewed distribution of data across clients.
-
Practical non-IID: Here, we model the data distribution using a Dirichlet distribution, which results in a more realistic and less extreme imbalance. For more details on this, refer to this paper.
Additionally, we offer a balance
option, where data amount is evenly distributed across all clients.
feature shift scenario
For the feature shift scenario, we utilize 3 widely used datasets in Domain Adaptation:
real-world scenario
For the real-world scenario, we introduce 5 naturally separated datasets:
- Camelyon17 (tumor tissue patches extracted from breast cancer metastases in lymph node sections, 5 hospitals, 2 labels)
- iWildCam (194 camera traps, 158 labels)
- Omniglot (20 clients, 50 labels)
- HAR (Human Activity Recognition) (30 clients, 6 labels)
- PAMAP2 (9 clients, 12 labels)
For more details on datasets and FL algorithms in IoT, please refer to FL-IoT.
Examples for MNIST in the label skew scenario
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
# python generate_MNIST.py noniid - exdir # for Extended Dirichlet strategy
The command line output of running python generate_MNIST.py noniid - dir
Number of classes: 10
Client 0 Size of data: 2630 Labels: [0 1 4 5 7 8 9]
Samples of labels: [(0, 140), (1, 890), (4, 1), (5, 319), (7, 29), (8, 1067), (9, 184)]
--------------------------------------------------
Client 1 Size of data: 499 Labels: [0 2 5 6 8 9]
Samples of labels: [(0, 5), (2, 27), (5, 19), (6, 335), (8, 6), (9, 107)]
--------------------------------------------------
Client 2 Size of data: 1630 Labels: [0 3 6 9]
Samples of labels: [(0, 3), (3, 143), (6, 1461), (9, 23)]
--------------------------------------------------
Show more
Client 3 Size of data: 2541 Labels: [0 4 7 8]
Samples of labels: [(0, 155), (4, 1), (7, 2381), (8, 4)]
--------------------------------------------------
Client 4 Size of data: 1917 Labels: [0 1 3 5 6 8 9]
Samples of labels: [(0, 71), (1, 13), (3, 207), (5, 1129), (6, 6), (8, 40), (9, 451)]
--------------------------------------------------
Client 5 Size of data: 6189 Labels: [1 3 4 8 9]
Samples of labels: [(1, 38), (3, 1), (4, 39), (8, 25), (9, 6086)]
--------------------------------------------------
Client 6 Size of data: 1256 Labels: [1 2 3 6 8 9]
Samples of labels: [(1, 873), (2, 176), (3, 46), (6, 42), (8, 13), (9, 106)]
--------------------------------------------------
Client 7 Size of data: 1269 Labels: [1 2 3 5 7 8]
Samples of labels: [(1, 21), (2, 5), (3, 11), (5, 787), (7, 4), (8, 441)]
--------------------------------------------------
Client 8 Size of data: 3600 Labels: [0 1]
Samples of labels: [(0, 1), (1, 3599)]
--------------------------------------------------
Client 9 Size of data: 4006 Labels: [0 1 2 4 6]
Samples of labels: [(0, 633), (1, 1997), (2, 89), (4, 519), (6, 768)]
--------------------------------------------------
Client 10 Size of data: 3116 Labels: [0 1 2 3 4 5]
Samples of labels: [(0, 920), (1, 2), (2, 1450), (3, 513), (4, 134), (5, 97)]
--------------------------------------------------
Client 11 Size of data: 3772 Labels: [2 3 5]
Samples of labels: [(2, 159), (3, 3055), (5, 558)]
--------------------------------------------------
Client 12 Size of data: 3613 Labels: [0 1 2 5]
Samples of labels: [(0, 8), (1, 180), (2, 3277), (5, 148)]
--------------------------------------------------
Client 13 Size of data: 2134 Labels: [1 2 4 5 7]
Samples of labels: [(1, 237), (2, 343), (4, 6), (5, 453), (7, 1095)]
--------------------------------------------------
Client 14 Size of data: 5730 Labels: [5 7]
Samples of labels: [(5, 2719), (7, 3011)]
--------------------------------------------------
Client 15 Size of data: 5448 Labels: [0 3 5 6 7 8]
Samples of labels: [(0, 31), (3, 1785), (5, 16), (6, 4), (7, 756), (8, 2856)]
--------------------------------------------------
Client 16 Size of data: 3628 Labels: [0]
Samples of labels: [(0, 3628)]
--------------------------------------------------
Client 17 Size of data: 5653 Labels: [1 2 3 4 5 7 8]
Samples of labels: [(1, 26), (2, 1463), (3, 1379), (4, 335), (5, 60), (7, 17), (8, 2373)]
--------------------------------------------------
Client 18 Size of data: 5266 Labels: [0 5 6]
Samples of labels: [(0, 998), (5, 8), (6, 4260)]
--------------------------------------------------
Client 19 Size of data: 6103 Labels: [0 1 2 3 4 9]
Samples of labels: [(0, 310), (1, 1), (2, 1), (3, 1), (4, 5789), (9, 1)]
--------------------------------------------------
Total number of samples: 70000
The number of train samples: [1972, 374, 1222, 1905, 1437, 4641, 942, 951, 2700, 3004, 2337, 2829, 2709, 1600, 4297, 4086, 2721, 4239, 3949, 4577]
The number of test samples: [658, 125, 408, 636, 480, 1548, 314, 318, 900, 1002, 779, 943, 904, 534, 1433, 1362, 907, 1414, 1317, 1526]
Saving to disk.
Finish generating dataset.
Models
-
for MNIST and Fashion-MNIST
- Mclr_Logistic(1*28*28) # convex
- LeNet()
- DNN(1*28*28, 100)
-
for Cifar10, Cifar100 and Tiny-ImageNet
- Mclr_Logistic(3*32*32) # convex
- FedAvgCNN()
- DNN(3*32*32, 100)
- 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
- TransformerModel() in Attention is all you need
-
for AmazonReview
-
for Omniglot
- FedAvgCNN()
-
for HAR and PAMAP
Environments
Install CUDA v11.6.
Install conda latest and activate conda.
conda env create -f env_cuda_latest.yaml # You may need to downgrade the torch using pip to match the CUDA version
How to start simulating (examples for FedAvg)
-
Download this project to an appropriate location using git.
git clone https://github.com/TsingZ0/PFLlib.git
-
Create proper environments (see Environments).
-
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
Note: It is preferable to tune algorithm-specific hyper-parameters before using any algorithm on a new machine.
Easy to extend
This library is designed to be easily extendable with new algorithms and datasets. Here’s how you can add them:
-
New Dataset: To add a new dataset, simply create a
generate_DATA.py
file in./dataset
and then write the download code and use the utils as shown in./dataset/generate_MNIST.py
(you can consider it as a template):# `generate_DATA.py` import necessary pkgs from utils import necessary processing funcs def generate_dataset(...): # download dataset as usual # pre-process dataset as usual X, y, statistic = separate_data((dataset_content, dataset_label), ...) train_data, test_data = split_data(X, y) save_file(config_path, train_path, test_path, train_data, test_data, statistic, ...) # call the generate_dataset func
-
New Algorithm: To add a new algorithm, extend the base classes Server and Client, which are defined in
./system/flcore/servers/serverbase.py
and./system/flcore/clients/clientbase.py
, respectively.- Server
# serverNAME.py import necessary pkgs from flcore.clients.clientNAME import clientNAME from flcore.servers.serverbase import Server class NAME(Server): def __init__(self, args, times): super().__init__(args, times) # select slow clients self.set_slow_clients() self.set_clients(clientAVG) def train(self): # server scheduling code of your algorithm
- Client
# clientNAME.py import necessary pkgs from flcore.clients.clientbase import Client class clientNAME(Client): def __init__(self, args, id, train_samples, test_samples, **kwargs): super().__init__(args, id, train_samples, test_samples, **kwargs) # add specific initialization def train(self): # client training code of your algorithm
- Server
-
New Model: To add a new model, simply include it in
./system/flcore/trainmodel/models.py
. -
New Optimizer: If you need a new optimizer for training, add it to
./system/flcore/optimizers/fedoptimizer.py
. -
New Evaluation Platform or Library: The evaluation framework is flexible, allowing users to build custom platforms or libraries for specific applications, such as FL-IoT and HtFLlib.
Privacy Evaluation
You can use the following privacy evaluation methods to assess the privacy-preserving capabilities of tFL/pFL algorithms in PFLlib. Please refer to ./system/flcore/servers/serveravg.py
for an example. Note that most of these evaluations are not typically considered in the original papers. We encourage you to add more attacks and metrics for privacy evaluation.
Currently supported attacks:
Currently supported metrics:
- PSNR (Peak Signal-to-Noise Ratio): an objective metric for image evaluation, defined as the logarithm of the ratio of the squared maximum value of RGB image fluctuations to the Mean Squared Error (MSE) between two images. A lower PSNR score indicates better privacy-preserving capabilities.
Systematical research supprot
To simulate Federated Learning (FL) under practical conditions, such as client dropout, slow trainers, slow senders, and network TTL (Time-To-Live), you can adjust the following parameters:
-
-cdr
: Dropout rate for clients. Clients are randomly dropped at each training round based on this rate. -
-tsr
and-ssr
: Slow trainer and slow sender rates, respectively. These parameters define the proportion of clients that will behave as slow trainers or slow senders. Once a client is selected as a "slow trainer" or "slow sender," it will consistently train/send slower than other clients. -
-tth
: Threshold for network TTL in milliseconds.
Thanks to @Stonesjtu, this library can also record the GPU memory usage for the model.
Experimental Results
If you're interested in experimental results (e.g., accuracy) for the algorithms mentioned above, you can find results in our accepted FL papers, which also utilize this library. These papers include:
Please note that while these results were based on this library, reproducing the exact results may be challenging as some settings might have changed in response to community feedback. For example, in earlier versions, we set shuffle=False
in clientbase.py
.
Here are the relevant papers for your reference:
@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}
}
最近版本更新:(数据更新于 2024-12-11 17:01:43)
2024-11-11 15:39:01 v0.1.10
2024-07-31 14:01:32 v0.1.9
2024-06-07 09:30:46 v0.1.8
2024-03-19 11:39:12 v0.1.7
2023-12-06 22:17:47 v0.1.6
2023-11-21 20:56:54 v0.1.5
2023-10-11 15:19:46 v0.1.4
2023-06-04 15:58:11 v0.1.3
2023-04-19 20:14:42 v0.1.2
2023-04-01 12:47:32 v0.1.1
主题(topics):
attack, differential-privacy, distributed-computing, dlg, domainnet, federated-learning, heterogeneity, imagenet, iot, non-iid, personalization, privacy, privacy-protection, python, pytorch
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