thu-syh / fl-bench

Benchmark of federated learning, dedicated to the community.

Federated Learning Benchmark

Method

Regular FL Methods

• FedAvg -- Communication-Efficient Learning of Deep Networks from Decentralized Data (AISTATS'17)

• FedAvgM -- Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification (ArXiv'19)

• FedProx -- Federated Optimization in Heterogeneous Networks (MLSys'20)

• SCAFFOLD -- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning (ICML'20)

• MOON -- Model-Contrastive Federated Learning (CVPR'21)

• FedDyn -- Federated Learning Based on Dynamic Regularization (ICLR'21)

• FedLC -- Federated Learning with Label Distribution Skew via Logits Calibration (ICML'22)

Personalized FL Methods

• Local-Only -- Local training only (without communication).

• FedMD -- FedMD: Heterogenous Federated Learning via Model Distillation (NIPS'19)

• APFL -- Adaptive Personalized Federated Learning (ArXiv'20)

• LG-FedAvg -- Think Locally, Act Globally: Federated Learning with Local and Global Representations (ArXiv'20)

• FedBN -- FedBN: Federated Learning On Non-IID Features Via Local Batch Normalization (ICLR'21)

• FedPer -- Federated Learning with Personalization Layers (AISTATS'20)

• FedRep -- Exploiting Shared Representations for Personalized Federated Learning (ICML'21)

• Per-FedAvg -- Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach (NIPS'20)

• pFedMe -- Personalized Federated Learning with Moreau Envelopes (NIPS'20)

• Ditto -- Ditto: Fair and Robust Federated Learning Through Personalization (ICML'21)

• pFedHN -- Personalized Federated Learning using Hypernetworks (ICML'21)

• pFedLA -- Layer-Wised Model Aggregation for Personalized Federated Learning (CVPR'22)

• CFL -- Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints (ArXiv'19)

• FedFomo -- Personalized Federated Learning with First Order Model Optimization (ICLR'21)

• FedBabu -- FedBabu: Towards Enhanced Representation for Federated Image Classification (ICLR'22)

• FedAP -- Personalized Federated Learning with Adaptive Batchnorm for Healthcare (IEEE'22)

• kNN-Per -- Personalized Federated Learning through Local Memorization (ICML'22)

More reproductions/features would come soon or later (depends on my mood 🤣).

Easy Run

e.g.

# partition the CIFAR-10 according to Dir(0.1) for 100 clients
cd data/utils
python run.py -d cifar10 -a 0.1 -cn 100
cd ../../

# run FedAvg under default setting.
cd src/server
python fedavg.py

About methods of generating federated dastaset, go check data/README.md for full details.

Monitor

1. Run python -m visdom.server on terminal.
2. Run src/server/${algo}.py --visible 1
3. Go check localhost:8097 on your browser.

Arguments

📢 All arguments have default value.

About the default values and hyperparameters of advanced FL methods, go check src/config/args.py for full details.

General Argument	Description
--dataset, -d	The name of dataset that experiment run on.
--model, -m	The model backbone experiment used.
--seed	Random seed for running experiment.
--join_ratio, -jr	Ratio for (client each round) / (client num in total).
--global_epoch, -ge	Global epoch, also called communication round.
--local_epoch, -le	Local epoch for client local training.
--finetune_epoch, -fe	Epoch for clients fine-tunning their models before test.
--test_gap, -tg	Interval round of performing test on clients.
--eval_test, -ee	Non-zero value for performing evaluation on joined clients' testset before and after local training.
--eval_train, -er	Non-zero value for performing evaluation on joined clients' trainset before and after local training.
--local_lr, -lr	Learning rate for client local training.
--momentum, -mom	Momentum for client local opitimizer.
--weight_decay, -wd	Weight decay for client local optimizer.
--verbose_gap, -vg	Interval round of displaying clients training performance on terminal.
--batch_size, -bs	Data batch size for client local training.
--server_cuda	Non-zero value indicates that tensors at server side are in gpu.
--client_cuda	Non-zero value indicates that tensors at client side are in gpu.
--visible	Non-zero value for using Visdom to monitor algorithm performance on localhost:8097.
--save_log	Non-zero value for saving algorithm running log in FL-bench/out/{$algo}.
--save_model	Non-zero value for saving output model(s) parameters in FL-bench/out/{$algo}.
--save_fig	Non-zero value for saving the accuracy curves showed on Visdom into a .jpeg file at FL-bench/out/{$algo}.
--save_metrics	Non-zero value for saving metrics stats into a .csv file at FL-bench/out/{$algo}.

Supported Datasets

🤗 This benchmark only support algorithms to solve image classification task for now.

Regular Image Datasets

• MNIST (1 x 28 x 28, 10 classes)

• CIFAR-10/100 (3 x 32 x 32, 10/100 classes)

• EMNIST (1 x 28 x 28, 62 classes)

• FashionMNIST (1 x 28 x 28, 10 classes)

• Syhthetic Dataset

• FEMNIST (1 x 28 x 28, 62 classes)

• CelebA (3 x 218 x 178, 2 classes)

• SVHN (3 x 32 x 32, 10 classes)

• USPS (1 x 16 x 16, 10 classes)

• Tiny-ImageNet-200 (3 x 64 x 64, 200 classes)

• CINIC-10 (3 x 32 x 32, 10 classes)

Medical Image Datasets

• COVID-19 (3 x 244 x 224, 4 classes)

• Organ-S/A/CMNIST (1 x 28 x 28, 11 classes)

Acknowledgement

Some reproductions in this benchmark are referred to https://github.com/TsingZ0/PFL-Non-IID, which is a great FL benchmark. 👍

This benchmark is still young, which means I will update it frequently and unpredictably. Therefore, periodically fetching the latest code is recommended. 🤖

If this benchmark is helpful to your research, it's my pleasure. 😏