• 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)

• FedGen -- Data-Free Knowledge Distillation for Heterogeneous Federated Learning (ICML'21)

• pFedSim (My Work⭐) -- pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning (ArXiv'23)

• 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)

• MetaFed -- MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare (IJCAI'22)

📢 Note that FL-bench needs 3.10 <= python < 3.12. I suggest you to checkout your python version before installing packages by pip.

pip install -r requirements.txt

conda env create -f environment.yml

At China mainland

poetry install

Not at China mainland

sed -i "26,30d" pyproject.toml && poetry lock --no-update && poetry install

At China mainland

docker build -t fl-bench .

Not at China mainland

docker build \
-t fl-bench \
--build-arg IMAGE_SOURCE=karhou/ubuntu:basic \
--build-arg CHINA_MAINLAND=false \
.

ALL classes of methods are inherited from FedAvgServer and FedAvgClient. If you wanna figure out the entire workflow and detail of variable settings, go check ./src/server/fedavg.py and ./src/client/fedavg.py.

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

# run FedAvg on CIFAR-10 with default settings.
cd src/server
python fedavg.py -d cifar10

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

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

📢 All generic arguments have their default value. Go check get_fedavg_argparser() in FL-bench/src/server/fedavg.py for full details of generic arguments.

About the default values and hyperparameters of advanced FL methods, go check corresponding FL-bench/src/server/${algo}.py for full details.

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)

• DomainNet (3 x ? x ?, 345 classes)

  • Go check data/README.md for the full process guideline 🧾.

Medical Image Datasets

• COVID-19 (3 x 244 x 224, 4 classes)

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

If you think this project is helpful and willing to buy me a coffee ☕, that would be AWESOME 🥳!

You can sponsor me by WeChat or PayPal.