Multi-Level Branched Regularization for Federated Learning

Official implementation of Multi-Level Branched Regularization for Federated Learning
Jinkyu Kim*, Geeho Kim*, Bohyung Han (* equal contribution) in ICML 2022

Paper | Project Page

This repository provides detailed information for reproducing results presented in the paper.
it includes models, hyperparameters, and implementation of client's data generation for {non-iid, iid} distribution.

⚙️ Setup

Dependencies

This repository is implemented based on Pytorch, and the required packages are specified in environment.yaml

Environment

We tested the code on virtual environment with Anaconda on Ubuntu 16.04.
Create the virtual environment by importing the dependencies with the command

conda env create -f environment.yaml -n fedmlb
conda activate fedmlb

💻 Training models from scratch

Non-iid data (Dirichlet 0.3) on CIFAR-100

100 clients, 5% participation, 1000 rounds communication, 5 local epochs, and ResNet18

python3 federated_train.py --cuda_visible_device 0 --method FedMLB --arch=ResNet18_FedMLB  --mode dirichlet --dirichlet_alpha 0.3  --global_epochs 1000 --local_epochs 5  --lr 0.1 --learning_rate_decay 0.998 --weight_decay 1e-3 --seed 0 --set CIFAR100  --workers 8 --participation_rate=0.05 --learning_rate_decay 0.998 --num_of_clients=100   --batch_size=50

🏷️ Citation

If you use our code for your work, please cite our paper as below:

@InProceedings{Kim2022Multi,
    author    = {Kim, Jinkyu and Kim, Geeho and Han, Bohyung},
    title     = {Multi-Level Branched Regularization for Federated Learning},
    booktitle = {International Conference on Machine Learning},
    year      = {2022},
    organization={PMLR}
}

Acknowledgement

Part of our code is borrowed from Federated-Learning-PyTorch