AFL-CS
Code implementation of the paper "AFL-CS: Asynchronous Federated Learning with Cosine Similarity-based Penalty Term and Aggregation" published in IEEE ICPADS 2023.
How to run
Install the required packages:
conda create -n aflcs python=3.8
conda activate aflcs
conda install mpi4py
conda install pytorch==1.12.1 torchvision==0.13.1 -c pytorch
pip install -r requirements.txt
python utils.py # download the datasets
Example:
bash scripts/run_mnist.sh
bash scripts/run_fashionmnist.sh
Citation
If you use this code for your research, please cite our paper:
@inproceedings{yan2023afl,
title={AFL-CS: Asynchronous Federated Learning with Cosine Similarity-based Penalty Term and Aggregation},
author={Yan, Bingjie and Jiang, Xinlong and Chen, Yiqiang and Gao, Chenlong and Liu, Xuequn},
booktitle={2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)},
pages={46--53},
year={2023},
organization={IEEE}
}