Code accompanying the paper "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing"

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Last update: Dec 6, 2022

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Deep Learning ProxyFL

Overview

ProxyFL

Code accompanying the paper "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing"

Authors: Shivam Kalra*, Junfeng Wen*, Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh†

* Denotes equal contribution
† University of Waterloo / Vector Institute

Prerequisite

Python 3.9

conda create -n ProxyFL python=3.9
conda activate ProxyFL

PyTorch 1.9.0

conda install pytorch=1.9.0 torchvision=0.10.0 numpy=1.21.2 -c pytorch

mpi4py 3.1.2

conda install -c conda-forge mpi4py=3.1.2

opacus 0.14.0

pip install 'opacus==0.14.0'

matplotlib 3.4.3

conda install -c conda-forge matplotlib=3.4.3

Run experiment

Download data via

bash download_data.sh

Then run the script

bash run_exp.sh

Citation

If you find this code useful in your research, please cite the following paper:

@inproceedings{kalra2021,
  title={ProxyFL: Decentralized Federated Learning through Proxy Model Sharing},
  author={Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh},
  year={2021}
}

Comments

The details about algorithms

Thanks for your code! Sorry to bother you. I want to know how the algorithms compare like FedAvg ,FML ,ProxyFL. I can't seem to find the details about them in your code. Thx！I am looking forward to your reply ！

opened by Xinchungeorb 1

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Code accompanying the paper "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing"

Related tags

Overview

ProxyFL

Prerequisite

Run experiment

Citation

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Comments

The details about algorithms

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