Sparse-Random-Networks-for-Communication-Efficient-Federated-Learning

PyTorch implementation of the FedPM framework by the authors of the ICLR 2023 paper "Sparse Random Networks for Communication-Efficient Federated Learning".

Sparse Random Networks for Communication-Efficient Federated Learning
Berivan Isik, Francesco Pase, Deniz Gunduz, Tsachy Weissman, Michele Zorzi
International Conference on Learning Representations (ICLR), 2023.

Environment setup:

Packages can be found in fedpm.yml.

Training:

Set the params_path in main.py to the the path of the {}.yaml file with the desired model and dataset. The default parameters can be found in the provided {}.yaml files. To train the model, run:

python3 main.py

References

If you find this work useful in your research, please consider citing our paper:

@inproceedings{
isik2023sparse,
title={Sparse Random Networks for Communication-Efficient Federated Learning},
author={Berivan Isik and Francesco Pase and Deniz Gunduz and Tsachy Weissman and Zorzi Michele},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=k1FHgri5y3-}
}