Federated Representation Learning in the Under-Parameterized Regime (ICML 2024)

This repository contains the code to reproduce the results for the proposed method FLUTE as presented in our paper:

Exploiting Shared Representations for Personalized Federated Learning by Renpu Liu, Cong Shen, and Jing Yang.

This paper has been accepted at ICML 2024.

Usage

To run FLUTE, use the following command template:

python main_flute.py --alg flute --dataset [dataset] --num_users [num_users] --model [model] --shard_per_user [shard_per_user] --frac [frac] --local_bs [local_bs] --lr [lr] --epochs [epochs] --local_ep [local_ep] --local_rep_ep [local_rep_ep] --server_update [server_update]

Example Command

python main_flute.py --alg flute --dataset cifar10 --num_classes 10 --num_users 100 --model cnn --shard_per_user 5 --frac 0.1 --local_bs 10 --lr 0.01 --epochs 100 --local_ep 10 --local_rep_ep 1 --server_update 1

Citation

If you use our implementation, please cite our paper:

@misc{liu2024federated,
      title={Federated Representation Learning in the Under-Parameterized Regime}, 
      author={Renpu Liu and Cong Shen and Jing Yang},
      year={2024},
      eprint={2406.04596},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Acknowledgements

Our code is based on Collins' work, available at FedRep GitHub Repository.