yuetan031 / FedPCL

[NeurIPS'22 Spotlight] Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
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Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Implementation of the paper accepted by NeurIPS 2022 Spotlight: Federated Learning from Pre-Trained Models: A Contrastive Learning Approach.

Requirments

This code requires the following:

Data Preparation

Pre-Trained Models Preparation

Running examples

Options

The default values for various paramters parsed to the experiment are given in options.py. Details about some of those parameters are given here.

Citation

If you find this project helpful, please consider to cite the following paper:

@inproceedings{tan2022federated,
  title={Federated Learning from Pre-Trained Models: A Contrastive Learning Approach},
  author={Tan, Yue and Long, Guodong and Ma, Jie and Liu, Lu and Zhou, Tianyi and Jiang, Jing},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2022}
}