FedRAP

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This project is the code and the supplementary of "Federated Recommendation with Additive Personalization"

Notice that FedRAP is highly sensitive to the Parameter Combinations, which may result in significant differences in performance!

Requirements

1. The code is implemented with Python >= 3.8 and torch~=1.13.1+cu117;
2. Other requirements can be installed by pip install -r requirements.txt.

Quick Start

1. First create two folders: ./logs and ./results;

2. Put datasets into the path [parent_folder]/datasets/;

3. python train.py --alias FedRAP --dataset movielens --data_file ml-100k.dat \
       --mu 1e-3 --l2_regularization 1e-6 --lr_network 1e-4 --lr_args 1e3
   

Citation

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

@inproceedings{
    li2024federated,
    title={Federated Recommendation with Additive Personalization},
    author={Zhiwei Li and Guodong Long and Tianyi Zhou},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=xkXdE81mOK}
}

Contact

• This project is free for academic usage. You can run it at your own risk.
• For any other purposes, please contact Mr. Zhiwei Li ()