FedRL

This is the code for paper: A Reinforcement Learning Federated Recommender System for Efficient Communication Using Reinforcement Selector and Hypernet Generator.

Citation

Please cite our paper if you find this code useful for your research. If you have any questions, you can contact us at the email address dycwq123@gmail.com.

@article{DBLP: journals/tors/FedRL24,
  author       = {Yicheng Di, Hongjian Shi, Ruhui Ma, 
                  Honghao Gao, Yuan Liu and Weiyu Wang},
  title        = {FedRL: A Reinforcement Learning
                  Federated Recommender System for 
                  Efficient Communication Using
                  Reinforcement Selector and 
                  Hypernet Generator},
  journal      = {XXX},
  volume       = {X},
  number       = {X},
  pages        = {XXX},
  year         = {2024},
  url          = {XXX},
  doi          = {XXX},
}

Get Data

Please find the data on Google Drive

Dependencies

At least 10GB GPU memory. At least 32GB memory.

• --tensorflow=2.3.0

• --numpy=1.16.0

• --python=3.7

• --keras=2.4.3

• --matplotlib=2.2.3