FedPPO: Reinforcement Learning-Based Client Selection for Federated Learning With Heterogeneous Data
conda env create -f fedppo_env.yaml
| Paramater | Control | Default Value | Choice |
|---|---|---|---|
| iid & non-iid | --iid |
0 (0 for non-iid) |
0, 1 |
| dataset | --dataset |
cifar10 |
cifar10, cifar100, emnist, imdb |
| traing epoch | --epoch |
100 |
|
| cluser method | --cluster_method |
hier |
hier, gmm, dbscan, kmeans, no |
| device | --gpu_id |
0 |
none for cpu device |
| user number | --num_users |
50 |
25, 50, 100, 200 |
| participation rate of users | --frac |
0.2 |
0.4, 0.2, 0.1, 0.05 |
python ppo_remove_main.py --model=resnet10 --dataset=cifar10 --iid=0 --epochs=100 --level_n_system=0.0 --level_n_lowerb=0.0 --target_acc=0.9 --frac=0.2 --non_iid_prob_class=0.7 --alpha_dirichlet=10 --gpu_id=0
If you find this project helpful, please consider to cite the following paper:
@ARTICLE{10909702,
author={Zhao, Zheyu and Li, Anran and Li, Ruidong and Yang, Lei and Xu, Xiaohua},
journal={IEEE Transactions on Cognitive Communications and Networking},
title={FedPPO: Reinforcement Learning-Based Client Selection for Federated Learning With Heterogeneous Data},
year={2025},
volume={},
number={},
pages={1-1},
keywords={Noise measurement;Training;Data models;Accuracy;Noise;Adaptation models;Computational modeling;Servers;Distributed databases;Convergence;Reinforcement Learning;Federated Learning;Heterogeneous data},
doi={10.1109/TCCN.2025.3547751}}
