This is the code repository of paper FedCSS: Joint Client-and-Sample Selection for Hard Sample-Aware Noise-Robust Federated Learning.
For example, when FedCSS with a selection ratio of 0.4 is used for training on MNIST dataset with 10 clients, 5 of which are corrupted with a corruption ratio of 0.6,
python main.py --dataset_name=mnist \
--select_client \
--client_num 10 \
--select_ratio 0.4 \
--corrupt_num=5 \
--train_type=meta \
--test_name=test \
--corruption_prob=0.6 \
--epochs=100 \
--batch_size=100 \
--lr=1e-2 \
--momentum=0.5
If you find our library helpful in your research, please consider citing it:
@article{li2023fedcss,
title={FedCSS: Joint Client-and-Sample Selection for Hard Sample-Aware Noise-Robust Federated Learning},
author={Li, Anran and Cao, Yue and Guo, Jiabao and Peng, Hongyi and Guo, Qing and Yu, Han},
journal={Proceedings of the ACM on Management of Data},
volume={1},
number={3},
pages={1--24},
year={2023},
publisher={ACM New York, NY, USA}
}