For any inquiries, please contact Xinyang Lin at 810427220@qq.com
ICML2022 - Federated Learning with Positive and Unlabeled Data
This code has been developed under Python3.7
, PyTorch 1.7.0
and CUDA 11.0
on Red Hat 8.3
.
Train FedPU model. We implemented the dataloader of FedMatch(ICLR 2021) on cifar10, for easier comparison.
FedPU works with FedAvg on non-iid data:
sh train_c10_FedAvg_FedPU_fmloader_noniid.sh
FedPU works with FedProx on non-iid data:
sh train_c10_FedProx_FedPU_fmloader_noniid.sh
Supervised learning experiment can be performed:
sh train_c10_FedAvg_SL_fmloader_noniid.sh
If our work is helpful for your research, please consider citing:
@inproceedings{lin2022federated,
title={Federated Learning with Positive and Unlabeled Data},
author={Lin, Xinyang and Chen, Hanting and Xu, Yixing and Xu, Chao and Gui, Xiaolin and Deng, Yiping and Wang, Yunhe},
booktitle={International Conference on Machine Learning},
pages={13344--13355},
year={2022},
organization={PMLR}
}