This repository is the official Pytorch implementation DEMO of FedLSR framework in this paper:
Towards Federated Learning against Noisy Labels via Local Self-Regularization (CIKM 2022 full paper track)
We provide a simple yet effective method FedLSR to tackle the distributed label noise issue when the total class number is not high. The slides for the oral presentation link is here.
Note: For experiments on clothing1M, it is suggested to raise the learning rate to 0.1 for FedLSR's implementation.
- FedAvg [paper]
- Symmetric Cross Entropy [paper] [code]
- Co-teaching [paper] [code]
- Robust Federated Learning [paper] [code]
- Python: 3.8
- Pytorch: 1.7.1
- torchvision: 0.8.2
- Other dependencies
- Girum & IAMjmj give some valuable comments in the Github Issue part, and I just clarify some missed points of this paper. Please visit there for more information.
@inproceedings{jiang2022towards,
title={Towards federated learning against noisy labels via local self-regularization},
author={Jiang, Xuefeng and Sun, Sheng and Wang, Yuwei and Liu, Min},
booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
pages={862--873},
year={2022}
}