This is the official PyTorch implementation for the paper: "FedNoRo: Towards Noise-Robust Federated Learning By Addressing Class Imbalance and Label Noise Heterogeneity", which is accepted at IJCAI'23 main track.
This paper proposes a federated noisy label learning framework for class-imbalanced and heterogeneous multi-source medical data.
Please download the ICH dataset from kaggle and preprocess it follow this notebook. Please download the ISIC 2019 dataset from this link. Data partition can be found in the paper.
Update (Mar. 2024): You may get the ICH dataset here.
We recommend using conda to setup the environment. See the requirements.txt for environment configuration.
- FedAvg [paper]
- FedProx [paper]
- FedLA (Logit Adjustment) [paper]
- RoFL [paper] [code]
- RHFL [paper] [code]
- FedLSR [paper] [code]
- FedCorr [paper] [code]
If this repository is useful for your research, please consider citing:
@inproceedings{wu2023fednoro,
title = {FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity},
author = {Wu, Nannan and Yu, Li and Jiang, Xuefeng and Cheng, Kwang-Ting and Yan, Zengqiang},
booktitle = {Proceedings of the Thirty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-23}},
pages = {4424--4432},
year = {2023},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2023/492},
url = {https://doi.org/10.24963/ijcai.2023/492},
}For any questions, please contact 'wnn2000@hust.edu.cn'.