This repository is an official PyTorch implementation of the paper "Personalized Retrogress-Resilient Federated Learning Towards Imbalanced Medical Data" [paper] from IEEE Transactions on Medical Imaging (TMI) 2022.

The dermoscopic FL dataset can be downloaded from Google Drive. Put the downloaded clientA, clientB, clientC and clientD subfolders in a newly-built folder ./data/.

• Python 3.7
• PyTorch >= 1.7.0
• numpy 1.19.4
• scikit-learn 0.24.2
• scipy 1.6.2
• albumentations 0.5.2

Clone this repository into any place you want.

git clone https://github.com/CityU-AIM-Group/PRR-Imbalance.git
cd PRR-Imbalance
mkdir data

• Train the PRR-Imbalance with default settings:

python ./main.py --theme prr-imbalance --iters 50 --wk_iters 5 --network vgg_nb --l_rate 0.7 --lr 1e-2 

If you find our work useful in your research or publication, please cite our work:

@ARTICLE{2022personalizedFL,
  title={Personalized Retrogress-Resilient Federated Learning Towards Imbalanced Medical Data}, 
  author={Chen, Zhen and Yang, Chen and Zhu, Meilu and Peng, Zhe and Yuan, Yixuan},
  journal={IEEE Transactions on Medical Imaging}, 
  year={2022},
  pages={1-1},
  doi={10.1109/TMI.2022.3192483}
}