Achieving fairness while preserving privacy in medical imaging tasks remains a significant challenge. Here, the authors present and comprehensively evaluate a federated learning framework to tackle both fairness and privacy issues, using a flexible regularization term to integrate multiple fairness criteria.
- Huijun Xing
- Rui Sun
- Zhen Li