From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching

arXiv:2404.17805v1 Announce Type: new
Abstract: Due to escalating privacy concerns, federated learning has been recognized as a vital approach for training deep neural networks with decentralized medical data. In practice, it is challenging to ensure consistent imaging quality across various institutions, often attributed to equipment malfunctions affecting a minority of clients. This imbalance in image quality can cause the federated model to develop an inherent bias towards higher-quality images, thus posing a severe fairness issue. In this study, we pioneer …

arxiv client cs.cv cs.lg fair federated learning optimization quality shift type via