3 papers
cs.LG2025
FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis
Nannan Wu, Zengqiang Yan, Nong Sang +2
Training a model that effectively handles both common and rare data-i.e., achieving performance fairness-is crucial in federated learning (FL). While existing fair FL methods have…
eess.IV2025
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching
Nannan Wu, Zhuo Kuang, Zengqiang Yan +2
Despite the potential of federated learning in medical applications, inconsistent imaging quality across institutions-stemming from lower-quality data from a minority of clients-bi…
cs.LG2024
From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching
Nannan Wu, Zhuo Kuang, Zengqiang Yan +1
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…