3 papers
cs.CV2025
FNBench: Benchmarking Robust Federated Learning against Noisy Labels
Xuefeng Jiang, Jia Li, Nannan Wu +7
Robustness to label noise within data is a significant challenge in federated learning (FL). From the data-centric perspective, the data quality of distributed datasets can not be…
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…