31 citations · 64 across the 20 of their papers we have counts for
5 papers · 1 filter
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…
FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity
Zhaobin Sun, Nannan Wu, Junjie Shi +4
Cross-silo federated learning (FL) enables decentralized organizations to collaboratively train models while preserving data privacy and has made significant progress in medical im…
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…
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise
Nannan Wu, Zhaobin Sun, Zengqiang Yan +1
Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property. However, exist…
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Nannan Wu, Li Yu, Xuefeng Jiang +2
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of cl…