most citedFedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise

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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…