[2603.19722] FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients

Computer Science > Machine Learning arXiv:2603.19722 (cs) [Submitted on 20 Mar 2026] Title:FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients Authors:Tian Wen, Zhiqin Yang, Yonggang Zhang, Xuefeng Jiang, Hao Peng, Yuwei Wang, Bo Han View a PDF of the paper titled FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients, by Tian Wen and Zhiqin Yang and Yonggang Zhang and Xuefeng Jiang and Hao Peng and Yuwei Wang and Bo Han View PDF HTML (experimental) Abstract:Federated learning (FL) suffers from performance degradation due to the inevitable presence of noisy annotations in distributed scenarios. Existing approaches have advanced in distinguishing noisy samples from the dataset for label correction by leveraging loss values. However, noisy samples recognition relying on scalar loss lacks reliability for FL under heterogeneous scenarios. In this paper, we rethink this paradigm from a representation perspective and propose \method~(\textbf{Fed}erated under \textbf{R}epresentation \textbf{G}emometry), which follows \textbf{the principle of ``representation geometry priority''} to recognize noisy labels. Firstly, \method~creates label-agnostic spherical representations by using self-supervision. It then iteratively fits a spherical von Mises-Fisher (vMF) mixture model to this geometry using previously identified clean samples to capture semantic clusters. This geometric evidence is integrated with a semantic-la...