AINeutralarXiv – CS AI · 6h ago6/10
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From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
Researchers propose FedSAF, a new approach to heterogeneous federated learning that shifts from coordinate-based alignment to structural alignment of class prototypes. The method addresses a fundamental limitation in existing prototype-based federated learning systems where forcing diverse client models into a single feature subspace reduces learning capacity, achieving up to 3.52% performance improvement over state-of-the-art methods.