Amirardalan Dehghanpour, Arian Noori, Ehsan Tanghatari, Naimeh Omidvar, Hamid R. Rabiee · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30075 · open-access PDF
Personalized federated learning must decide which clients should share representations. We introduce HDC-FL (Head-Derived Clustering for Personalized Federated Learning), which reads that decision off a signal shared-encoder training already produces: the client-specific classifi- er heads. At periodic refresh rounds the server projects the cached heads into a normalized low-rank descriptor space, groups the descriptors, builds one encoder prototype per group from the cached client encoders, and returns to each client a soft mixture of those prototypes paired with its own unchanged head. Clien
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