From Classifier Heads to Encoder Prototypes: Personalized Federated Learning with Head-Derived Clustering
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. Client-specific heads are never averaged or distributed to oth- er clients; the shared-encoder traject...
| Authors | Amirardalan Dehghanpour, Arian Noori, Ehsan Tanghatari et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 October 2026 |
| Last updated at source | 21 October 2026 |
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