Newest papers

From Classifier Heads to Encoder Prototypes: Personalized Federated Learning with Head-Derived Clustering

Amirardalan Dehghanpour, Arian Noori, Ehsan Tanghatari, Naimeh Omidvar, Hamid Reza Rabiee · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30075 · cited by 0 works (OpenAlex) · open-access PDF

Russian

Мы вводим HDC-FL (Head-Derived Clustering for Personalized Federated Learning), который читает, что решение от сигнала совместного обучения кодировщика уже производит: клиент-специфические классификационные головы. При периодических обновлениях серверы проектируют кешированные головы в стандартизированное пространство с низким рангом описания, группируют описатели, строят один прототип кодировщика по группе от кешированного кодировщика клиента и возвращают каждому клиенту мягкую смесь этих прототипов, упакованных с собственной неизмененной головой. Клиент-специфические головы никогда не подсчитываются или не распределяются между клиентами; траектория совместного кодирования остается нетронутой; и не требуется ника

Machine translation (Meta M2M100); the English original follows.

Abstract

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 trajectory is left intact; and no second optimization trajectory, additional local objective, or post-training fine-tuning stage is required. Across 11 CIFAR-10 and CIFAR-100 configurations covering label-subset, Dirichlet and rotation heterogeneity with ResNet-18 and MobileNetV2 encoders, HDC-FL is competitive with the strongest personalized and clustered baselines in macro-averaged mean accuracy and attains the numerically highest macro-averaged worst-client and bottom-20% accuracy among the evaluated methods. The tail results distinguish methods that look similar when only their means are compared. A controlled donor-swap diagnostic further finds positive donor-ranking associations between head similarity and encoder compatibility in all three settings tested, most clearly under covariate shift.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Classifier (UML), Encoder, Federated learning, Cluster analysis, Cache

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange