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Cheng Fang

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#machine learning Preprint Jan 2026

Federated Personalization of Early-Exit Networks

X-FED is proposed, a novel Conflict-Aware Cross-Client Federated Exit Distillation framework that jointly addresses both client- and depth-wise conflicts while extending PFL to early-exit networks and introduces a client-decoupled formulation that reduces communication overhead with theoretical soundness.

Boyi Liu, Zimu Zhou, Cheng Fang et al. · 0 citations