AI RESEARCH
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
arXiv CS.AI
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ArXi:2605.07211v1 Announce Type: cross Mobile devices face diverse resource constraints and non-IID data class distributions, requiring fast on-device inference for local in-distribution (ID) classes and on-demand remote for client-specific out-of-distribution (OOD) classes. Hybrid split federated learning (Hybrid SFL) couples personalized client-side front ends (ing early exit) with a generalized server-side backend for fallback inference, balancing accuracy and cost.