Momentum-Guided Federated Split Distillation for Personalized Temporal Edge Intelligence

Abstract:We propose a momentum-guided federated split distillation framework for personalized, efficient, and autonomous temporal edge intelligence. We introduce TeRR-SAtt, our novel temporal reservoir student attention design that combines fixed reservoir representations, a lightweight temporal student, and personalized output modules. We also present AMGF, our anticipatory momentum-guided fusion mechanism that clusters clients through learning momentum and derives specialized teacher updates. On real-world smart-building data, TeRR-SAtt reduces edge training latency by 65.50%, inference latency by 44.70%, training memory usage by 18.40%, and inference CPU usage by 33.10% over the considered baselines. At the same time, AMGF improves local learning by up to 35.31% in RMSE compared to global updates.

Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)

arXiv:2609.31159 [cs.AI]

(or arXiv:2609.31159v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.31159 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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