OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligence

Abstract:We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptive models for heterogeneous edge environments. The framework orchestrates the architecture search process on a server-side NAS service, enabling edge services to derive personalised architectures under device-level energy, computation, and memory constraints. We introduce an energy-aware global architecture search mechanism that learns a compact global representation across heterogeneous services. We develop an energy-efficient architecture selection mechanism that enables each service to derive a personalised subnet that satisfies its resource constraints via a progressive, greedy, energy-aware pruning strategy. We propose an energy-efficient personalised model optimisation scheme that updates service-adaptive parameters while preserving global representations, where a primal-dual optimisation mechanism enforces strict energy budgets during architecture adaptation. Experiments on real-world and benchmark datasets demonstrate the effectiveness of the proposed approach.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.22805 [cs.LG]

(or arXiv:2607.22805v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.22805 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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