为异构边缘设备定制节能模型，自动适配不同资源约束。
OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligence
- 服务器端统一搜索架构，边缘侧按需生成个性化模型。
- 在满足设备能量、计算和内存限制下，实现高效能模型部署。
- 支持多服务协同优化，兼顾全局知识与本地资源效率。
我们提出 OrchNAS，一个面向个性化联邦边缘智能的节能框架，利用神经架构搜索服务（NAS）在异构边缘环境中自动生成适配服务的模型。该框架在服务器端协调架构搜索过程，使边缘服务能够在设备级能量、计算和内存约束下，推导出个性化模型。我们设计了一种能量感知的全局架构搜索机制，学习跨异构服务的紧凑全局表示；开发了能量高效的架构选择机制，通过渐进式、贪婪的能量感知剪枝策略，使每个服务可生成满足资源约束的子网；提出一种能量高效的个性化模型优化方案，在保留全局表示的同时更新服务自适应参数，并采用原始-对偶优化机制在架构适配过程中严格遵守能量预算。在真实世界和基准数据集上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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