AI 中文总结
提出OrchNAS框架，利用神经架构搜索服务为异构边缘环境设计自适应模型。通过能源感知全局搜索机制、节能架构选择机制及个性化模型优化方案，在资源约束下获个性化架构，实验验证了该方法的有效性。
AI 中文摘要
我们提出了OrchNAS，这是一个具有能源感知、个性化的联邦边缘智能框架，它利用神经架构搜索服务为异构边缘环境自动设计服务自适应模型。该框架在服务器端NAS服务上编排架构搜索过程，使边缘服务能够在设备级能源、计算和内存约束下获得个性化架构。我们引入了一种能源感知全局架构搜索机制，以跨异构服务学习紧凑的全局表示。我们开发了一种节能架构选择机制，使每个服务能够通过渐进、贪婪、能源感知的剪枝策略获得满足其资源约束的个性化子网。我们提出了一种节能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，其中原对偶优化机制在架构自适应期间强制执行严格的能源预算。在真实世界和基准数据集上的实验证明了所提方法的有效性。
英文摘要
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.
