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

AI 中文解读

OrchNAS来了！这项研究让AI模型能像定制西装一样，根据每个设备的“体型”和“预算”自动设计最合适的“大脑”，既省电又高效。简单来说，现在的AI模型往往要求强大的计算能力和充足电量，但手机、智能家居、物联网设备千差万别。OrchNAS就像一个聪明的设计师，先在云端学习通用的AI知识，然后针对每个设备的具体条件——比如电池容量、内存大小、处理器性能——进行“智能剪裁”，去掉不必要的部分，保留最核心的能力。这样一来，手机上的语音助手能更省电地工作，智能摄像头能更快速地进行图像识别，甚至老旧设备也能流畅运行新的AI功能。这项技术对普通人最大的影响是：未来你的手机、手表、家居设备会变得更聪明、更省电，AI应用不再只是高端手机的专利，它能让更多设备享受到智能服务，同时延长电池寿命，提升用户体验。

arXiv:2607.22805v1 Announce Type: new 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.