发表机构
University of Padova; National Yang-Ming Chiao-Tung University; German Aerospace Center（帕多瓦大学; 国立阳明交通大学; 德国航空航天中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无线IoT网络中集中式信道测量开销大、指纹共享风险高的问题，提出边缘原生联邦学习框架及个性化算法FedSwitch，在损失停滞时自动切换本地微调，提升区域分类精度，并给出理论保证与实验验证。
AI 中文摘要
无线物联网（IoT）网络可以利用基站（BSs）本地观测到的信道状态信息（CSI）来执行区域分类，即推断发射机（TX）的起源区域，从而实现基于信道的认证。然而，集中化这些测量会带来大量的通信开销，并需要共享依赖于位置的无线电指纹。为解决这些困难，我们提出了一种边缘原生的联邦学习（FL）框架，其中基站通过参数服务器（PS）协作训练一个共享分类器，而不暴露原始数据，从而支持如欺骗检测等物理层安全任务。为克服由不同基站位置和传播条件引起的统计异质性，我们引入了FedSwitch，一种个性化联邦学习算法，该算法在损失停滞时自动将边缘节点从协作训练过渡到本地微调。我们为所提出的协作训练阶段提供了理论收敛保证，并推导了将分类错误与统计差异联系起来的分析界限。对合成信道测量的大量评估表明，FedSwitch在区域分类准确性上显著优于标准联邦学习基线。
英文摘要
Wireless Internet-of-Things (IoT) networks can leverage locally observed channel state information (CSI) at base-stations (BSs) to perform region classification, i.e., infer the region of origin of a transmitter (TX), thereby enabling channel-based authentication. However, centralizing these measurements incurs substantial communication overhead and requires sharing location-dependent radio fingerprints. To address these difficulties, we propose an edge-native federated learning (FL) framework where BSs collaboratively train a shared classifier via a parameter server (PS) without exposing raw data, thereby supporting physical-layer security tasks like spoofing detection. To overcome the statistical heterogeneity arising from diverse BS locations and propagation conditions, we introduce FedSwitch, a personalized FL algorithm that automatically transitions edge nodes from collaborative training to local fine-tuning upon loss stagnation. We provide theoretical convergence guarantees for the proposed collaborative training phase and derive analytical bounds linking classification errors to statistical discrepancies. Extensive evaluations on synthetic channel measurements demonstrate that FedSwitch significantly improves region-classification accuracy over standard FL baselines.
