Anomaly detection for the Internet of Things (IoT) is a major intelligent service required by many fields, including intrusion detection, device-activity analysis, and security supervision. However, the heterogeneous distribution of data and resource-constrained end nodes present challenges for existing anomaly detection models. Due to the advantages of flexible deployment and multi-dimensional resources, high altitude platform stations (HAPSs) and unmanned aerial vehicles (UAVs), which are important components of vertical heterogeneous networks (VHetNets), have significant potential for sensing, computing, storage, and communication applications in ubiquitous IoT systems. In this paper, we propose a novel VHetNet-enabled asynchronous federated learning (AFL) framework to enable decentralized UAVs to collaboratively train a global anomaly detection model. In the VHetNet-enabled AFL framework, a HAPS operates as a central aerial server, and the local models trained in UAVs are uploaded to the HAPS for global aggregation due to its wide coverage and strong storage and computation capabilities. We introduce a UAV selection strategy into the AFL framework to prevent UAVs with low local model quality and large energy consumption from affecting the learning efficiency and detection accuracy of the global model. To ensure the security of transmissions between UAVs and the HAPS, we add designed noise to local model parameters in UAVs to achieve differential privacy. Moreover, we propose a compound-action actor-critic (CA2C)-based joint device association, UAV selection, and UAV trajectory planning algorithm to further enhance the overall federated execution efficiency and detection model accuracy. Extensive experimental evaluation on a real-world dataset demonstrates that the proposed algorithm can achieve high detection accuracy with short federated execution time and low energy consumption.

翻译：物联网（IoT）异常检测是许多领域所需的主要智能服务，包括入侵检测、设备活动分析和安全监管。然而，数据的异构分布和资源受限的终端节点对现有异常检测模型提出了挑战。由于高空气球站（HAPS）和无人机（UAV）作为垂直异构网络（VHetNets）的重要组成部分，具有灵活部署和多维资源的优势，在泛在物联网系统的感知、计算、存储和通信应用中潜力巨大。本文提出了一种新颖的基于VHetNet的异步联邦学习（AFL）框架，使分散的无人机能够协作训练全局异常检测模型。在基于VHetNet的AFL框架中，HAPS作为中央空中服务器运行，由于具有广覆盖和强大的存储计算能力，无人机训练的本地模型被上传至HAPS进行全局聚合。我们在AFL框架中引入无人机选择策略，以防止本地模型质量低和能耗大的无人机影响全局模型的学习效率和检测精度。为确保无人机与HAPS之间传输的安全性，我们在无人机本地模型参数中添加设计噪声以实现差分隐私。此外，我们提出了一种基于复合动作-评论家（CA2C）的联合设备关联、无人机选择和无人机轨迹规划算法，以进一步提升整体联邦执行效率和检测模型精度。在真实数据集上的广泛实验评估表明，所提算法能够在较短的联邦执行时间和低能耗下实现高检测精度。