| Summary: | Anomaly detection for the Internet of Things (IoT) is a major intelligent service required by many fields, including intrusion detection, state monitoring, device-activity analysis, and security supervision. However, the heterogeneous distribution of data and resource-constrained end nodes in ubiquitous IoT systems 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 by adopting the compound-action actor-critic (CA2C) algorithm for UAV selection, which enables decentralized UAVs to collaboratively train a global anomaly detection model based on their local sensory data from IoT devices. In the VHetNet-enabled AFL framework, the UAV selection process aims to prevent UAVs with low local model quality and large energy consumption from affecting the learning efficiency and model accuracy. Due to the wide coverage as well as strong storage and computation capabilities, a HAPS operates as a central aerial server for aggregating local models of UAVs asynchronously and making decisions intelligently. Moreover, we propose a CA2C-based joint device association, UAV selection, and UAV placement algorithm to further enhance the overall federated execution efficiency and detection model accuracy under UAV energy constraints. Extensive experimental evaluation on real-world datasets demonstrates that the proposed algorithm can achieve high detection accuracy with short federated execution time and low energy consumption.
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