  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.
