Hierarchical Federated Learning with Integrated Sensing-Communication-Computation Over Space-Air-Ground Integrated Networks

Federated learning has achieved significant advancements in edge artificial intelligence (AI) by addressing issues related to data privacy and communication overload. Moreover, hierarchical federated learning over space-air-ground integrated networks (FedSAG), which consists of low-Earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and edge devices, aims to provide AI services in sparsely populated regions lacking ground communication infrastructure. However, previous studies have overlooked the essential sensing process required for acquiring training data, potentially compromising training efficiency and model accuracy. In this paper, we propose an integrated sensing-communication-computation (ISCC) enabled FedSAG system, which allows remote edge devices to collect data via wireless sensing and collaboratively train a global model without sharing local data. We then analyse the convergence of the ISCC-enabled FedSAG and formulate two optimization problems. The first aims to minimize sensing variance under energy and time constraints, while the second seeks to reduce transmission energy through optimal route selection between UAVs and LEO satellite. We reformulate the problems to a minimum spanning tree and propose a Chu-Liu-Edmonds algorithm based a two-stage optimization. Simulation results demonstrate that our proposed algorithm significantly enhances convergence performance and reduces energy consumption.