Cross-Domain Federated Computation Offloading for Age of Information Minimization in Satellite-Airborne-Terrestrial Networks
X. Xia, Haitham H. Esmat, K. Dyer, Beatriz Lorenzo, Linke Guo · 2023
Satellite-Airborne-Terrestrial Networks (SATNs) are expected to provide communication and edge-computing services for a plethora of IoT applications. However, preserving the freshness of information is challenging since it requires timely data collection, bandwidth, and offloading decisions across different administrative domains. In this paper, we aim to optimize the age of information (AoI) and energy consumption tradeoff when serving multiple traffic classes in SATNs. A cross-domain federated computation offloading algorithm (Fed-SATEC-Off) is presented in which different service providers (SPs) collaborate to allocate the bandwidth while unmanned aerial vehicles (UAVs) and satellites make decisions to collect, relay, and offload the computing tasks. Given the requirements of each traffic class, the optimum collaborative strategies between SPs, UAVs, and satellites are obtained together with the computation offloading topology. Our algorithm is based on multi-agent actor-critic and incorporates federated learning to improve the convergence of the learning process. The numerical results show that Fed-SATEC-Off reduces the AoI by factor 4 and achieves faster convergence than existing approaches.