Cooperative Federated Learning over Hybrid Terrestrial and Non-Terrestrial Networks

While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting access to the associated data-driven services. In this paper, we propose a cooperative ground-to-satellite federated learning (FL) methodology to facilitate machine learning service management over remote regions. Our methodology orchestrates satellite constellations to provide the following key functions during FL: (i) processing data offloaded from ground devices, (ii) aggregating models within device clusters, and (iii) relaying models/data to other satellites via inter-satellite links (ISLs). Due to the limited coverage time of each satellite over a particular remote area, we facilitate satellite transmission of trained models and acquired data to neighboring satellites via ISL, so that the incoming satellite can continue FL for the region. We also develop a training latency minimizer which optimizes over the amount of data to be offloaded from ground devices to satellites. Through experiments on benchmark datasets, we show that our scheme can significantly speed up the convergence of FL compared with terrestrial-only and other satellite baseline approaches.