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While network coverage maps continue to expand, many devices located inremote areas remain unconnected to terrestrial communication infrastructures,preventing them from getting access to the associated data-driven services. Inthis paper, we propose a ground-to-satellite cooperative federated learning(FL) methodology to facilitate machine learning service management over remoteregions. Our methodology orchestrates satellite constellations to provide thefollowing key functions during FL: (i) processing data offloaded from grounddevices, (ii) aggregating models within device clusters, and (iii) relayingmodels/data to other satellites via inter-satellite links (ISLs). Due to thelimited coverage time of each satellite over a particular remote area, wefacilitate satellite transmission of trained models and acquired data toneighboring satellites via ISL, so that the incoming satellite can continueconducting FL for the region. We theoretically analyze the convergence behaviorof our algorithm, and develop a training latency minimizer which optimizes oversatellite-specific network resources, including the amount of data to beoffloaded from ground devices to satellites and satellites' computation speeds.Through experiments on three datasets, we show that our methodology cansignificantly speed up the convergence of FL compared with terrestrial-only andother satellite baseline approaches.

Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading