Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning

arXiv:2401.15541v2 Announce Type: replace-cross
Abstract: In the ambitious realm of space AI, the integration of federated learning (FL) with low Earth orbit (LEO) satellite constellations holds immense promise. However, many challenges persist in terms of feasibility, learning efficiency, and convergence. These hurdles stem from the bottleneck in communication, characterized by sporadic and irregular connectivity between LEO satellites and ground stations, coupled with the limited computation capability of satellite edge computing (SEC). This paper proposes a novel FL-SEC framework that empowers …

abstract arxiv challenges communication convergence cs.dc cs.lg earth edge efficiency federated learning however integration low low earth orbit realm satellite satellites space stem stitching terms the edge type