  Driven by the ever-increasing penetration and proliferation of data-driven
applications, a new generation of wireless communication, the sixth-generation
(6G) mobile system enhanced by artificial intelligence (AI), has attracted
substantial research interests. Among various candidate technologies of 6G, low
earth orbit (LEO) satellites have appealing characteristics of ubiquitous
wireless access. However, the costs of satellite communication (SatCom) are
still high, relative to counterparts of ground mobile networks. To support
massively interconnected devices with intelligent adaptive learning and reduce
expensive traffic in SatCom, we propose federated learning (FL) in LEO-based
satellite communication networks. We first review the state-of-the-art
LEO-based SatCom and related machine learning (ML) techniques, and then analyze
four possible ways of combining ML with satellite networks. The learning
performance of the proposed strategies is evaluated by simulation and results
reveal that FL-based computing networks improve the performance of
communication overheads and latency. Finally, we discuss future research topics
along this research direction.
