  The advances in satellite technology developments have recently seen a large
number of small satellites being launched into space on Low Earth orbit (LEO)
to collect massive data such as Earth observational imagery. The traditional
way which downloads such data to a ground station (GS) to train a machine
learning (ML) model is not desirable due to the bandwidth limitation and
intermittent connectivity between LEO satellites and the GS. Satellite edge
computing (SEC), on the other hand, allows each satellite to train an ML model
onboard and uploads only the model to the GS which appears to be a promising
concept. This paper proposes FedLEO, a novel federated learning (FL) framework
that realizes the concept of SEC and overcomes the limitation (slow
convergence) of existing FL-based solutions. FedLEO (1) augments the
conventional FL's star topology with ``horizontal'' intra-plane communication
pathways in which model propagation among satellites takes place; (2) optimally
schedules communication between ``sink'' satellites and the GS by exploiting
the predictability of satellite orbiting patterns. We evaluate FedLEO
extensively and benchmark it with the state of the art. Our results show that
FedLEO drastically expedites FL convergence, without sacrificing -- in fact it
considerably increases -- the model accuracy.
