Scheduling for On-Board Federated Learning with Satellite Clusters

arXiv:2402.09105v1 Announce Type: cross
Abstract: Mega-constellations of small satellites have evolved into a source of massive amount of valuable data. To manage this data efficiently, on-board federated learning (FL) enables satellites to train a machine learning (ML) model collaboratively without having to share the raw data. This paper introduces a scheme for scheduling on-board FL for constellations connected with intra-orbit inter-satellite links. The proposed scheme utilizes the predictable visibility pattern between satellites and ground station (GS), both at the individual …

abstract arxiv board cs.dc cs.lg data federated learning machine machine learning massive paper raw satellite satellites scheduling small train type