Scheduling for On-Board Federated Learning with Satellite Clusters
Nasrin Razmi Bho Matthiesen Armin Dekorsy Petar Popovski
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
satellite level and cumulatively within the entire orbit, to mitigate
intermittent connectivity and best use of available time. To this end, two
distinct schedulers are employed: one for coordinating the FL procedures among
orbits, and the other for controlling those within each orbit. These two
schedulers cooperatively determine the appropriate time to perform global
updates in GS and then allocate suitable duration to satellites within each
orbit for local training, proportional to usable time until next global update.
This scheme leads to improved test accuracy within a shorter time.