Scheduling for On-Board Federated Learning with Satellite Clusters

TL;DR

This paper proposes a scheduling scheme for on-board federated learning in satellite constellations, leveraging predictable satellite-ground visibility to optimize training and global updates, resulting in faster and more accurate model training.

Contribution

It introduces a novel scheduling framework utilizing intra-orbit and inter-orbit coordination based on satellite visibility patterns for efficient federated learning.

Findings

Improved test accuracy achieved faster.

Effective handling of intermittent connectivity.

Optimized scheduling enhances training efficiency.

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 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.