On-board Federated Learning for Satellite Clusters with Inter-Satellite Links

TL;DR

This paper introduces a novel federated learning approach for satellite constellations with inter-satellite links, improving convergence speed and reducing communication load through in-network aggregation and system design leveraging satellite predictability.

Contribution

It proposes a new FL framework utilizing intra-orbit ISLs and predictability, with synchronous and asynchronous variants, to enhance data processing efficiency in satellite clusters.

Findings

Sevenfold increase in convergence speed with ISLs

Tenfold reduction in communication load via in-network aggregation

Effective handling of intermittent connectivity in satellite FL

Abstract

The emergence of mega-constellations of interconnected satellites has a major impact on the integration of cellular wireless and non-terrestrial networks, while simultaneously offering previously inconceivable data gathering capabilities. This paper studies the problem of running a federated learning (FL) algorithm within low Earth orbit satellite constellations connected with intra-orbit inter-satellite links (ISL), aiming to efficiently process collected data in situ. Satellites apply on-board machine learning and transmit local parameters to the parameter server (PS). The main contribution is a novel approach to enhance FL in satellite constellations using intra-orbit ISLs. The key idea is to rely on predictability of satellite visits to create a system design in which ISLs mitigate the impact of intermittent connectivity and transmit aggregated parameters to the PS. We first devise a synchronous FL, which is extended towards an asynchronous FL for the case of sparse satellite visits to the PS. An efficient use of the satellite resources is attained by sparsification-based compression the aggregated parameters within each orbit. Performance is evaluated in terms of accuracy and required data transmission size. We observe a sevenfold increase in convergence speed over the state-of-the-art using ISLs, and $10\times$ reduction in communication load through the proposed in-network aggregation strategy.