Bringing Federated Learning to Space

TL;DR

This paper systematically adapts federated learning algorithms for satellite constellations, demonstrating their scalability and efficiency in space environments, enabling faster on-board model training and improved satellite autonomy.

Contribution

It introduces a comprehensive 'space-ification' framework that modifies terrestrial FL algorithms for orbital constraints and evaluates their performance across diverse satellite configurations.

Findings

Space-adapted FL algorithms scale to 100 satellites.

Multi-month training cycles reduced to days, 9x speedup.

Performance approaches centralized training results.

Abstract

As Low Earth Orbit (LEO) satellite constellations rapidly expand to hundreds and thousands of spacecraft, the need for distributed on-board machine learning becomes critical to address downlink bandwidth limitations. Federated learning (FL) offers a promising framework to conduct collaborative model training across satellite networks. Realizing its benefits in space naturally requires addressing space-specific constraints, from intermittent connectivity to dynamics imposed by orbital motion. This work presents the first systematic feasibility analysis of adapting off-the-shelf FL algorithms for satellite constellation deployment. We introduce a comprehensive "space-ification" framework that adapts terrestrial algorithms (FedAvg, FedProx, FedBuff) to operate under orbital constraints, producing an orbital-ready suite of FL algorithms. We then evaluate these space-ified methods through extensive parameter sweeps across 768 constellation configurations that vary cluster sizes (1-10), satellites per cluster (1-10), and ground station networks (1-13). Our analysis demonstrates that space-adapted FL algorithms efficiently scale to constellations of up to 100 satellites, achieving performance close to the centralized ideal. Multi-month training cycles can be reduced to days, corresponding to a 9x speedup through orbital scheduling and local coordination within satellite clusters. These results provide actionable insights for future mission designers, enabling distributed on-board learning for more autonomous, resilient, and data-driven satellite operations.