Bringing Federated Learning to Space

• URL: http://arxiv.org/abs/2511.14889v1
• Date: Tue, 18 Nov 2025 20:16:07 GMT
• Title: Bringing Federated Learning to Space
• Authors: Grace Kim, Filip Svoboda, Nicholas Lane,
• Abstract summary: Federated learning offers a promising framework to conduct collaborative model training across satellite networks.<n>We introduce a comprehensive "space-ification" framework that adapts terrestrial algorithms to operate under orbital constraints.<n>Our analysis demonstrates that space-adapted FL algorithms efficiently scale to constellations of up to 100 satellites.
• Score: 3.058685580689604
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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