Decentralized Learning in Space: A Framework for Efficient Model Training in LEO Constellations

Relying on ground-based infrastructure for model training in Low Earth Orbit (LEO) constellations introduces significant challenges, including intermittent and costly spaceground communication. To address these challenges, this paper proposes a decentralized learning framework that enables model training directly within satellite constellations, utilizing intra-and inter-plane inter-satellite links (ISLs) for information exchange. The paper develops a geo-spatial filtering mechanism that selects the most relevant satellites to form a smaller, more efficient constellation. It then introduces a novel Adaptive Halving-Doubling (AHD) algorithm that enables collective communication within the emerging partial ring topology. Experimental results validate the framework's effectiveness, efficiency, and scalability. Notably, the transmission energy was observed to constitute less than 10% of traditional full constellation (FC) setups, even with increasing model sizes. Moreover, communication overhead relative to FC setups decreased with increasing constellation size, highlighting the operational efficiency of the framework.