Federated learning for orbital edge computing: architectures, opportunities, and open challenges

Yang, Nan, Gambito, Mark Adrian, Javadi, Bahman, Calheiros, Rodrigo N., Villari, Massimo and Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 2026. Federated learning for orbital edge computing: architectures, opportunities, and open challenges. IEEE Internet Computing 10.1109/mic.2026.3740411

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Abstract

Low Earth orbit (LEO) satellite constellations are becoming orbital edge computing platforms that can sense, process, and learn near data sources. Federated Learning (FL) is promising in this setting because it supports repeated model adaptation without forcing every raw observation to be downlinked, centralized, or redistributed. Yet orbital FL is not terrestrial FL placed in space: it must operate over shell- and plane-structured constellations, sparse ground-station access, time-varying inter-satellite links, heterogeneous onboard hardware, limited energy and thermal budgets, and delayed or noisy labels. This article connects representative architectures, mission use cases, and open challenges for GS-centric, GS-assisted, and fully on-orbit FL.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	1089-7801
Last Modified:	19 Oct 2026 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/190276

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