arXiv Machine Learning August 5, 2026 By Ziwu Liu, In\^es Pinto Gouveia, Rehana Yasmin, Paulo Esteves-Verissimo, Ali Shoker

FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations

Read the original on arXiv Machine Learning →

arXiv:2608. 03436v1 Announce Type: cross Abstract: Federated learning over low Earth orbit (LEO) satellite networks is limited by frequent link changes, short contact times, and a highly dynamic topology, making centralized or synchronized training inefficient and hard to scale.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations

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