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arXiv · 2109.01348

Ground-Assisted Federated Learning in LEO Satellite Constellations

Nasrin Razmi·Bho Matthiesen·Armin Dekorsy·Petar Popovski
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Abstract

In Low Earth Orbit (LEO) mega constellations, there are relevant use cases, such as inference based on satellite imaging, in which a large number of satellites collaboratively train a machine learning model without sharing their local datasets. To address this problem, we propose a new set of algorithms based on Federated learning (FL), including a novel asynchronous FL procedure based on FedAvg that exhibits better robustness against heterogeneous scenarios than the state-of-the-art. Extensive numerical evaluations based on MNIST and CIFAR-10 datasets highlight the fast convergence speed and excellent asymptotic test accuracy of the proposed method.

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BibTeXRIS

Nasrin Razmi, Bho Matthiesen, Armin Dekorsy, Petar Popovski. 2021-09-03. Ground-Assisted Federated Learning in LEO Satellite Constellations. https://doi.org/10.1109/lwc.2022.3141120

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