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.
@article{arxiv.2109.01348,
title = {Ground-Assisted Federated Learning in LEO Satellite Constellations},
author = {Nasrin Razmi and Bho Matthiesen and Armin Dekorsy and Petar Popovski},
journal= {arXiv preprint arXiv:2109.01348},
year = {2023}
}