Ground-Assisted Federated Learning in LEO Satellite Constellations

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.

Submitted to IEEE Wireless Communications Letters

Cited by in corpus (7)

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• On-board Federated Learning for Satellite Clusters with Inter-Satellite Links
• Distributed satellite information networks: Architecture, enabling technologies, and trends
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• FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation
• NGSO Constellation Design for Global Connectivity
• Sparse Incremental Aggregation in Multi-Hop Federated Learning