Ground-Assisted Federated Learning in LEO Satellite Constellations

• URL: http://arxiv.org/abs/2109.01348v1
• Date: Fri, 3 Sep 2021 07:17:20 GMT
• Title: Ground-Assisted Federated Learning in LEO Satellite Constellations
• Authors: Nasrin Razmi and Bho Matthiesen and Armin Dekorsy and Petar Popovski
• Abstract summary: We propose a new set of algorithms based of Federated learning (FL) Our approach differs substantially from the standard FL algorithms, as it takes into account the predictable connectivity patterns that are immanent to the constellations. In particular, the achieved test accuracy is within 96% to 99.6% of the centralized solution.
• Score: 39.646300161201076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 data sets. To address this problem, we propose a new set of algorithms based of Federated learning (FL). Our approach differs substantially from the standard FL algorithms, as it takes into account the predictable connectivity patterns that are immanent to the LEO constellations. Extensive numerical evaluations highlight the fast convergence speed and excellent asymptotic test accuracy of the proposed method. In particular, the achieved test accuracy is within 96% to 99.6% of the centralized solution and the proposed algorithm has less hyperparameters to tune than state-of-the-art asynchronous FL methods.

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