Federated Learning in Satellite Constellations

This article has been accepted for publication in IEEE Network. This is the author's version which has not been fully edited and content may change prior to final publication. Abstract Federated learning (FL) has recently emerged as a distributed machine learning paradigm for systems with limited and intermittent connectivity. This paper presents the new context brought to FL by satellite constellations, where the connectivity patterns are significantly different from the ones observed in conventional terrestrial FL. The focus is on large constellations in low Earth orbit (LEO), where each satellites participates in a data-driven FL task using a locally stored dataset. This scenario is motivated by the trend towards mega constellations of interconnected small satellites in LEO and the integration of artificial intelligence in satellites. We propose a classification of satellite FL based on the communication capabilities of the satellites, the constellation design, and the location of the parameter server. A comprehensive overview of the current state-of-the-art in this field is provided and the unique challenges and opportunities of satellite FL are discussed. Finally, we outline several open research directions for FL in satellite constellations and present some future perspectives on this topic. A rapidly increasing number of satellites is orbiting Earth and collecting massive amounts of data. This data can be utilized in three principal ways: to train a machine learning (ML) model, to do inference, or to store it for later retrieval. B. Matthiesen, N. Razmi, and A. Dekorsy are with the Gauss-Olbers Center, c/o University of Bremen, and the Department of Communications Engineering, University of Bremen, 28359 Bremen, Germany (e-mail: {matthiesen, razmi, dekorsy}@ant.unibremen.de). I. Leyva-Mayorga and P. Popovski are with the Department of Electronic Systems, Aalborg University, 9100 Aalborg, Denmark (e-mail: {ilm, petarp}@es.aau.dk).