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Start Over Author "Popovski, Petar" Publisher institute of electrical and electronics engineers (ieee)
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17. Federated Learning in Satellite Constellations

Author

Matthiesen, Bho, Razmi, Nasrin, Leyva-Mayorga, Israel, Dekorsy, Armin, and Popovski, Petar

Subjects
Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences, Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning, Computer Networks and Communications, Hardware and Architecture, Information Theory (cs.IT), Computer Science - Information Theory, Software, Machine Learning (cs.LG), Information Systems
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., Copyright 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Published
2023

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