Federated Learning in Satellite Constellations
This work is incremental, as it surveys and categorizes existing federated learning approaches for satellite networks, targeting researchers and practitioners in distributed AI and space technology.
The paper addresses the application of federated learning in satellite constellations, highlighting the distinct connectivity challenges compared to terrestrial systems, and provides a classification and overview of the field without presenting new experimental results or concrete numbers.
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.