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.

翻译：联邦学习（FL）近期作为一种适用于有限且间歇性连接系统的分布式机器学习范式而崭露头角。本文介绍了卫星星座为FL带来的新背景，其中连接模式与传统地面FL中的模式显著不同。研究重点聚焦于低地球轨道（LEO）中的大型星座，其中每颗卫星使用本地存储的数据集参与数据驱动的FL任务。这一场景的动机源于LEO中互联小卫星巨型星座的发展趋势以及人工智能在卫星中的集成应用。我们基于卫星的通信能力、星座设计以及参数服务器的位置，提出了一种卫星FL分类方法。本文对该领域当前最新技术进行了全面概述，并讨论了卫星FL面临的独特挑战与机遇。最后，我们概述了卫星星座中FL的若干开放研究方向，并提出了该主题的未来展望。