The design of satellite missions is currently undergoing a paradigm shift from the historical approach of individualised monolithic satellites towards distributed mission configurations, consisting of multiple small satellites. With a rapidly growing number of such satellites now deployed in orbit, each collecting large amounts of data, interest in on-board orbital edge computing is rising. Federated Learning is a promising distributed computing approach in this context, allowing multiple satellites to collaborate efficiently in training on-board machine learning models. Though recent works on the use of Federated Learning in orbital edge computing have focused largely on homogeneous satellite constellations, Federated Learning could also be employed to allow heterogeneous satellites to form ad-hoc collaborations, e.g. in the case of communications satellites operated by different providers. Such an application presents additional challenges to the Federated Learning paradigm, arising largely from the heterogeneity of such a system. In this position paper, we offer a systematic review of these challenges in the context of the cross-provider use case, giving a brief overview of the state-of-the-art for each, and providing an entry point for deeper exploration of each issue.

翻译：卫星任务设计目前正经历从历史性的单体卫星定制化方法向分布式任务配置的范式转变，这种配置由多颗小型卫星组成。随着此类在轨部署卫星数量的快速增长，每颗卫星都在收集大量数据，对星载轨道边缘计算的兴趣日益浓厚。联邦学习在此背景下是一种前景广阔的分布式计算方法，允许多颗卫星高效协作训练星载机器学习模型。尽管近期关于联邦学习在轨道边缘计算中应用的研究主要集中于同构卫星星座，但联邦学习同样可用于促成异构卫星形成临时协作，例如在不同运营商管理的通信卫星之间。此类应用给联邦学习范式带来了额外挑战，主要源于系统的异构性。在本立场文件中，我们针对跨运营商用例背景下的这些挑战进行了系统性梳理，简要概述了各项挑战的最新研究进展，并为每个问题的深入探索提供了切入点。