Distributed machine learning (DML) results from the synergy between machine learning and connectivity. Federated learning (FL) is a prominent instance of DML in which intermittently connected mobile clients contribute to the training of a common learning model. This paper presents the new context brought to FL by satellite constellations where the connectivity patterns are significantly different from the ones assumed in terrestrial FL. We provide a taxonomy of different types of satellite connectivity relevant for FL and show how the distributed training process can overcome the slow convergence due to long offline times of clients by taking advantage of the predictable intermittency of the satellite communication links.
翻译:联邦学习(FL)是DML的一个突出实例,在DML中,断断续续的移动客户对共同学习模式的培训作出了贡献,本文件介绍了卫星星座给FL带来的新环境,其中连接模式与地面FL中假设的模式大不相同。 我们提供了与FL相关的不同类型卫星连接分类,并展示了分布式培训过程如何利用卫星通信连接的可预见间隙,克服客户长期离线时间造成的缓慢趋同。