Mega-constellations of small satellites have evolved into a source of massive amount of valuable data. To manage this data efficiently, on-board federated learning (FL) enables satellites to train a machine learning (ML) model collaboratively without having to share the raw data. This paper introduces a scheme for scheduling on-board FL for constellations connected with intra-orbit inter-satellite links. The proposed scheme utilizes the predictable visibility pattern between satellites and ground station (GS), both at the individual satellite level and cumulatively within the entire orbit, to mitigate intermittent connectivity and best use of available time. To this end, two distinct schedulers are employed: one for coordinating the FL procedures among orbits, and the other for controlling those within each orbit. These two schedulers cooperatively determine the appropriate time to perform global updates in GS and then allocate suitable duration to satellites within each orbit for local training, proportional to usable time until next global update. This scheme leads to improved test accuracy within a shorter time.

翻译：小型卫星巨型星座已发展成为海量有价值数据的来源。为高效管理这些数据，星上联邦学习（FL）使卫星能够在不共享原始数据的情况下协同训练机器学习（ML）模型。本文提出了一种针对星间链路互联轨道内星座的星上联邦学习调度方案。该方案利用卫星与地面站之间可预测的可见性模式（既考虑单颗卫星层面，也考虑整个轨道的累积效应），以缓解间歇性连接问题并充分利用可用时间。为此，方案采用两个不同的调度器：一个用于协调轨道间的联邦学习流程，另一个用于控制每个轨道内的流程。这两个调度器协同确定在地面站执行全局更新的合适时机，然后为轨道内卫星分配与下一次全局更新前的可用时间成比例的局部训练时长。该方案能在更短时间内实现更高的测试准确率。