The advances in satellite technology developments have recently seen a large number of small satellites being launched into space on Low Earth orbit (LEO) to collect massive data such as Earth observational imagery. The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS. Satellite edge computing (SEC), on the other hand, allows each satellite to train an ML model onboard and uploads only the model to the GS which appears to be a promising concept. This paper proposes FedLEO, a novel federated learning (FL) framework that realizes the concept of SEC and overcomes the limitation (slow convergence) of existing FL-based solutions. FedLEO (1) augments the conventional FL's star topology with ``horizontal'' intra-plane communication pathways in which model propagation among satellites takes place; (2) optimally schedules communication between ``sink'' satellites and the GS by exploiting the predictability of satellite orbiting patterns. We evaluate FedLEO extensively and benchmark it with the state of the art. Our results show that FedLEO drastically expedites FL convergence, without sacrificing -- in fact it considerably increases -- the model accuracy.

翻译：卫星技术发展的进步近年来导致大量小型卫星被发射到低地球轨道（LEO）空间，以收集地球观测图像等海量数据。由于LEO卫星与地面站（GS）之间的带宽限制和间歇性连接，传统的将此类数据下载到地面站以训练机器学习（ML）模型的方法并不可取。另一方面，卫星边缘计算（SEC）允许每颗卫星在轨训练ML模型，并仅将模型上传至地面站，这似乎是一个很有前景的概念。本文提出FedLEO，一种新颖的联邦学习（FL）框架，该框架实现了SEC的概念，并克服了现有基于FL的解决方案的局限性（收敛速度慢）。FedLEO：（1）通过引入卫星间发生模型传播的“水平”平面内通信路径，增强了传统FL的星型拓扑结构；（2）通过利用卫星轨道模式的可预测性，优化“汇聚”卫星与地面站之间的通信调度。我们对FedLEO进行了广泛评估，并与现有最优方法进行了基准测试。结果表明，FedLEO在显著加速FL收敛的同时，不仅没有牺牲模型精度，反而大幅提升了模型精度。