In the ambitious realm of space AI, the integration of federated learning (FL) with low Earth orbit (LEO) satellite constellations holds immense promise. However, many challenges persist in terms of feasibility, learning efficiency, and convergence. These hurdles stem from the bottleneck in communication, characterized by sporadic and irregular connectivity between LEO satellites and ground stations, coupled with the limited computation capability of satellite edge computing (SEC). This paper proposes a novel FL-SEC framework that empowers LEO satellites to execute large-scale machine learning (ML) tasks onboard efficiently. Its key components include i) personalized learning via divide-and-conquer, which identifies and eliminates redundant satellite images and converts complex multi-class classification problems to simple binary classification, enabling rapid and energy-efficient training of lightweight ML models suitable for IoT/edge devices on satellites; ii) orbital model retraining, which generates an aggregated "orbital model" per orbit and retrains it before sending to the ground station, significantly reducing the required communication rounds. We conducted experiments using Jetson Nano, an edge device closely mimicking the limited compute on LEO satellites, and a real satellite dataset. The results underscore the effectiveness of our approach, highlighting SEC's ability to run lightweight ML models on real and high-resolution satellite imagery. Our approach dramatically reduces FL convergence time by nearly 30 times, and satellite energy consumption down to as low as 1.38 watts, all while maintaining an exceptional accuracy of up to 96%.

翻译：在太空人工智能的宏伟领域中，联邦学习（FL）与低地球轨道（LEO）卫星星座的集成极具潜力。然而，在可行性、学习效率和收敛性方面仍存在诸多挑战。这些障碍源于通信瓶颈，表现为LEO卫星与地面站之间零星且不规则的连接，以及卫星边缘计算（SEC）有限的计算能力。本文提出了一种新颖的FL-SEC框架，使LEO卫星能够高效地执行大规模机器学习（ML）任务。其关键组件包括：i) 通过分治策略实现个性化学习，识别并消除冗余卫星图像，将复杂的多类别分类问题转化为简单的二分类问题，从而支持卫星上适用于物联网/边缘设备的轻量级ML模型实现快速且节能的训练；ii) 轨道模型再训练，为每个轨道生成聚合的“轨道模型”并在发送至地面站前对其进行再训练，大幅减少所需的通信轮次。我们使用Jetson Nano（一种紧密模拟LEO卫星有限计算能力的边缘设备）和真实卫星数据集进行了实验。结果突显了我们方法的有效性，展示了SEC在真实高分辨率卫星图像上运行轻量级ML模型的能力。我们的方法将FL收敛时间缩短了近30倍，卫星能耗低至1.38瓦，同时保持了高达96%的卓越准确率。