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，联邦学习与低轨卫星星座的融合展现出巨大潜力。然而，在可行性、学习效率及收敛性方面仍存在诸多挑战。这些障碍源于通信瓶颈——低轨卫星与地面站之间偶发且不规则的连接特性，以及卫星边缘计算有限的计算能力。本文提出一种新颖的联邦学习-卫星边缘计算框架，使低轨卫星能够高效地在轨执行大规模机器学习任务。其核心组件包括：i) 通过分治策略实现的个性化学习，通过识别并消除冗余卫星图像，将复杂的多分类问题转化为简单的二分类问题，从而支持卫星上适合物联网/边缘设备的轻量级机器学习模型的快速节能训练；ii) 轨道模型重训练方法，为每个轨道生成聚合的"轨道模型"并在发送至地面站前进行重训练，显著减少所需的通信轮次。我们采用Jetson Nano（一种高度模拟低轨卫星有限计算能力的边缘设备）及真实卫星数据集进行实验。结果验证了该方法的有效性，凸显了卫星边缘计算在真实高分辨率卫星图像上运行轻量级机器学习模型的能力。本方法将联邦学习收敛时间缩短近30倍，卫星能耗低至1.38瓦，同时保持高达96%的卓越精度。