将卫星接入边缘：普适且高效的低轨卫星联邦学习
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning
AI总结：
针对低轨卫星通信间歇与算力受限问题，提出FL-SEC框架，通过分治个性化学习和轨道模型重训练，在Jetson Nano与真实数据集上实现近30倍收敛加速、1.38瓦低能耗和96%准确率。
摘要：
在太空AI这一雄心勃勃的领域中，联邦学习（FL）与低地球轨道（LEO）卫星星座的结合具有巨大前景。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：LEO卫星与地面站之间零星且不规则的连接，加之卫星边缘计算（SEC）有限的计算能力。本文提出一种新颖的FL-SEC框架，使LEO卫星能够在星上高效执行大规模机器学习（ML）任务。其关键组件包括：i）通过分而治之实现个性化学习，识别并消除冗余卫星图像，将复杂的多类分类问题转化为简单的二分类，从而快速且节能地训练适用于星上物联网/边缘设备的轻量级ML模型；ii）轨道模型重训练，为每条轨道生成聚合的“轨道模型”，并在发送至地面站前对其重训练，显著减少所需通信轮次。我们使用Jetson Nano（一种高度模拟LEO卫星有限算力的边缘设备）和真实卫星数据集开展实验。结果证明了本方法的有效性，凸显了SEC在真实高分辨率卫星图像上运行轻量级ML模型的能力。本方法将FL收敛时间大幅缩短近30倍，卫星能耗降至低至1.38瓦，同时保持高达96%的出色准确率。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
