为卫星星座设计高效通信的联邦学习算法，降低地面站通信负担。
Communication-Efficient Learning for Satellite Constellations
- 通过本地训练与压缩减少卫星与地面站通信次数和数据量。
- 引入误差反馈机制，在真实太空场景中显著提升模型精度。
- 算法通用性强，适用于各类联邦学习任务，适合空间计算场景。
低地球轨道卫星星座已广泛用于定位、地球成像和通信。本文研究利用此类星座解决学习问题，采用联邦学习范式：卫星本地收集并处理数据，地面站聚合本地模型。针对通信开销大的问题，提出一种新型通信高效算法，通过本地训练和数据压缩减少通信频次与传输量，并设计误差反馈机制以提升模型准确率。该机制还可推广至其他算法，具有通用性。理论分析了算法收敛性，并在真实太空场景下通过仿真与现有方法对比，验证其优越性能。
原文摘要 · Abstract (English)
Satellite constellations in low-Earth orbit are now widespread, enabling positioning, Earth imaging, and communications. In this paper we address the solution of learning problems using these satellite constellations. In particular, we focus on a federated approach, where satellites collect and locally process data, with the ground station aggregating local models. We focus on designing a novel, communication-efficient algorithm that still yields accurate trained models. To this end, we employ several mechanisms to reduce the number of communications with the ground station (local training) and their size (compression). We then propose an error feedback mechanism that enhances accuracy, which yields, as a byproduct, an algorithm-agnostic error feedback scheme that can be more broadly applied. We analyze the convergence of the resulting algorithm, and compare it with the state of the art through simulations in a realistic space scenario, showcasing superior performance.
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