为卫星联邦学习设计节能调度，电池寿命提升3倍以上。
Energy-Aware Federated Learning in Satellite Constellations
- 根据卫星能量状态动态调整计算时间，减少电池使用
- 在不降低模型收敛速度的前提下，电池寿命提升超3倍
- 适合需要长期稳定运行的太空物联网和星群系统
卫星星群中的联邦学习通过卫星协同训练机器学习模型，是实现全球智能互联及将空间网络融入地面移动网络的有前景技术。该计算密集型任务所需的能量由太阳能板提供，或在地球阴影中由内部电池供电。对电池及系统可用能量资源的精细管理不仅关乎卫星可靠运行，也避免电池过早老化。本文提出一种新型能量感知的计算时间调度策略，旨在最小化电池使用，且不影响模型收敛速度。数值结果表明，相比无能耗感知的任务调度，电池寿命可提升超过3倍。
原文摘要 · Abstract (English)
Federated learning in satellite constellations, where the satellites collaboratively train a machine learning model, is a promising technology towards enabling globally connected intelligence and the integration of space networks into terrestrial mobile networks. The energy required for this computationally intensive task is provided either by solar panels or by an internal battery if the satellite is in Earth's shadow. Careful management of this battery and system's available energy resources is not only necessary for reliable satellite operation, but also to avoid premature battery aging. We propose a novel energy-aware computation time scheduler for satellite FL, which aims to minimize battery usage without any impact on the convergence speed. Numerical results indicate an increase of more than 3x in battery lifetime can be achieved over energy-agnostic task scheduling.
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