论文标题
安排LEO卫星星座的地面协助联合学习
Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations
论文作者
论文摘要
考虑到直接在低地球轨道（LEO）中的卫星上的机器学习模型的分布式训练。基于专门针对卫星场景的独特挑战的联合学习（FL）算法，我们设计了一个调度程序，该调度程序利用了地面站（GS）和卫星之间访问时间的可预测性，以降低模型的稳定性。数值实验表明，这可以提高收敛速度三个因子。
Distributed training of machine learning models directly on satellites in low Earth orbit (LEO) is considered. Based on a federated learning (FL) algorithm specifically targeted at the unique challenges of the satellite scenario, we design a scheduler that exploits the predictability of visiting times between ground stations (GS) and satellites to reduce model staleness. Numerical experiments show that this can improve the convergence speed by a factor three.
