Space AI has become increasingly important and sometimes even necessary for government, businesses, and society. An active research topic under this mission is integrating federated learning (FL) with satellite communications (SatCom) so that numerous low Earth orbit (LEO) satellites can collaboratively train a machine learning model. However, the special communication environment of SatCom leads to a very slow FL training process up to days and weeks. This paper proposes NomaFedHAP, a novel FL-SatCom approach tailored to LEO satellites, that (1) utilizes high-altitude platforms (HAPs) as distributed parameter servers (PS) to enhance satellite visibility, and (2) introduces non-orthogonal multiple access (NOMA) into LEO to enable fast and bandwidth-efficient model transmissions. In addition, NomaFedHAP includes (3) a new communication topology that exploits HAPs to bridge satellites among different orbits to mitigate the Doppler shift, and (4) a new FL model aggregation scheme that optimally balances models between different orbits and shells. Moreover, we (5) derive a closed-form expression of the outage probability for satellites in near and far shells, as well as for the entire system. Our extensive simulations have validated the mathematical analysis and demonstrated the superior performance of NomaFedHAP in achieving fast and efficient FL model convergence with high accuracy as compared to the state-of-the-art.

翻译：空间人工智能对政府、企业乃至社会日益重要，甚至已成为必要条件。在此使命下，一个活跃的研究方向是将联邦学习与卫星通信相结合，使大量低地球轨道卫星能够协同训练机器学习模型。然而，卫星通信的特殊传输环境导致联邦学习训练过程极其缓慢，耗时可达数天至数周。本文提出NomaFedHAP——一种专为LEO卫星设计的联邦学习-卫星通信新方法，其核心创新包括：(1) 利用高空平台作为分布式参数服务器以增强卫星可见性；(2) 将非正交多址接入引入LEO卫星网络，实现快速且带宽高效的模型传输；此外，(3) 提出一种新型通信拓扑，通过高空平台桥接不同轨道卫星以抑制多普勒频移；(4) 设计新型联邦学习模型聚合方案，实现不同轨道与壳层间模型的最优均衡；同时，(5) 推导出近端壳层和远端壳层卫星及整个系统中断概率的闭式表达式。大量仿真实验验证了数学分析的准确性，并证明与现有技术相比，NomaFedHAP在实现快速高精度联邦学习模型收敛方面具有显著优越性能。