Driven by the ever increasing penetration and proliferation of data-driven applications, a new generation of wireless communication, the sixth generation (6G) mobile system enhanced by artificial intelligence, has attracted substantial research interests. Among various candidate technologies of 6G, low Earth orbit (LEO) satellites have appealing characteristics of ubiquitous wireless access. However, the costs of satellite communication (SatCom) are still high, relative to their counterparts of ground mobile networks. To support massively interconnected devices with intelligent adaptive learning and reduce expensive traffic in SatCom, we propose federated learning (FL) in LEO-based satellite communication networks. We first review the state-of-the-art LEO-based SatCom and related machine learning (ML) techniques, and then analyze four possible ways of combining ML with satellite networks. The learning performance of the proposed strategies is evaluated by simulation and results reveal that FL-based computing networks improve the performance of communication overheads and latency. Finally, we discuss future research topics along this research direction.

中文翻译：

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具有联邦学习功能的基于卫星的计算网络


在数据驱动应用日益普及和扩散的推动下，新一代无线通信、人工智能增强的第六代（6G）移动系统引起了广泛的研究兴趣。在6G的各种候选技术中，低地球轨道（LEO）卫星具有无处不在的无线接入的吸引人的特性。然而，与地面移动网络相比，卫星通信 (SatCom) 的成本仍然很高。为了通过智能自适应学习支持大规模互连设备并减少卫星通信中昂贵的流量，我们提出在基于 LEO 的卫星通信网络中进行联邦学习（FL）。我们首先回顾了最先进的基于 LEO 的卫星通信和相关机器学习 (ML) 技术，然后分析了将 ML 与卫星网络相结合的四种可能方法。通过模拟评估所提出策略的学习性能，结果表明基于 FL 的计算网络改善了通信开销和延迟的性能。最后，我们沿着这个研究方向讨论未来的研究主题。