Low earth orbit (LEO) mega-constellations, integrating government space systems and commercial practices, have emerged as enabling technologies for the sixth generation (6G) networks due to their good merits of global coverage and ubiquitous services for military and civilian use cases. However, convergent LEO-based satellite networking infrastructures still lack leveraging the synergy of space and terrestrial systems. This paper, therefore, extends conventional serverless cloud platforms with serverless edge learning architectures for 6G proliferated LEO (p-LEO) satellite ecosystems and provides a new distributed training design from a networking perspective. The proposed design dynamically orchestrates communications and computation functionalities and resources among heterogeneous physical units to efficiently fulfill multi-agent deep reinforcement learning for service-level agreements. Innovative ecosystem enhancements, including ultrabroadband access, anti-jammed transmissions, resilient networking, and related open challenges, are also investigated for end-to-end connectivity, communications, and learning performance.

翻译：低地球轨道(LEO)巨型星座,将政府空间系统和商业做法结合起来,已成为第六代(6G)网络的赋能技术,因为其良好的优点是覆盖全球,为军事和民用案件提供无处不在的服务,然而,聚集的低地轨道卫星网络基础设施仍然缺乏利用空间和地面系统的协同作用,因此,本文件扩展了传统的无服务器云平台,为6G 扩散的低地轨道(p-LEO)卫星生态系统提供了无服务器边缘学习结构,并从联网角度提供了新的分布式培训设计。拟议设计动态协调通信和计算各不同实体单位的功能和资源,以高效完成服务级协议的多剂深度强化学习。创新的生态系统增强,包括超宽带接入、防堵传输、弹性联网和相关公开挑战,也用于端对端连接、通信和学习表现的调查。