arXiv cs.AI· Zheng Lin, Zhanwei Wang, Yuxin Zhang, Yafei Wang, Ons Aouedi, Zihan Fang, Jinfeng Xu, Jing Yang, Vu Nguyen Ha, Yuanwei Liu, Mohamed-Slim Alouini, Symeon Chatzinotas·· 3 小时前AI 评分40
首个综述：低轨卫星星座中的分布式训练、协同推理与 LLM 星上适配
Satellite Intelligence: Distributed Training, Collaborative Inference,and LLM Adaptation in LEO Constellation
AI 导读
arXiv 论文提出首个联合覆盖低轨（LEO）卫星网络三大方向的综述：分布式训练（联邦学习 FL 与拆分学习 SL 实现星地协同训练）、协同推理（单星、多星与星地架构满足实时服务需求）以及星上 LLM 适配与服务。论文针对卫星网络动态拓扑、间歇链路与严格 SWaP 约束，给出卫星智能（SI）的设计原则，并指出开放挑战与未来方向，为空间原生 AI 技术提供研究路线图。
正文 · 原文
Authors:Zheng Lin, Zhanwei Wang, Yuxin Zhang, Yafei Wang, Ons Aouedi, Zihan Fang, Jinfeng Xu, Jing Yang, Vu Nguyen Ha, Yuanwei Liu, Mohamed-Slim Alouini, Symeon Chatzinotas
Abstract:The proliferation of orbital sensing data and the real-time demands of emerging global services are pushing low Earth orbit (LEO) satellite networks from bent-pipe relays toward intelligent onboard processing. This shift confronts a fundamental tension, where modern artificial intelligence (AI) training paradigms require intensive computation, abundant memory, and stable connectivity, all of which are scarce under the dynamic topologies, intermittent links, and severe size, weight, and power (SWaP) constraints of satellite networks. Addressing this tension demands rethinking AI workflows, from training and inference to the emerging deployment of large language models (LLMs), under a constraint-aware design philosophy. To the best of our knowledge, this paper provides the first survey that jointly covers all three, whereas prior surveys examine these aspects in isolation. We begin by identifying the deployment challenge of satellite intelligence (SI) and deriving design principles for SI. We then examine three advancing paradigms: i) distributed training, where federated learning (FL) and split learning (SL) enable satellite-ground co-training under resource limitations; ii) collaborative inference, which exploits single-satellite, multi-satellite, and satellite-ground architectures to meet real-time service demands; and iii) on-orbit LLM adaptation and serving, which tackles the frontier challenge of deploying LLMs under severe orbital constraints and resource-aware serving. Finally, we identify open challenges and future directions. By bridging the AI and satellite networks, this paper provides a research roadmap for the implementation of space-native AI technologies.
| Comments: | 38 pages, 16 figures |
| Subjects: | Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.35282 [cs.NI] |
| (or arXiv:2610.35282v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35282 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lin Zheng [view email]
[v1]
Mon, 26 Oct 2026 11:55:41 UTC (5,589 KB)
来源：arXiv cs.AI · arxiv.org