发表机构
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对低轨卫星网络联邦学习的动态拓扑与通信限制，提出FedRings环形去中心化框架，通过感知链路的路由调度、自适应聚合及历史补偿实现高效稳定学习，性能优于现有方法。
AI 中文摘要
低轨（LEO）卫星网络上的联邦学习受频繁链路变化、短接触时间及高度动态拓扑限制，使得集中式或同步训练效率低下且难以扩展。为解决该问题，本文提出FedRings，一种将卫星组织为环形通信结构的去中心化框架。它采用感知链路的通信调度时空路由策略，使模型交换与LEO中实际可见窗口及时变连通性模式对齐；模型更新通过自适应稀疏增量聚合沿环传播，该方法通过逐步组合和压缩更新减少通信开销；为处理通信中断，历史补偿机制维持训练连续性。结合感知拓扑的路由、通信调度及高效聚合，FedRings可在动态LEO网络中实现稳定高效的学习，同时降低通信成本，实验表明其在现实环境中始终优于现有方法。
英文摘要
Federated learning over low Earth orbit (LEO) satellite networks is limited by frequent link changes, short contact times, and a highly dynamic topology, making centralized or synchronized training inefficient and hard to scale. To address this, we propose FedRings, a decentralized framework that organizes satellites into ring-based communication structures. It uses a spatio-temporal routing strategy with link-aware communication scheduling to align model exchange with actual visibility windows and time-varying connectivity patterns in LEO. Model updates are propagated along the ring using adaptive sparse incremental aggregation, which reduces communication overhead by progressively combining and compressing updates. To handle communication interruptions, a historical compensation mechanism maintains training continuity. By combining topology-aware routing, communication scheduling, and efficient aggregation, FedRings enables stable and efficient learning in dynamic LEO networks while reducing communication cost, and experiments show it consistently outperforms existing methods in realistic settings.
