为低轨卫星网络设计去中心化联邦学习框架，解决连接频繁变化难题。
FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations
- 将卫星分组为环状结构，按可见窗口调度通信
- 通过稀疏增量聚合降低通信开销，支持动态拓扑
- 适合大规模低轨卫星群，提升训练稳定性和效率
低地球轨道（LEO）卫星网络中的联邦学习受限于频繁的链路切换、短暂的接触时间及高度动态的拓扑结构，导致集中式或同步训练效率低下且难以扩展。为此，我们提出 FedRings，一种去中心化框架，将卫星组织成基于环的通信结构。该框架采用时空路由策略与链路感知的通信调度，使模型更新与实际可见窗口及随时间变化的连通性模式对齐。模型更新沿环传播，使用自适应稀疏增量聚合，逐步合并并压缩更新信息，显著降低通信开销。针对通信中断问题，引入历史补偿机制以维持训练连续性。结合拓扑感知路由、通信调度与高效聚合，FedRings 在动态 LEO 网络中实现了稳定高效的训练，同时降低通信成本；实验表明其在真实场景下持续优于现有方法。
原文摘要 · Abstract (English)
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.
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