FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations
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 combi...
| Authors | Ziwu Liu, Inês Pinto Gouveia, Rehana Yasmin et al. |
|---|---|
| Primary category | cs.DC |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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