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FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations

arXiv 2608.03436 ↗First posted 4 Aug 2026cs.DC cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 4 Aug 2026.

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15 of 24 references.

References · 15

  1. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,488
  2. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 276
  3. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  4. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  5. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  6. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  7. 2023FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigationcited by 8
  8. 2023FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusioncited by 8
  9. 2024DFedSat: Communication-Efficient and Robust Decentralized Federated Learning for LEO Satellite Constellationscited by 6
  10. 2024Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approachcited by 4
  11. 2024ALANINE: A Novel Decentralized Personalized Federated Learning For Heterogeneous LEO Satellite Constellationcited by 4
  12. 2025sat-QFL: Secure Quantum Federated Learning for Low Orbit Satellitescited by 2
3 more
  1. 2025Sparse Incremental Aggregation in Satellite Federated Learningcited by 1
  2. 2025A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networkscited by 1
  3. 2025Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networkscited by 1

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