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

arXiv: https://arxiv.org/abs/2608.03436 · HTML: https://mlsanity.com/paper/2608.03436/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [Ground-Assisted Federated Learning in LEO Satellite Constellations](https://mlsanity.com/paper/2109.01348/) (2021)
- [FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations](https://mlsanity.com/paper/2202.01267/) (2022)
- [FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs](https://mlsanity.com/paper/2205.07216/) (2022)
- [AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms](https://mlsanity.com/paper/2212.11522/) (2022)
- [FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation](https://mlsanity.com/paper/2304.08537/) (2023)
- [FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion](https://mlsanity.com/paper/2311.09540/) (2023)
- [DFedSat: Communication-Efficient and Robust Decentralized Federated Learning for LEO Satellite Constellations](https://mlsanity.com/paper/2407.05850/) (2024)
- [Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach](https://mlsanity.com/paper/2410.13602/) (2024)
- [ALANINE: A Novel Decentralized Personalized Federated Learning For Heterogeneous LEO Satellite Constellation](https://mlsanity.com/paper/2411.07752/) (2024)
- [sat-QFL: Secure Quantum Federated Learning for Low Orbit Satellites](https://mlsanity.com/paper/2509.16504/) (2025)
- [Sparse Incremental Aggregation in Satellite Federated Learning](https://mlsanity.com/paper/2501.11385/) (2025)
- [A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks](https://mlsanity.com/paper/2507.22339/) (2025)
- [Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks](https://mlsanity.com/paper/2512.08882/) (2025)

## 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.