mlsanityData to 13 Oct 2026

Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation

arXiv 2303.16071 ↗First posted 25 Mar 2023cs.LG cs.NI eess.SPcited by 6

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Abstract (arXiv)

Low-Earth orbit (LEO) satellites have been prosperously deployed for various Earth observation missions due to its capability of collecting a large amount of image or sensor data. However, traditionally, the data training process is performed in the terrestrial cloud server, which leads to a high transmission overhead. With the recent development of LEO, it is more imperative to provide ultra-dense LEO constellation with enhanced on-board computation capability. Benefited from it, we have proposed a collaborative federated learning for low Earth orbit (FELLO). We allocate the entire process on LEOs with low payload inter-satellite transmissions, whilst the low-delay terrestrial gateway server (GS) only takes care for initial signal controlling. The GS initially selects an LEO server, whereas its LEO clients are all determined by clustering mechanism and communication capability through the optical inter-satellite links (ISLs). The re-clustering of changing LEO server will be executed once with low communication quality of FELLO. In the simulations, we have numerically analyzed the proposed FELLO under practical Walker-based LEO constellation configurations along with MNIST training dataset for classification mission. The proposed FELLO outperforms the conventional centralized and distributed architectures with higher classification accuracy as well as comparably lower latency of joint communication and computing.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 25 Mar 2023.

Builds on

11 of 24 references.

References · 11

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2020Satellite Communications in the New Space Era: A Survey and Future Challengescited by 36
  4. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  5. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  6. 2021Satellite Based Computing Networks with Federated Learningcited by 25
  7. 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
  8. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  9. 2022Five Facets of 6G: Research Challenges and Opportunitiescited by 17
  10. 2020Inter-Plane Inter-Satellite Connectivity in Dense LEO Constellationscited by 12
  11. 2022NGSO Constellation Design for Global Connectivitycited by 5

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Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2024Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDMcited by 6
  2. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5
  3. 2025Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networkscited by 3
  4. 2024Scheduling for On-Board Federated Learning with Satellite Clusterscited by 2
  5. 2025A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networkscited by 1
  6. 2025Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networkscited by 1