mlsanityData to 14 Oct 2026

Satellite Based Computing Networks with Federated Learning

arXiv 2111.10586 ↗First posted 20 Nov 2021eess.SP cs.LGcited by 25

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Abstract (arXiv)

Driven by the ever-increasing penetration and proliferation of data-driven applications, a new generation of wireless communication, the sixth-generation (6G) mobile system enhanced by artificial intelligence (AI), has attracted substantial research interests. Among various candidate technologies of 6G, low earth orbit (LEO) satellites have appealing characteristics of ubiquitous wireless access. However, the costs of satellite communication (SatCom) are still high, relative to counterparts of ground mobile networks. To support massively interconnected devices with intelligent adaptive learning and reduce expensive traffic in SatCom, we propose federated learning (FL) in LEO-based satellite communication networks. We first review the state-of-the-art LEO-based SatCom and related machine learning (ML) techniques, and then analyze four possible ways of combining ML with satellite networks. The learning performance of the proposed strategies is evaluated by simulation and results reveal that FL-based computing networks improve the performance of communication overheads and latency. Finally, we discuss future research topics along this research direction.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 20 Nov 2021.

Builds on

5 of 17 references.

References · 5

  1. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  2. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  3. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 153
  4. 2020Satellite Communications in the New Space Era: A Survey and Future Challengescited by 36
  5. 2020Vision, Requirements, and Technology Trend of 6G: How to Tackle the Challenges of System Coverage, Capacity,User Data-Rate and Movement Speedcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 6; 2024: 7; 2025: 5; 2026: 2.

25 papers.

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  1. 2024Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmissioncited by 35
  2. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  3. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  4. 2022Federated Learning in Satellite Constellationscited by 21
  5. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  6. 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
  7. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
  8. 2023Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellationcited by 6
  9. 2023Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregationcited by 6
  10. 2024Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDMcited by 6
  11. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  12. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5
13 more
  1. 2024From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networkscited by 4
  2. 2022Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compressioncited by 3
  3. 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
  4. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 2
  5. 2023Hierarchical Multi-Agent Multi-Armed Bandit for Resource Allocation in Multi-LEO Satellite Constellation Networkscited by 1
  6. 2024Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellationscited by 1
  7. 2024Heterogeneity: An Open Challenge for Federated On-board Machine Learningcited by 0
  8. 2025Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Predictioncited by 0
  9. 2025Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networkscited by 0
  10. 2025Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systemscited by 0
  11. 2025orb-QFL: Orbital Quantum Federated Learningcited by 0
  12. 2026Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddingscited by 0
  13. 2026SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learningcited by 0