mlsanityData to 7 Oct 2026

Ground-Assisted Federated Learning in LEO Satellite Constellations

arXiv 2109.01348 ↗First posted 3 Sep 2021eess.SP cs.LGcited by 31

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Abstract (arXiv)

In Low Earth Orbit (LEO) mega constellations, there are relevant use cases, such as inference based on satellite imaging, in which a large number of satellites collaboratively train a machine learning model without sharing their local datasets. To address this problem, we propose a new set of algorithms based on Federated learning (FL), including a novel asynchronous FL procedure based on FedAvg that exhibits better robustness against heterogeneous scenarios than the state-of-the-art. Extensive numerical evaluations based on MNIST and CIFAR-10 datasets highlight the fast convergence speed and excellent asymptotic test accuracy of the proposed method.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 3 Sep 2021.

Builds on

10 of 29 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  6. 2019Asynchronous Federated Optimizationcited by 264
  7. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
  8. 2019Convergence of Edge Computing and Deep Learning: A Comprehensive Surveycited by 120
  9. 2020Satellite Communications in the New Space Era: A Survey and Future Challengescited by 36
  10. 2019LEO Small-Satellite Constellations for 5G and Beyond-5G Communicationscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 7; 2024: 9; 2025: 5; 2026: 6.

31 papers.

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  1. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  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. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  8. 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
  9. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
  10. 2023Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellationcited by 6
  11. 2024Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDMcited by 6
  12. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
19 more
  1. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5
  2. 2024Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approachcited by 4
  3. 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
  4. 2024Scheduling for On-Board Federated Learning with Satellite Clusterscited by 2
  5. 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
  6. 2023Hierarchical Multi-Agent Multi-Armed Bandit for Resource Allocation in Multi-LEO Satellite Constellation Networkscited by 1
  7. 2024Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellationscited by 1
  8. 2025Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networkscited by 1
  9. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 1
  10. 2025Bringing Federated Learning to Spacecited by 1
  11. 2024Sparse Incremental Aggregation in Multi-Hop Federated Learningcited by 0
  12. 2025Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networkscited by 0
  13. 2025Intelligent Spectrum Management in Satellite Communicationscited by 0
  14. 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellationscited by 0
  15. 2026Constraint-Aware Execution Planning for Hybrid Space-Ground Compute Workloadscited by 0
  16. 2026FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellationscited by 0
  17. 2026FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellationscited by 0
  18. 2026ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removalcited by 0
  19. 2026FractalNet-Based Heterogeneous Federated Learning for Orbital Edge Intelligence in Satellite Mega-Constellations: A Wildfire Case Studycited by 0