mlsanityData to 7 Oct 2026

FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs

arXiv 2205.07216 ↗First posted 15 May 2022cs.LG cs.NI eess.SPcited by 21

Not on the map. Why?

Abstract (arXiv)

Low Earth Orbit (LEO) satellite constellations have seen a surge in deployment over the past few years by virtue of their ability to provide broadband Internet access as well as to collect vast amounts of Earth observational data that can be utilized to develop AI on a global scale. As traditional machine learning (ML) approaches that train a model by downloading satellite data to a ground station (GS) are not practical, Federated Learning (FL) offers a potential solution. However, existing FL approaches cannot be readily applied because of their excessively prolonged training time caused by the challenging satellite-GS communication environment. This paper proposes FedHAP, which introduces high-altitude platforms (HAPs) as distributed parameter servers (PSs) into FL for Satcom (or more concretely LEO constellations), to achieve fast and efficient model training. FedHAP consists of three components: 1) a hierarchical communication architecture, 2) a model dissemination algorithm, and 3) a model aggregation algorithm. Our extensive simulations demonstrate that FedHAP significantly accelerates FL model convergence as compared to state-of-the-art baselines, cutting the training time from several days down to a few hours, yet achieving higher accuracy.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 15 May 2022.

Builds on

11 of 17 references.

References · 11

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Asynchronous Federated Optimizationcited by 264
  4. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
  5. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  6. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  7. 2021Satellite Based Computing Networks with Federated Learningcited by 25
  8. 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
  9. 2020A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Futurecited by 14
  10. 2020Inter-Plane Inter-Satellite Connectivity in Dense LEO Constellationscited by 12
  11. 2020Point-to-Point Communication in Integrated Satellite-Aerial Networks: State-of-the-art and Future Challengescited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 5; 2025: 6; 2026: 2.

21 papers.

Sort:

  1. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  2. 2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
  3. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  4. 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
  5. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
  6. 2023Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregationcited by 6
  7. 2024Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDMcited by 6
  8. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  9. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5
  10. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  11. 2024Scheduling for On-Board Federated Learning with Satellite Clusterscited by 2
  12. 2025Federated Learning-Distillation Alternation for Resource-Constrained IoTcited by 2
9 more
  1. 2023FLSTRA: Federated Learning in Stratospherecited by 1
  2. 2024Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellationscited by 1
  3. 2025Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networkscited by 1
  4. 2025Bringing Federated Learning to Spacecited by 1
  5. 2025Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networkscited by 1
  6. 2024Heterogeneity: An Open Challenge for Federated On-board Machine Learningcited by 0
  7. 2025Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networkscited by 0
  8. 2026FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellationscited by 0
  9. 2026FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellationscited by 0