mlsanityData to 7 Oct 2026

Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM

arXiv 2401.00685 ↗First posted 1 Jan 2024cs.LG cs.AI cs.DCcited by 6

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Abstract (arXiv)

Space AI has become increasingly important and sometimes even necessary for government, businesses, and society. An active research topic under this mission is integrating federated learning (FL) with satellite communications (SatCom) so that numerous low Earth orbit (LEO) satellites can collaboratively train a machine learning model. However, the special communication environment of SatCom leads to a very slow FL training process up to days and weeks. This paper proposes NomaFedHAP, a novel FL-SatCom approach tailored to LEO satellites, that (1) utilizes high-altitude platforms (HAPs) as distributed parameter servers (PS) to enhance satellite visibility, and (2) introduces non-orthogonal multiple access (NOMA) into LEO to enable fast and bandwidth-efficient model transmissions. In addition, NomaFedHAP includes (3) a new communication topology that exploits HAPs to bridge satellites among different orbits to mitigate the Doppler shift, and (4) a new FL model aggregation scheme that optimally balances models between different orbits and shells. Moreover, we (5) derive a closed-form expression of the outage probability for satellites in near and far shells, as well as for the entire system. Our extensive simulations have validated the mathematical analysis and demonstrated the superior performance of NomaFedHAP in achieving fast and efficient FL model convergence with high accuracy as compared to the state-of-the-art.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 20 of its 38 references that are in the map. First posted on arXiv 1 Jan 2024.

Builds on

20 of 38 references.

References · 20

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 276
  4. 2019Asynchronous Federated Optimizationcited by 264
  5. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  6. 2022Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraintscited by 30
  7. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  8. 2021Satellite Based Computing Networks with Federated Learningcited by 25
  9. 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
  10. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  11. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  12. 2020A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Futurecited by 14
8 more
  1. 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
  2. 2023FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigationcited by 8
  3. 2019Improvement of the Global Connectivity using Integrated Satellite-Airborne-Terrestrial Networks with Resource Optimizationcited by 7
  4. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
  5. 2023Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellationcited by 6
  6. 2023Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregationcited by 6
  7. 2022Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Networkcited by 5
  8. 2019A Tutorial on Nonorthogonal Multiple Access for 5G and Beyondcited by 1

Built on by

Cited by year

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

6 papers.

  1. 2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
  2. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5
  3. 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
  4. 2024Landmark-based Localization using Stereo Vision and Deep Learning in GPS-Denied Battlefield Environmentcited by 1
  5. 2025Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networkscited by 1
  6. 2025orb-QFL: Orbital Quantum Federated Learningcited by 0