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

arXiv: https://arxiv.org/abs/2401.00685 · HTML: https://mlsanity.com/paper/2401.00685/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 1 Jan 2024.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Ground-Assisted Federated Learning in LEO Satellite Constellations](https://mlsanity.com/paper/2109.01348/) (2021)
- [Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints](https://mlsanity.com/paper/2205.06730/) (2022)
- [FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations](https://mlsanity.com/paper/2202.01267/) (2022)
- [Satellite Based Computing Networks with Federated Learning](https://mlsanity.com/paper/2111.10586/) (2021)
- [On-Board Federated Learning for Dense LEO Constellations](https://mlsanity.com/paper/2111.12769/) (2021)
- [FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs](https://mlsanity.com/paper/2205.07216/) (2022)
- [AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms](https://mlsanity.com/paper/2212.11522/) (2022)
- [A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future](https://mlsanity.com/paper/2007.15088/) (2020)
- [Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling](https://mlsanity.com/paper/2302.13447/) (2023)
- [FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation](https://mlsanity.com/paper/2304.08537/) (2023)
- [Improvement of the Global Connectivity using Integrated Satellite-Airborne-Terrestrial Networks with Resource Optimization](https://mlsanity.com/paper/1912.03828/) (2019)
- [One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes](https://mlsanity.com/paper/2305.12316/) (2023)
- [Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation](https://mlsanity.com/paper/2303.16071/) (2023)
- [Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation](https://mlsanity.com/paper/2309.01828/) (2023)
- [Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2212.01215/) (2022)
- [A Tutorial on Nonorthogonal Multiple Access for 5G and Beyond](https://mlsanity.com/paper/1902.08992/) (2019)

## Built on by (most cited first, 6 of 6)

- [LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks](https://mlsanity.com/paper/2501.01293/) (2025)
- [Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning](https://mlsanity.com/paper/2401.15541/) (2024)
- [Energy-Aware Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2409.14832/) (2024)
- [Landmark-based Localization using Stereo Vision and Deep Learning in GPS-Denied Battlefield Environment](https://mlsanity.com/paper/2402.12551/) (2024)
- [Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks](https://mlsanity.com/paper/2512.08882/) (2025)
- [orb-QFL: Orbital Quantum Federated Learning](https://mlsanity.com/paper/2509.16505/) (2025)

## 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.