mlsanityData to 7 Oct 2026

Satellite Federated Edge Learning: Architecture Design and Convergence Analysis

arXiv 2404.01875 ↗First posted 2 Apr 2024eess.SP cs.DC cs.ITcited by 10

Not on the map. Why?

Abstract (arXiv)

The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL. Notably, frequent model transmission between the satellites and ground incurs prolonged waiting time and large transmission latency. This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to LEO mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation, which enhances transmission efficiency. Theoretical convergence analysis is provided to characterize the algorithm performance. Extensive simulations show that our FEDMEGA algorithm outperforms existing satellite FEEL algorithms, exhibiting an approximate 30% improvement in convergence rate.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 30 of its 66 references that are in the map. First posted on arXiv 2 Apr 2024.

Builds on

30 of 66 references.

References · 30

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18 more
  1. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  2. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  3. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  4. 2020Federated Edge Learning : Design Issues and Challengescited by 26
  5. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  6. 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
  7. 2022Federated Learning in Satellite Constellationscited by 21
  8. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  9. 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
  10. 2023Fast model inference and training on-board of Satellitescited by 10
  11. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  12. 2022Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 9
  13. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  14. 2021GPU4S: Embedded GPUs in Space -- Latest Project Updatescited by 6
  15. 2022Future Space Networks: Toward the Next Giant Leap for Humankindcited by 6
  16. 2022Selected Trends in Artificial Intelligence for Space Applicationscited by 5
  17. 2022Creating a Safety Assurance Case for an ML Satellite-Based Wildfire Detection and Alert Systemcited by 1
  18. 2022Network-Wide Task Offloading With LEO Satellites: A Computation and Transmission Fusion Approachcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 3.

10 papers.

  1. 2025Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologiescited by 4
  2. 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
  3. 2024Hierarchical Learning and Computing over Space-Ground Integrated Networkscited by 1
  4. 2025Microservice Deployment in Space Computing Power Networks via Robust Reinforcement Learningcited by 1
  5. 2025Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networkscited by 1
  6. 2025Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networkscited by 0
  7. 2025Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systemscited by 0
  8. 2026Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learningcited by 0
  9. 2026Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?cited by 0
  10. 2026ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removalcited by 0