mlsanityData to 7 Oct 2026

Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling

arXiv 2302.13447 ↗First posted 27 Feb 2023cs.LG cs.NIcited by 10

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Abstract (arXiv)

The advances in satellite technology developments have recently seen a large number of small satellites being launched into space on Low Earth orbit (LEO) to collect massive data such as Earth observational imagery. The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS. Satellite edge computing (SEC), on the other hand, allows each satellite to train an ML model onboard and uploads only the model to the GS which appears to be a promising concept. This paper proposes FedLEO, a novel federated learning (FL) framework that realizes the concept of SEC and overcomes the limitation (slow convergence) of existing FL-based solutions. FedLEO (1) augments the conventional FL's star topology with ``horizontal'' intra-plane communication pathways in which model propagation among satellites takes place; (2) optimally schedules communication between ``sink'' satellites and the GS by exploiting the predictability of satellite orbiting patterns. We evaluate FedLEO extensively and benchmark it with the state of the art. Our results show that FedLEO drastically expedites FL convergence, without sacrificing -- in fact it considerably increases -- the model accuracy.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 27 Feb 2023.

Builds on

11 of 15 references.

References · 11

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 276
  3. 2019Asynchronous Federated Optimizationcited by 264
  4. 2020Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarmscited by 31
  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. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  10. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  11. 2022Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 2; 2026: 2.

10 papers.

  1. 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
  2. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
  3. 2023Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregationcited by 6
  4. 2024Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDMcited by 6
  5. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  6. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5
  7. 2025Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networkscited by 1
  8. 2025Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networkscited by 1
  9. 2026MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellitescited by 0
  10. 2026Resource-Aware Federated Mixture-of-Experts with Adaptive Pruning for Onboard Learning in LEO Satellite Constellationscited by 0