mlsanityData to 7 Oct 2026

AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms

arXiv 2212.11522 ↗First posted 22 Dec 2022cs.LG cs.DC cs.NIcited by 18

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Abstract (arXiv)

Low Earth Orbit (LEO) constellations, each comprising a large number of satellites, have become a new source of big data "from the sky". Downloading such data to a ground station (GS) for big data analytics demands very high bandwidth and involves large propagation delays. Federated Learning (FL) offers a promising solution because it allows data to stay in-situ (never leaving satellites) and it only needs to transmit machine learning model parameters (trained on the satellites' data). However, the conventional, synchronous FL process can take several days to train a single FL model in the context of satellite communication (Satcom), due to a bottleneck caused by straggler satellites. In this paper, we propose an asynchronous FL framework for LEO constellations called AsyncFLEO to improve FL efficiency in Satcom. Not only does AsynFLEO address the bottleneck (idle waiting) in synchronous FL, but it also solves the issue of model staleness caused by straggler satellites. AsyncFLEO utilizes high-altitude platforms (HAPs) positioned "in the sky" as parameter servers, and consists of three technical components: (1) a ring-of-stars communication topology, (2) a model propagation algorithm, and (3) a model aggregation algorithm with satellite grouping and staleness discounting. Our extensive evaluation with both IID and non-IID data shows that AsyncFLEO outperforms the state of the art by a large margin, cutting down convergence delay by 22 times and increasing accuracy by 40%.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 22 Dec 2022.

Builds on

8 of 15 references.

References · 8

  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. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  5. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  6. 2021Satellite Based Computing Networks with Federated Learningcited by 25
  7. 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
  8. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 4; 2025: 5; 2026: 3.

18 papers.

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  1. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  2. 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
  3. 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
  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. 2024Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDMcited by 6
  8. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5
  9. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  10. 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
  11. 2025Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networkscited by 1
  12. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 1
6 more
  1. 2025Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networkscited by 1
  2. 2025Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networkscited by 0
  3. 2025orb-QFL: Orbital Quantum Federated Learningcited by 0
  4. 2026Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddingscited by 0
  5. 2026FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellationscited by 0
  6. 2026FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellationscited by 0