mlsanityData to 14 Oct 2026

SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework

arXiv 2504.13479 ↗First posted 18 Apr 2025cs.NI cs.DC cs.LGcited by 2

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Abstract (arXiv)

Recently, the rapid development of LEO satellite networks spurs another widespread concern-data processing at satellites. However, achieving efficient computation at LEO satellites in highly dynamic satellite networks is challenging and remains an open problem when considering the constrained computation capability of LEO satellites. For the first time, we propose a novel distributed learning framework named SFL-LEO by combining Federated Learning (FL) with Split Learning (SL) to accommodate the high dynamics of LEO satellite networks and the constrained computation capability of LEO satellites by leveraging the periodical orbit traveling feature. The proposed scheme allows training locally by introducing an asynchronous training strategy, i.e., achieving local update when LEO satellites disconnect with the ground station, to provide much more training space and thus increase the training performance. Meanwhile, it aggregates client-side sub-models at the ground station and then distributes them to LEO satellites by borrowing the idea from the federated learning scheme. Experiment results driven by satellite-ground bandwidth measured in Starlink demonstrate that SFL-LEO provides a similar accuracy performance with the conventional SL scheme because it can perform local training even within the disconnection duration.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 50 references that are in the map. First posted on arXiv 18 Apr 2025.

Builds on

39 of 50 references.

References · 39

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27 more
  1. 2022Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customizationcited by 35
  2. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  3. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  4. 2025A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluationscited by 27
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  16. 2025Constructing 4D Radio Map in LEO Satellite Networks with Limited Samplescited by 13
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  27. 2024Efficient Satellite-Ground Interconnection Design for Low-orbit Mega-Constellation Topologycited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellationscited by 0
  2. 2026A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizationscited by 0