mlsanityData to 14 Oct 2026

FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks

arXiv 2311.01483 ↗First posted 2 Nov 2023cs.LG cs.AI cs.DCcited by 38

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Abstract (arXiv)

Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communication but also for various machine learning applications, such as space modulation recognition, remote sensing image classification, etc. However, the ground station (GS) may be incapable of downloading such a large volume of raw sensing data for centralized model training due to the limited contact time with LEO satellites (e.g. 5 minutes). Therefore, federated learning (FL) has emerged as the promising solution to address this problem via on-device training. Unfortunately, to enable FL on LEO satellites, we still face three critical challenges that are i) heterogeneous computing and memory capabilities, ii) limited uplink rate, and iii) model staleness. To this end, we propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites. Specifically, we first present a novel sub-structure scheme to enable heterogeneous local model training considering different computing, memory, and communication constraints on LEO satellites. Additionally, we propose a pseudo-synchronous model aggregation strategy to dynamically schedule model aggregation for compensating model staleness. To further demonstrate the effectiveness of the FedSN, we evaluate it using space modulation recognition and remote sensing image classification tasks by leveraging the data from real-world satellite networks. Extensive experimental results demonstrate that FedSN framework achieves higher accuracy, lower computing, and communication overhead than the state-of-the-art benchmarks and the effectiveness of each components in FedSN.

Key facts. Cited by 38 papers in the map (data through 14 Oct 2026). Builds on 21 of its 45 references that are in the map. First posted on arXiv 2 Nov 2023.

Builds on

21 of 45 references.

References · 21

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  4. 2016Wide Residual Networkscited by 3,070
  5. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 337
  6. 2019Asynchronous Federated Optimizationcited by 267
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  8. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  9. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  10. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  11. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 90
  12. 2023Split Learning in 6G Edge Networkscited by 68
9 more
  1. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  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. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  5. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  6. 2021RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Networkcited by 11
  7. 2022Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 9
  8. 2023FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigationcited by 8
  9. 2022The Digital Divide in Canada and the Role of LEO Satellites in Bridging the Gapcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 18; 2026: 13.

38 papers.

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  1. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  2. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  3. 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
  4. 2024SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensingcited by 14
  5. 2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Expertscited by 11
  6. 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
  7. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 8
  8. 2025LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systemscited by 8
  9. 2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attackscited by 7
  10. 2025Rethinking Membership Inference Attacks Against Transfer Learningcited by 6
  11. 2025LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learningcited by 6
  12. 2026Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneitycited by 4
26 more
  1. 2024Privacy-preserving Universal Adversarial Defense for Black-box Modelscited by 3
  2. 2025Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generationcited by 3
  3. 2025A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observationcited by 3
  4. 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
  5. 2025Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edgecited by 2
  6. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 2
  7. 2024Hierarchical Learning and Computing over Space-Ground Integrated Networkscited by 1
  8. 2025Secure Resource Allocation via Constrained Deep Reinforcement Learningcited by 1
  9. 2025Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networkscited by 1
  10. 2025Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computingcited by 1
  11. 2025A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networkscited by 1
  12. 2025Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networkscited by 1
  13. 2026SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compressioncited by 1
  14. 2026Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classificationcited by 1
  15. 2025Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networkscited by 0
  16. 2025DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protectioncited by 0
  17. 2025Centralized vs. Decentralized Security for Space AI Systems? A New Lookcited by 0
  18. 2026Exploiting Adaptive Channel Pruning for Communication-Efficient Split Learningcited by 0
  19. 2026GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systemscited by 0
  20. 2026MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellitescited by 0
  21. 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellationscited by 0
  22. 2026Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?cited by 0
  23. 2026Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Expertscited by 0
  24. 2026FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellationscited by 0
  25. 2026SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoningcited by 0
  26. 2026Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimizationcited by 0