mlsanityData to 7 Oct 2026

SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework

arXiv 2409.13503 ↗First posted 20 Sep 2024cs.DC cs.AI cs.LGcited by 21

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Abstract (arXiv)

Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, where coverage limitations and increasing bandwidth congestion significantly hinder model convergence. Fortunately, the advancement of low-Earth orbit (LEO) satellite networks offers promising new communication avenues to augment traditional terrestrial FL. Despite this potential, the limited satellite-ground communication bandwidth and the heterogeneous operating environments of ground devices-including variations in data, bandwidth, and computing power-pose substantial challenges for effective and robust satellite-assisted FL. To address these challenges, we propose SatFed, a resource-efficient satellite-assisted heterogeneous FL framework. SatFed implements freshness-based model prioritization queues to optimize the use of highly constrained satellite-ground bandwidth, ensuring the transmission of the most critical models. Additionally, a multigraph is constructed to capture real-time heterogeneous relationships between devices, including data distribution, terrestrial bandwidth, and computing capability. This multigraph enables SatFed to aggregate satellite-transmitted models into peer guidance, enhancing local training in heterogeneous environments. Extensive experiments with real-world LEO satellite networks demonstrate that SatFed achieves superior performance and robustness compared to state-of-the-art benchmarks.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 32 of its 64 references that are in the map. First posted on arXiv 20 Sep 2024.

Builds on

32 of 64 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2017Federated Multi-Task Learningcited by 669
  7. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  8. 2021Towards Personalized Federated Learningcited by 462
  9. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  10. 2019Asynchronous Federated Optimizationcited by 264
  11. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
  12. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
20 more
  1. 2023Split Learning in 6G Edge Networkscited by 68
  2. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  3. 2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 60
  4. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  5. 2024AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learningcited by 40
  6. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  7. 2023AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Drivingcited by 32
  8. 2024Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunitiescited by 32
  9. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  10. 2023Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Schemecited by 19
  11. 2023Federated Learning under Heterogeneous and Correlated Client Availabilitycited by 18
  12. 2024D2-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communicationscited by 18
  13. 2024PACP: Priority-Aware Collaborative Perception for Connected and Autonomous Vehiclescited by 17
  14. 2024IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformercited by 16
  15. 2024SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensingcited by 14
  16. 2022Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learningcited by 11
  17. 2024LEO Satellite Networks Assisted Geo-distributed Data Processingcited by 10
  18. 2023Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Drivingcited by 9
  19. 2022The Digital Divide in Canada and the Role of LEO Satellites in Bridging the Gapcited by 3
  20. 2024Privacy-preserving Universal Adversarial Defense for Black-box Modelscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 8; 2026: 9.

21 papers.

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  1. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  2. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 30
  3. 2024IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengerscited by 20
  4. 2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Expertscited by 11
  5. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 7
  6. 2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attackscited by 7
  7. 2025Rethinking Membership Inference Attacks Against Transfer Learningcited by 5
  8. 2026Conflict-Aware Client Selection for Multi-Server Federated Learningcited by 5
  9. 2025A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observationcited by 2
  10. 2024t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Drivingcited by 1
  11. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 1
  12. 2025Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computingcited by 1
9 more
  1. 2026SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compressioncited by 1
  2. 2026Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classificationcited by 1
  3. 2024Tracing Human Stress from Physiological Signals using UWB Radarcited by 0
  4. 2025DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protectioncited by 0
  5. 2026SIDeR: Semantic Identity Decoupling for Unrestricted Face Privacycited by 0
  6. 2026MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellitescited by 0
  7. 2026Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Expertscited by 0
  8. 2026SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoningcited by 0
  9. 2026Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimizationcited by 0