mlsanityData to 14 Oct 2026

Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks

arXiv 2303.15991 ↗First posted 26 Mar 2023cs.LGcited by 95

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Abstract (arXiv)

The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing multiple client devices to offload substantial training workloads to an edge server via layer-wise model split. By observing that existing PSL schemes incur excessive training latency and large volume of data transmissions, we propose an innovative PSL framework, namely, efficient parallel split learning (EPSL), to accelerate model training. To be specific, EPSL parallelizes client-side model training and reduces the dimension of local gradients for back propagation (BP) via last-layer gradient aggregation, leading to a significant reduction in server-side training and communication latency. Moreover, by considering the heterogeneous channel conditions and computing capabilities at client devices, we jointly optimize subchannel allocation, power control, and cut layer selection to minimize the per-round latency. Simulation results show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy compared with the state-of-the-art benchmarks, and the tailored resource management and layer split strategy can considerably reduce latency than the counterpart without optimization.

Key facts. Cited by 95 papers in the map (data through 14 Oct 2026). Builds on 35 of its 71 references that are in the map. First posted on arXiv 26 Mar 2023.

Builds on

35 of 71 references.

References · 35

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2014Going Deeper with Convolutionscited by 4,241
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  6. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  8. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  9. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 193
  10. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  11. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  12. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
23 more
  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  2. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 95
  3. 2015Speeding Up Distributed Machine Learning Using Codescited by 76
  4. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  5. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  6. 2019Toward an Automated Auction Framework for Wireless Federated Learning Services Marketcited by 38
  7. 2023AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Drivingcited by 32
  8. 2022Differentially Private Label Protection in Split Learningcited by 26
  9. 2020Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmissioncited by 23
  10. 2021Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learningcited by 23
  11. 2022A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networkscited by 22
  12. 2021Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inferencecited by 19
  13. 2021Towards Energy Efficient Federated Learning over 5G+ Mobile Devicescited by 16
  14. 2021Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performancecited by 11
  15. 2021RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Networkcited by 11
  16. 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
  17. 2022Actions at the Edge: Jointly Optimizing the Resources in Multi-access Edge Computingcited by 8
  18. 2023Time-sensitive Learning for Heterogeneous Federated Edge Intelligencecited by 8
  19. 2021Spatio-Temporal Split Learningcited by 7
  20. 2021In-network Computation for Large-scale Federated Learning over Wireless Edge Networkscited by 7
  21. 2022Time Minimization in Hierarchical Federated Learningcited by 4
  22. 2018Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-latencycited by 3
  23. 2021JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networkscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 33; 2025: 40; 2026: 17.

95 papers.

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  1. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 128
  2. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  3. 2023Split Learning in 6G Edge Networkscited by 68
  4. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  5. 2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 62
  6. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  7. 2024AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learningcited by 40
  8. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  9. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  10. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  11. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 31
  12. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
38 more
  1. 2024AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Mergingcited by 22
  2. 2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
  3. 2024SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Frameworkcited by 21
  4. 2024IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengerscited by 20
  5. 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devicescited by 16
  6. 2024IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformercited by 16
  7. 2025LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Datacited by 15
  8. 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
  9. 2024SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensingcited by 14
  10. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 14
  11. 2024CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perceptioncited by 13
  12. 2024TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networkscited by 12
  13. 2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Expertscited by 11
  14. 2025Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoringcited by 10
  15. 2025PRISM: Privacy-Aware Routing for Adaptive Cloud-Edge LLM Inference via Semantic Sketch Collaborationcited by 10
  16. 2023Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learningcited by 9
  17. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 8
  18. 2024Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networkscited by 7
  19. 2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attackscited by 7
  20. 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
  21. 2024Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networkscited by 6
  22. 2025Rethinking Membership Inference Attacks Against Transfer Learningcited by 6
  23. 2025SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compressioncited by 6
  24. 2025LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learningcited by 6
  25. 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platformscited by 5
  26. 2024Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencodercited by 4
  27. 2024FedsLLM: Federated Split Learning for Large Language Models over Communication Networkscited by 3
  28. 2024Parallel Split Learning with Global Samplingcited by 3
  29. 2024Privacy-preserving Universal Adversarial Defense for Black-box Modelscited by 3
  30. 2025Visual Fidelity Index for Generative Semantic Communications with Critical Information Embeddingcited by 3
  31. 2025Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generationcited by 3
  32. 2025A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observationcited by 3
  33. 2025Optimizing Model Splitting and Device Task Assignment for Deceptive Signal Assisted Private Multi-hop Split Learningcited by 3
  34. 2025Federated Split Learning with Improved Communication and Storage Efficiencycited by 3
  35. 2025Sample Efficient Experience Replay in Non-stationary Environmentscited by 3
  36. 2026HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Trainingcited by 3
  37. 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
  38. 2025Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edgecited by 2

50 most cited of 95 All 95 (JSON)