mlsanityData to 14 Oct 2026

Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks

arXiv 2411.06773 ↗First posted 11 Nov 2024cs.LG cs.DCcited by 6

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Abstract (arXiv)

The integration of autonomous driving technologies with vehicular networks presents significant challenges in privacy preservation, communication efficiency, and resource allocation. This paper proposes a novel U-shaped split federated learning (U-SFL) framework to address these challenges on the way of realizing in vehicular edge networks. U-SFL is able to enhance privacy protection by keeping both raw data and labels on the vehicular user (VU) side while enabling parallel processing across multiple vehicles. To optimize communication efficiency, we introduce a semantic-aware auto-encoder (SAE) that significantly reduces the dimensionality of transmitted data while preserving essential semantic information. Furthermore, we develop a deep reinforcement learning (DRL) based algorithm to solve the NP-hard problem of dynamic resource allocation and split point selection. Our comprehensive evaluation demonstrates that U-SFL achieves comparable classification performance to traditional split learning (SL) while substantially reducing data transmission volume and communication latency. The proposed DRL-based optimization algorithm shows good convergence in balancing latency, energy consumption, and learning performance.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 21 of its 41 references that are in the map. First posted on arXiv 11 Nov 2024.

Builds on

21 of 41 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  4. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  5. 2018Distributed learning of deep neural network over multiple agentscited by 279
  6. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  7. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  8. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  9. 2023Split Learning in 6G Edge Networkscited by 68
  10. 2020Communication-Computation Trade-Off in Resource-Constrained Edge Inferencecited by 60
  11. 2015Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computingcited by 28
  12. 2022A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networkscited by 22
9 more
  1. 2021Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Thingscited by 20
  2. 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
  3. 2020Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcarecited by 14
  4. 2021Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performancecited by 11
  5. 2022Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learningcited by 11
  6. 2023Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learningcited by 9
  7. 2022C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learningcited by 8
  8. 2020Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networkscited by 7
  9. 2019Ultra-Reliable Low-Latency Communications in Autonomous Vehicular Networkscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2025Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learningcited by 3
  2. 2024Split Learning in Computer Vision for Semantic Segmentation Delay Minimizationcited by 2
  3. 2025Deploying Large AI Models on Resource-Limited Devices with Split Federated Learningcited by 1
  4. 2026Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture, Challenges, and Case Studycited by 0
  5. 2026Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmapcited by 0
  6. 2026QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach
  2. 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
  3. 2024Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
  4. 2024Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
  5. 2025Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures
  6. 2024DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
  7. 2024Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation
  8. 2026Bridging Network Fragmentation: A Semantic-Augmented DRL Framework for UAV-aided VANETs