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

arXiv: https://arxiv.org/abs/2411.06773 · HTML: https://mlsanity.com/paper/2411.06773/

## 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 (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [Split Learning over Wireless Networks: Parallel Design and Resource Management](https://mlsanity.com/paper/2204.08119/) (2022)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Split Learning in 6G Edge Networks](https://mlsanity.com/paper/2306.12194/) (2023)
- [Communication-Computation Trade-Off in Resource-Constrained Edge Inference](https://mlsanity.com/paper/2006.02166/) (2020)
- [Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing](https://mlsanity.com/paper/1510.00888/) (2015)
- [A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networks](https://mlsanity.com/paper/2212.06107/) (2022)
- [Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things](https://mlsanity.com/paper/2103.02762/) (2021)
- [Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles](https://mlsanity.com/paper/2210.15496/) (2022)
- [Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare](https://mlsanity.com/paper/2012.12591/) (2020)
- [Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance](https://mlsanity.com/paper/2109.09246/) (2021)
- [Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning](https://mlsanity.com/paper/2209.02428/) (2022)
- [Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning](https://mlsanity.com/paper/2311.09441/) (2023)
- [C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning](https://mlsanity.com/paper/2207.12397/) (2022)
- [Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks](https://mlsanity.com/paper/2001.09223/) (2020)
- [Ultra-Reliable Low-Latency Communications in Autonomous Vehicular Networks](https://mlsanity.com/paper/1903.01863/) (2019)

## Built on by (most cited first, 6 of 6)

- [Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning](https://mlsanity.com/paper/2501.01828/) (2025)
- [Split Learning in Computer Vision for Semantic Segmentation Delay Minimization](https://mlsanity.com/paper/2412.14272/) (2024)
- [Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning](https://mlsanity.com/paper/2504.09114/) (2025)
- [Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture, Challenges, and Case Study](https://mlsanity.com/paper/2603.04936/) (2026)
- [Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap](https://mlsanity.com/paper/2603.13570/) (2026)
- [QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning](https://mlsanity.com/paper/2606.09869/) (2026)

## 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.