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.

翻译：自动驾驶技术与车联网的融合在隐私保护、通信效率和资源分配方面提出了重大挑战。本文提出了一种新型U形分割联邦学习（U-SFL）框架，以应对在车联网边缘网络中实现这些目标所面临的挑战。U-SFL通过将原始数据和标签均保留在车载用户（VU）端，同时支持多车辆并行处理，从而增强了隐私保护能力。为优化通信效率，我们引入了语义感知自动编码器（SAE），该编码器在保留关键语义信息的同时显著降低了传输数据的维度。此外，我们开发了一种基于深度强化学习（DRL）的算法，以解决动态资源分配与分割点选择这一NP难问题。综合评估表明，U-SFL在实现与传统分割学习（SL）相当的分类性能的同时，大幅降低了数据传输量和通信延迟。所提出的基于DRL的优化算法在平衡延迟、能耗与学习性能方面表现出良好的收敛性。