车辆边缘网络中用分片学习提升隐私与通信效率
Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks
- 提出U型分片联邦学习框架，数据和标签保留在车端
- 用语义感知编码器降低传输数据量，保持关键信息
- 用深度强化学习动态优化资源分配与分割点
自动驾驶技术与车载网络的融合在隐私保护、通信效率和资源分配方面面临重大挑战。本文提出一种新型的U型分片联邦学习（U-SFL）框架，以应对车载边缘网络中的这些挑战。U-SFL通过将原始数据和标签保留在车辆用户（VU）侧，有效增强隐私保护，并支持多车并行处理。为提升通信效率，引入语义感知自编码器（SAE），显著降低传输数据维度，同时保留关键语义信息。此外，设计基于深度强化学习（DRL）的算法，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在分类性能上可媲美传统分片学习（SL），同时大幅减少数据传输量和通信延迟。所提出的DRL优化算法在平衡延迟、能耗与学习性能方面表现出良好收敛性。
原文摘要 · Abstract (English)
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.
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