Split learning (SL) has emerged as a promising approach for model training without revealing the raw data samples from the data owners. However, traditional SL inevitably leaks label privacy as the tail model (with the last layers) should be placed on the server. To overcome this limitation, one promising solution is to utilize U-shaped architecture to leave both early layers and last layers on the user side. In this paper, we develop a novel parallel U-shaped split learning and devise the optimal resource optimization scheme to improve the performance of edge networks. In the proposed framework, multiple users communicate with an edge server for SL. We analyze the end-to-end delay of each client during the training process and design an efficient resource allocation algorithm, called LSCRA, which finds the optimal computing resource allocation and split layers. Our experimental results show the effectiveness of LSCRA and that U-shaped parallel split learning can achieve a similar performance with other SL baselines while preserving label privacy. Index Terms: U-shaped network, split learning, label privacy, resource allocation, 5G/6G edge networks.

翻译：分割学习（SL）已成为一种在不泄露数据所有者原始样本的情况下进行模型训练的有效方法。然而，传统SL不可避免地会泄露标签隐私，因为尾端模型（包含最后几层）必须部署在服务器上。为克服这一局限，一种可行的解决方案是采用U型架构，将前端层和尾端层均保留在用户侧。本文提出了一种新颖的并行U型分割学习框架，并设计了最优资源优化方案以提升边缘网络性能。在所提框架中，多个用户与边缘服务器进行通信以执行分割学习。我们分析了每个客户端在训练过程中的端到端延迟，并设计了一种高效资源分配算法LSCRA，该算法可找到最优的计算资源分配与分割层位置。实验结果表明，LSCRA具有有效性，且U型并行分割学习在保护标签隐私的同时能够达到与其他SL基线方法相似的性能。索引术语：U型网络，分割学习，标签隐私，资源分配，5G/6G边缘网络。