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拥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，因为尾部模型（包含最后几层）需部署在服务器端。为克服这一限制，一种可行的解决方案是采用U形架构，将早期层和最后层均保留在用户侧。本文提出了一种新颖的并行U形分割学习框架，并设计了最优资源优化方案以提升边缘网络性能。在所提出的框架中，多个用户与边缘服务器协作完成SL。我们分析了训练过程中每个客户端的端到端延迟，并设计了一种高效资源分配算法LSCRA，该算法能够找到最优的计算资源分配方案与分割层。实验结果表明，LSCRA具有有效性，且U形并行分割学习在保证标签隐私的同时，能达到与其他SL基线模型相似的性能。索引词：U形网络、分割学习、标签隐私、资源分配、5G/6G边缘网络。