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 PSL 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.

本研究开发了一种新型的并行 U 形分割学习方法，并设计了最佳化资源分配方案，以提高边缘网络的性能。实验结果表明，该方案能够在保护标签隐私的同时，实现与其他分割学习基线模型类似的性能。