In the realm of the Internet of Things (IoT), deploying deep learning models to process data generated or collected by IoT devices is a critical challenge. However, direct data transmission can cause network congestion and inefficient execution, given that IoT devices typically lack computation and communication capabilities. Centralized data processing in data centers is also no longer feasible due to concerns over data privacy and security. To address these challenges, we present an innovative Edge-assisted U-Shaped Split Federated Learning (EUSFL) framework, which harnesses the high-performance capabilities of edge servers to assist IoT devices in model training and optimization process. In this framework, we leverage Federated Learning (FL) to enable data holders to collaboratively train models without sharing their data, thereby enhancing data privacy protection by transmitting only model parameters. Additionally, inspired by Split Learning (SL), we split the neural network into three parts using U-shaped splitting for local training on IoT devices. By exploiting the greater computation capability of edge servers, our framework effectively reduces overall training time and allows IoT devices with varying capabilities to perform training tasks efficiently. Furthermore, we proposed a novel noise mechanism called LabelDP to ensure that data features and labels can securely resist reconstruction attacks, eliminating the risk of privacy leakage. Our theoretical analysis and experimental results demonstrate that EUSFL can be integrated with various aggregation algorithms, maintaining good performance across different computing capabilities of IoT devices, and significantly reducing training time and local computation overhead.

翻译：在物联网领域，部署深度学习模型处理物联网设备生成或采集的数据是一项关键挑战。然而，由于物联网设备通常缺乏计算和通信能力，直接传输数据可能导致网络拥塞和执行效率低下。同时，出于数据隐私和安全考虑，在数据中心进行集中式数据处理也已不再可行。为解决这些挑战，我们提出了一种创新的边缘辅助U型分割联邦学习（EUSFL）框架，该框架利用边缘服务器的高性能能力来辅助物联网设备进行模型训练与优化。在该框架中，我们利用联邦学习使数据持有者无需共享数据即可协作训练模型，通过仅传输模型参数来增强数据隐私保护。此外，受分割学习的启发，我们采用U型分割方法将神经网络划分为三部分，在物联网设备上进行本地训练。通过利用边缘服务器更强的计算能力，该框架有效减少了总体训练时间，并使不同能力的物联网设备都能高效执行训练任务。最后，我们提出了一种名为LabelDP的新型噪声机制，确保数据特征和标签能够安全抵御重构攻击，消除隐私泄露风险。理论分析和实验结果表明，EUSFL可与多种聚合算法集成，在物联网设备不同计算能力下均保持良好性能，并显著降低训练时间与本地计算开销。