  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.
