Split Learning (SL) is an emerging privacy-preserving machine learning technique that enables resource constrained edge devices to participate in model training by partitioning a model into client-side and server-side sub-models. While SL reduces computational overhead on edge devices, it encounters significant challenges in heterogeneous environments where devices vary in computing resources, communication capabilities, environmental conditions, and privacy requirements. Although recent studies have explored heterogeneous SL frameworks that optimize split points for devices with varying resource constraints, they often neglect personalized privacy requirements and local model customization under varying environmental conditions. To address these limitations, we propose P3SL, a Personalized Privacy-Preserving Split Learning framework designed for heterogeneous, resource-constrained edge device systems. The key contributions of this work are twofold. First, we design a personalized sequential split learning pipeline that allows each client to achieve customized privacy protection and maintain personalized local models tailored to their computational resources, environmental conditions, and privacy needs. Second, we adopt a bi-level optimization technique that empowers clients to determine their own optimal personalized split points without sharing private sensitive information (i.e., computational resources, environmental conditions, privacy requirements) with the server. This approach balances energy consumption and privacy leakage risks while maintaining high model accuracy. We implement and evaluate P3SL on a testbed consisting of 7 devices including 4 Jetson Nano P3450 devices, 2 Raspberry Pis, and 1 laptop, using diverse model architectures and datasets under varying environmental conditions. Experimental results demonstrate that P3SL significantly mitigates privacy leakage risks, reduces system energy consumption by up to 59.12%, and consistently retains high accuracy compared to the state-of-the-art heterogeneous SL system.

Camouflaged object detection aims to identify objects that blend seamlessly with their background, posing a greater challenge compared to general object detection tasks. Due to its ability to recognize camouflaged objects, such detection models hold significant practical value across various fields. To accurately identify camouflaged targets in various complex environments, we designed a dual-guided camouflaged object detection network based on boundary and texture information(BTDGNet). The process consists of two main stages. The first stage is the localization stage, which leverages a convolutional neural network (CNN) to capture boundary and texture information of objects. These features are then fused to achieve coarse localization of the camouflaged objects. In the second stage, the recognition stage, we employ a Transformer to extract global information from the image, enhancing the differentiation between foreground and background. An interactive fusion module is designed to fully exploit and integrate both global and local features, producing precise prediction images. By leveraging boundary and texture information, the model's adaptability to different camouflaged objects is improved. The integration of local and global features enhances the model's detection accuracy from various perspectives, ultimately building a camouflaged object detection model suitable for a wide range of complex scenarios. The proposed method was extensively compared with other state-of-the-art methods across four public datasets, and the results demonstrated superior performance. Furthermore, benefiting from our dual-guidance strategy that leverages both texture and boundary information, our model demonstrates robust performance. We conducted tests on detection tasks across four different domains, and the results confirm that our model can accurately segment camouflaged objects in complex scenes.