As edge computing and the Internet of Things (IoT) expand, horizontal collaboration (HC) emerges as a distributed data processing solution for resource-constrained devices. In particular, a convolutional neural network (CNN) model can be deployed on multiple IoT devices, allowing distributed inference execution for image recognition while ensuring model and data privacy. Yet, this distributed architecture remains vulnerable to adversaries who want to make subtle alterations that impact the model, even if they lack access to the entire model. Such vulnerabilities can have severe implications for various sectors, including healthcare, military, and autonomous systems. However, security solutions for these vulnerabilities have not been explored. This paper presents a novel framework for Secure Horizontal Edge with Adversarial Threat Handling (SHEATH) to detect adversarial noise and eliminate its effect on CNN inference by recovering the original feature maps. Specifically, SHEATH aims to address vulnerabilities without requiring complete knowledge of the CNN model in HC edge architectures based on sequential partitioning. It ensures data and model integrity, offering security against adversarial attacks in diverse HC environments. Our evaluations demonstrate SHEATH's adaptability and effectiveness across diverse CNN configurations.

翻译：随着边缘计算和物联网（IoT）的扩展，水平协作（HC）作为一种分布式数据处理方案，为资源受限设备提供了解决方案。特别是，卷积神经网络（CNN）模型可以部署在多个物联网设备上，实现图像识别的分布式推理执行，同时确保模型和数据隐私。然而，这种分布式架构仍然容易受到攻击者的威胁，他们可能进行细微的修改以影响模型，即使无法访问整个模型。此类漏洞可能对医疗、军事和自主系统等多个领域产生严重影响。然而，针对这些漏洞的安全解决方案尚未得到充分探索。本文提出了一种新颖的框架——安全水平边缘对抗威胁处理（SHEATH），旨在通过恢复原始特征图来检测对抗性噪声并消除其对CNN推理的影响。具体而言，SHEATH旨在解决基于顺序分区的HC边缘架构中的漏洞，而无需完全了解CNN模型。它确保数据和模型的完整性，为多种HC环境提供对抗攻击的安全性。我们的评估证明了SHEATH在不同CNN配置中的适应性和有效性。