Edge-Only Universal Adversarial Attacks in Distributed Learning

Abstract

Distributed learning frameworks, which partition neural network models across multiple computing nodes, enhance efficiency in collaborative edge-cloud systems, but may also introduce new vulnerabilities to evasion attacks, often in the form of adversarial perturbations. In this work, we present a new threat model that explores the feasibility of generating universal adversarial perturbations (UAPs) when the attacker has access only to the edge portion of the model, consisting of its initial network layers. Unlike traditional attacks that require full model knowledge, our approach shows that adversaries can induce effective mispredictions in the unknown cloud component by manipulating key feature representations at the edge. Following the proposed threat model, we introduce both edge-only untargeted and targeted formulations of UAPs designed to control intermediate features before the split point. Our results on ImageNet demonstrate strong attack transferability to the unknown cloud part, and we compare the proposed method with classical white-box and black-box techniques, highlighting its effectiveness. Additionally, we analyze the capability of an attacker to achieve targeted adversarial effects with edge-only knowledge, revealing intriguing behaviors across multiple networks. By introducing the first adversarial attacks with edge-only knowledge in split inference, this work underscores the importance of addressing partial model access in adversarial robustness, encouraging further research in this area.