Abstract
The vulnerability of machine learning models to adversarial attacks has been attracting considerable attention in recent years. Most existing studies focus on the behavior of stand-alone single-agent learners. In comparison, this work studies adversarial training over graphs, where individual agents are subjected to perturbations of varied strength levels across space. It is expected that interactions by linked agents, and the heterogeneity of the attack models that are possible over the graph, can help enhance robustness in view of the coordination power of the group. Using a min-max formulation of diffusion learning, we develop a decentralized adversarial training framework for multi-agent systems. We analyze the convergence properties of the proposed scheme for both convex and non-convex environments, and illustrate the enhanced robustness to adversarial attacks.
Abstract (translated)
机器学习模型对对抗攻击的脆弱性近年来吸引了相当大的注意力。大多数现有研究都关注单个独立学习器的行为。相比之下，本研究研究在图形上的对抗训练，个体 agents 在空间中受到各种强度水平的变化影响。预计通过连接agents的互动，以及在图形上的攻击模型的多样性，可以改善群体协调力，从而增强鲁棒性。使用扩散学习的最小最大定义，我们开发了分布式对抗训练框架，为多Agent系统。我们对 proposed scheme 在凸环境和非凸环境下的收敛性质进行了分析，并展示了对对抗攻击的增强鲁棒性。
URL
https://arxiv.org/abs/2303.13326
