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arXiv · 2303.01936

Multi-Agent Adversarial Training Using Diffusion Learning

Ying Cao·Elsa Rizk·Stefan Vlaski·Ali H. Sayed
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Abstract

This work focuses on adversarial learning over graphs. We propose a general adversarial training framework for multi-agent systems using diffusion learning. We analyze the convergence properties of the proposed scheme for convex optimization problems, and illustrate its enhanced robustness to adversarial attacks.

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Ying Cao, Elsa Rizk, Stefan Vlaski, Ali H. Sayed. 2023-03-03. Multi-Agent Adversarial Training Using Diffusion Learning. https://arxiv.org/abs/2303.01936

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