Multi-Agent Adversarial Training Using Diffusion Learning
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 31 references that are in the map. First posted on arXiv 3 Mar 2023.
Builds on
21 of 31 references.
References · 21
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
- 2013Intriguing properties of neural networkscited by 5,114
- 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
- 2017Adversarial Examples for Evaluating Reading Comprehension Systemscited by 568
- 2017Robust Adversarial Reinforcement Learningcited by 439
- 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 333
- 2016Adversarial Training Methods for Semi-Supervised Text Classificationcited by 280
- 2019On Gradient Descent Ascent for Nonconvex-Concave Minimax Problemscited by 266
- 2017PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examplescited by 243
- 2019Topology Attack and Defense for Graph Neural Networks: An Optimization Perspectivecited by 196
- 2019Adversarial Robustness through Local Linearizationcited by 146
9 more
- 2019Graph Adversarial Training: Dynamically Regularizing Based on Graph Structurecited by 93
- 2019Adversarial Robustness Against the Union of Multiple Perturbation Modelscited by 88
- 2019Efficient Algorithms for Smooth Minimax Optimizationcited by 75
- 2019Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Ratecited by 22
- 2019GraphDefense: Towards Robust Graph Convolutional Networkscited by 16
- 2019Distributed Learning in Non-Convex Environments -- Part II: Polynomial Escape from Saddle-Pointscited by 13
- 2021Concurrent Adversarial Learning for Large-Batch Trainingcited by 11
- 2022Distributed Adversarial Training to Robustify Deep Neural Networks at Scalecited by 9
- 2017Performance Limits of Stochastic Sub-Gradient Learning, Part II: Multi-Agent Casecited by 3
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Citing papers per year: 2023: 2.
2 papers.