aiqbench analysis
This research provides a theoretical framework demonstrating that decentralized adversarial training algorithms can escape local minima faster than centralized strategies under mild attack conditions, potentially leading to flatter and more robust models. The study systematically compares consensus and diffusion approaches against centralized methods, offering critical insights for optimizing distributed learning environments in multi-agent systems. While the findings are significant for improving model robustness, their practical impact depends on managing performance trade-offs as perturbation bounds increase.
On the Escaping Efficiency of Distributed Adversarial Training Algorithms
arXiv:2509.11337v2 Announce Type: replace Abstract: Adversarial training has been widely studied in recent years due to its role in improving model robustness against adversarial attacks. This paper focuses on comparing different distributed adversarial training algorithms--including centralized and decentralized strategies--within multi-agent learning environments. Previous studies have highlighted the importance of model flatness in determining robustness. To this end, we develop a general theoretical framework to study the escaping efficiency of these algorithms from local minima, which is closely related to the flatness of the resulting models. We show that when the perturbation bound is sufficiently small (i.e., when the attack strength is relatively mild) and a large batch size is used, decentralized adversarial training algorithms--including consensus and diffusion--are guaranteed to escape faster from local minima than the centralized strategy, thereby favoring flatter minima. However, as the perturbation bound increases, this trend may no longer hold. In the simulation results, we illustrate our theoretical findings and systematically compare the performance of models obtained through decentralized and centralized adversarial training algorithms. The results highlight the potential of decentralized strategies to enhance the robustness of models in distributed settings.
Abstract:Adversarial training has been widely studied in recent years due to its role in improving model robustness against adversarial attacks. This paper focuses on comparing different distributed adversarial training algorithms--including centralized and decentralized strategies--within multi-agent learning environments. Previous studies have highlighted the importance of model flatness in determining robustness. To this end, we develop a general theoretical framework to study the escaping efficiency of these algorithms from local minima, which is closely related to the flatness of the resulting models. We show that when the perturbation bound is sufficiently small (i.e., when the attack strength is relatively mild) and a large batch size is used, decentralized adversarial training algorithms--including consensus and diffusion--are guaranteed to escape faster from local minima than the centralized strategy, thereby favoring flatter minima. However, as the perturbation bound increases, this trend may no longer hold. In the simulation results, we illustrate our theoretical findings and systematically compare the performance of models obtained through decentralized and centralized adversarial training algorithms. The results highlight the potential of decentralized strategies to enhance the robustness of models in distributed settings.
Machine Learning (cs.LG)
arXiv:2509.11337 [cs.LG]
(or arXiv:2509.11337v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2509.11337 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 272 words
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