As Machine Learning (ML) is increasingly used in solving various tasks in
real-world applications, it is crucial to ensure that ML algorithms are robust
to any potential worst-case noises, adversarial attacks, and highly unusual
situations when they are designed. Studying ML robustness will significantly
help in the design of ML algorithms. In this paper, we investigate ML
robustness using adversarial training in centralized and decentralized
environments, where ML training and testing are conducted in one or multiple
computers. In the centralized environment, we achieve a test accuracy of 65.41%
and 83.0% when classifying adversarial examples generated by Fast Gradient Sign
Method and DeepFool, respectively. Comparing to existing studies, these results
demonstrate an improvement of 18.41% for FGSM and 47% for DeepFool. In the
decentralized environment, we study Federated learning (FL) robustness by using
adversarial training with independent and identically distributed (IID) and
non-IID data, respectively, where CIFAR-10 is used in this research. In the IID
data case, our experimental results demonstrate that we can achieve such a
robust accuracy that it is comparable to the one obtained in the centralized
environment. Moreover, in the non-IID data case, the natural accuracy drops
from 66.23% to 57.82%, and the robust accuracy decreases by 25% and 23.4% in
C&W and Projected Gradient Descent (PGD) attacks, compared to the IID data
case, respectively. We further propose an IID data-sharing approach, which
allows for increasing the natural accuracy to 85.04% and the robust accuracy
from 57% to 72% in C&W attacks and from 59% to 67% in PGD attacks.

研究了机器学习的鲁棒性，使用集中式和分散式环境进行对抗训练，结果显示比现有研究提高了 18.41% 和 47% 的准确性，并提出了独立同分布和非独立同分布数据的 IID 数据共享方法，可提高自然准确性和鲁棒准确性。

通过对抗性训练提高机器学习的稳健性

Improving Machine Learning Robustness via Adversarial Training

We introduce a two-player contest for evaluating the safety and robustness of
machine learning systems, with a large prize pool. Unlike most prior work in ML
robustness, which studies norm-constrained adversaries, we shift our focus to
unconstrained adversaries. Defenders submit machine learning models, and try to
achieve high accuracy and coverage on non-adversarial data while making no
confident mistakes on adversarial inputs. Attackers try to subvert defenses by
finding arbitrary unambiguous inputs where the model assigns an incorrect label
with high confidence. We propose a simple unambiguous dataset ("bird-or-
bicycle") to use as part of this contest. We hope this contest will help to
more comprehensively evaluate the worst-case adversarial risk of machine
learning models.

本研究引入了一个两人对弈的竞赛，用于评估机器学习系统的安全性和鲁棒性，针对非范数约束的对手进行研究。 防御方提交机器学习模型，试图在非对手数据上实现高准确性和覆盖率，并在对抗性输入上没有自信错误。 攻击者试图通过寻找任意的明确输入，在其高置信度下将错误标签分配给模型来破坏防御。 我们提出了一个简单的明确数据集（“鸟或自行车”）作为本竞赛的一部分。 我们希望这个竞赛能够帮助更全面地评估机器学习模型的最坏对抗风险。