Abstract
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-I