Title: Distributed Adversarial Training to Robustify Deep Neural Networks at Scale. Abstract: Current deep neural networks are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective and popular approach, known as adversarial training, has been shown to mitigate the negative impact of adversarial attacks by virtue of a min-max robust training method. While effective, this approach is difficult to scale well to large models on large datasets (e.g., ImageNet) in general. To address this challenge, we propose distributed adversarial training (DAT), a large-batch adversarial training framework implemented over multiple machines. DAT supports one-shot and iterative attack generation methods, gradient quantization, and training over labeled and unlabeled data. Theoretically, we provide, under standard conditions in the optimization theory, the convergence rate of DAT to the first-order stationary points in general non-convex settings.  Empirically, on ResNet-18 and -50 under CIFAR-10 and ImageNet, we demonstrate that DAT either matches or outperforms state-of-the-art robust accuracies and achieves a graceful training speedup.