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Along with the proliferation of artificial intelligence and Internet of things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool deep neural networks (DNNs) used by industrial IoT (IIoT) applications. Due to biased training data or vulnerable underlying models, imperceptible modifications on inputs made by adversarial attacks may result in devastating consequences. Although existing methods are promising in defending such malicious attacks, most of them can only deal with limited existing attack types, which makes the deployment of large-scale IIoT devices a great challenge. To address this problem, in this article, we present an effective federated defense approach named FDA<sup>3</sup> that can aggregate defense knowledge against adversarial examples from different sources. Inspired by federated learning, our proposed cloud-based architecture enables the sharing of defense capabilities against different attacks among IIoT devices. Comprehensive experimental results show that the generated DNNs by our approach can not only resist more malicious attacks than existing attack-specific adversarial training methods, but also prevent IIoT applications from new attacks.

ieee transactions on industrial informatics

FDA<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula>: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications

FDA$^3$: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications