LAUSR

Industrial Internet of Things (IIoT) systems are key enabling infrastructures that sustain the functioning of production and manufacturing. To satisfy the intelligence demands, federated learning has been envisioned as a promising technique for IIoT applications with privacy training requirements. However, research works have shown that, by training the local model on crafted poisoning samples malicious participants can jeopardize the functionalities of the global model. In this article, we propose a robust federated learning method, named RobustFL, in IIoT systems to defend against poisoning attacks. The main idea is that we conduct an adversarial training framework, in which an extra logits-based predictive model is built at the server-side to predict which participant a given logit belongs to. Meanwhile, the federated model is adversarially trained to prevent this predictive behavior, thus mitigating the poisoning attack influences. We evaluate the poisoning attack and our defense method on three benchmark datasets. Experimental results demonstrate the superiority of our proposed method in terms of high accuracy and efficiency in defending against poisoning attacks.