Detection and Mitigation of Label-Flipping Attacks in FL Systems With KL Divergence

Abstract

The application of federated learning (FL) in the Internet of Things (IoT) is experiencing rapid growth. FL becomes vulnerable to data poisoning attacks in the IoT environment, characterized by distributed devices, limited resources, and open networks. Since IoT systems rely on measurement data to make intelligent decisions, attackers can deceive the system by flipping the labels of local data, causing it to misclassify source class samples as target labels at a low cost. Existing defense mechanisms primarily rely on similarity analysis to detect malicious behaviors. However, these methods often lack the sensitivity to handle high-dimensional or complex network models, struggling to identify subtle but significant changes in model parameters or updates. Considering this issue, we propose a label-flipping-robust approach to distinguish malicious clients from benign ones. In brief, we adopt Kullback-Leibler (KL) divergence to quantify the difference between client models, which offers an improved ability to capture the subtle variations within the model. Furthermore, we introduce privacy-protecting FL (GAN in FL) to protect users’ privacy while utilizing the shared knowledge in the global generator to support detecting malicious clients. Experimental results on real-world data sets show that our proposed algorithm can effectively defend against label-flipping attacks (LFAs) under various data distributions.

Topics & Concepts