IoT Intrusion Detection Based on Personalized Federated Learning

The failure of edge devices in the IoT will affect the use of IoT applications. The introduction of the federated learning can train efficient models for devices under the premise of protecting privacy. However, current solutions rarely focus on the problem of data heterogeneity on IoT devices. In this paper, we introduce two personalized federated learning algorithms to implement intrusion detection models, which aim to solve data heterogeneity. We perform diverse partitions on the IoT dataset to simulate data heterogeneity on devices. Our experiments show that the proposed models have high performance in detecting attacks under various data distributions. Under the Non-IID setting, the test accuracies of our models are 95.5% and 93.4%, which are 8.4% and 6.3% higher than the model using traditional federated learning (FedAvg), respectively.