Federated Learning for Anomaly Detection in Vehicular Networks

The Internet of Things (IoT) has become an important enabler for vehicular network applications, primarily the Internet of Vehicles (IoV). With the increase in the use of IoV, there is a potential increase in vulnerabilities to attacks and faults on vehicular networks. These misbehaviors or anomalies can vary from wrongly broadcasted data to more intense attacks like Denial of Service (DoS). It has become necessary to protect these vehicular networks through anomaly or misbehavior detection mechanisms. Deep learning models can be used for anomaly detection, considering the large volume of vehicular data available to train them. However, this gives rise to a need for privacy and security against data theft or information leaks of vehicular data. Hence, privacy preserving approaches like federated learning can be leveraged for anomaly detection. In this paper, we develop three federated learning (FL) schemes based on the federated averaging (FedAvg), FedAvg with Adam optimizer (FedAvg-Adam) and FedProx algorithms to acquire deep learning models in a distributed manner to perform anomaly detection in the IoV setting. The federated learning tasks run on local nodes deployed at the network edge, and models are combined on a global server deployed on the cloud. Our evaluation results using a publicly available IoV-relevant dataset show that these schemes were able to learn accurate models which permit effective anomaly detection in vehicular networks under different data distributions and network architectures.