Privacy enabled driver behavior analysis in heterogeneous IoV using federated learning

Internet of Vehicles (IoV) is a paradigm of ITS that incorporates automobiles, transportation, information sharing, and traffic infrastructure management with the aim to improve safety on roads. Federated learning enables the Internet of Vehicles (IoV) networks with minimal data communication overhead and privacy infringement. The vehicles participate in model training for improving the model accuracy at the global level and enhancing road safety to a great extent. In this paper, we apply federated learning to driver behavior analysis in which the vehicles in the network collaborate to train CNN-LSTM and CNN-Bi-LSTM deep learning models for driver behavior classification (safe, unsafe, or fatigue) without sharing raw data. The CNN-LSTM and CNN-Bi-LSTM models have been implemented and analyzed using real-time driver’s behavior data, collected using inbuilt smartphone sensors or vehicle onboard devices. The proposed model has been evaluated using the performance parameters viz., accuracy, Area Under Curve (AUC), loss, precision, and recall. Both IID and non-IID types of datasets have been considered for analyzing the diversity of the proposed model. It is evident that implementation of federated learning architecture provided similar parametric outcomes on IID and non-IID datasets in comparison to contemporary deep learning implementations on a non-distributed dataset. The proposed work aims towards designing a reliable intelligent transportation system using federated learning.