Security of drivers in intelligent transportation systems: privacy-preserving federated transfer learning for driver drowsiness detection

Driver drowsiness is a serious concern for road safety within intelligent transportation systems, and it can undermine the safety and dependability of critical transport infrastructure. As modern vehicles become more connected and data-focused, centralized learning systems that share driver and vehicle information can expose private details and raise privacy and security concerns. This study presents a privacy-preserving framework that enables secure learning among multiple vehicles without sharing raw data. It uses the On-Board Diagnostic-II sensor data, combined with transfer learning, to detect driver drowsiness in real time within a federated learning framework. Signals such as speed, engine revolutions, throttle position, and steering torque are extracted from cars and then converted into image representations using Mel-Frequency Cepstral Coefficients so the model can identify changes in driving behavior. These image features are used to train a pretrained ResNet50 network; this trained model can classify driver states as drowsy or normal. Each vehicle trains on its own data while the central server updates the shared model weights through a client-weighted averaging strategy that keeps learning balanced for all clients. This process keeps data private while the model trained on different driving pattern. Using client weights DrowsyXnet achieved 98.29% accuracy, which is nearly matched the centralized baseline of 98.67%. The latent feature graph showed a clear separation between drowsy and normal states, indicating that the model learns the underlying signals rather than merely incidental correlations. The proposed framework improves intelligent transportation systems while preventing leakage of private data. The use of driver drowsiness detection system into vehicles can prevent drowsiness related accidents and enhance overall road safety.