Privacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection

Drowsiness affects the drivers' sensory, cognitive, and psychomotor abilities, which are necessary for safe driving. Drowsiness detection is a critical technique to avoid traffic accidents. Federated learning (FL) can solve the problem of insufficient driver facial data by utilizing different industrial entities' data. However, in the FL system, the privacy information of the drivers might be leaked. In addition, reducing the communication costs and maintaining the model performance are also challenges in industrial scenarios. In this work, we propose a federated transfer learning method with the privacy-preserving protocol for driver drowsiness detection, named PFTL-DDD. We use fine-tuning transfer learning on the initial model of the drowsiness detection FL system. Furthermore, a CKKS-based privacy-preserving protocol is applied to preserve the drivers' privacy data by encrypting the exchanged parameters. The experimental results show that the PFTL-DDD method is superior in terms of accuracy and efficiency compared to the conventional federated learning on the NTHU-DDD and YAWDD datasets. The theoretical analysis demonstrates that the proposed transfer learning method can reduce the communication cost of the system, and the CKKS-based security protocol can protect personal privacy.