INTERNET OF VEHICLE (IOV) BASED DRIVER EMOTION DETECTION USING FEDERATED LEARNING

Abstract

With the advancement of edge smart computing devices and Internet of Vehicle (IoV) technologies emotion detection has become one of the most used methods in smart vehicle while driving. Many models have been employed however, privacy disclosure and communication cost are still a question. To address this question a federated learning driver emotion detection system model is proposed. It intelligently utilizes collaboration between edge, client and cloud for realizing dynamic model also protecting edge data privacy. Federated Learning has an advantage on privacy. In this thesis two different algorithm FedAvg and FedSGD are compared. It is found that accuracy of FedAvg is better than FedSGD. Also, FedSGD takes more steps to converge than FedAvg.