INTERNET OF VEHICLE (IOV) BASED DRIVER EMOTION DETECTION USING FEDERATED LEARNING
Date
2023-04
Authors
Regmi, Pankaj Nidhi
Journal Title
Journal ISSN
Volume Title
Publisher
IOE Pulchowk Campus
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.
Description
Internet of Things (IoT) has revolutionized the world and the way we live. With the
advancement of edge smart computing devices and IoT technologies we are able to
control our smart device from any part of the world. Internet of Vehicle (IoV) is a subset
of IoT which rather focus on vehicle to on vehicle-to-vehicle (V2V) or vehicle-toinfrastructure (V2I) communication transferring the way we interact with cars, and
enabling the new features that were previously impossible
Keywords
System architecture, Federated learning