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

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dc.contributor.authorRegmi, Pankaj Nidhi
dc.date.accessioned2023-06-19T07:11:30Z
dc.date.available2023-06-19T07:11:30Z
dc.date.issued2023-04
dc.descriptionInternet 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 impossibleen_US
dc.description.abstractWith 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.en_US
dc.identifier.urihttps://elibrary.tucl.edu.np/handle/20.500.14540/18005
dc.language.isoenen_US
dc.publisherIOE Pulchowk Campusen_US
dc.relation.ispartofseries076MSICE011;
dc.subjectSystem architectureen_US
dc.subjectFederated learningen_US
dc.titleINTERNET OF VEHICLE (IOV) BASED DRIVER EMOTION DETECTION USING FEDERATED LEARNINGen_US
dc.typeThesisen_US
local.academic.levelMastersen_US
local.affiliatedinstitute.titlePulchowk Campusen_US
local.institute.titleInstitute of Engineeringen_US
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