AI Federated Learning for Face Recognition at the Edge

dc.contributor.advisorNajafirad, Peyman
dc.contributor.authorAfrin, Sadia
dc.contributor.committeeMemberDesai, Kevin
dc.contributor.committeeMemberXie, Mimi
dc.date.accessioned2024-01-25T19:32:00Z
dc.date.available2024-01-25T19:32:00Z
dc.date.issued2022
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractDeep learning based face recognition models require massive amount of centralized data to train the model efficiently. For image classification tasks, the centralized data can be utilized from public database. However, for face recognition it is prohibited to access private data. Due to this privacy concern, face recognition under privacy protocol has been one of the most difficult tasks in the era of computer vision. Federate learning which is a form of machine learning model addresses the issue. It helps to train the model with multiple devices or clients without allowing them share the data. In this work, using federated learning we improved both the personalized and generalized model.
dc.description.departmentComputer Science
dc.format.extent52 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9798841760603
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2347
dc.languageen
dc.subjectFacial recognition
dc.subjectDeep learning
dc.subjectImage classification
dc.subject.classificationComputer science
dc.titleAI Federated Learning for Face Recognition at the Edge
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentComputer Science
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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