Unlocking the benefits of transfer learning in edge-cloud computing environments

dc.contributor.advisorCao, Hung
dc.contributor.authorNuh Mih, Atah
dc.date.accessioned2024-06-19T15:05:51Z
dc.date.available2024-06-19T15:05:51Z
dc.date.issued2024-04
dc.description.abstractTransfer learning’s success motivates the need to understand its characteristics across cloud, edge, and edge-cloud computing paradigms. Thus, this extensive research evaluates the role of transfer learning in 1) cloud computing; 2) edge computing; and 3) edge-cloud computing. It first proposes a transfer learning approach to address the data limitation and model scalability challenges for machine learning in a cloud computing environment. Then, this study provides a model optimization for deep neural networks to improve hardware efficiency for training models on edge devices and investigates the role of transfer learning on resource consumption. Finally, a weight-averaging method is proposed for collaborative knowledge transfer across a unified edge and cloud computing environment to improve training performance for local edge models and global server models. The research conclusively shows that transfer learning benefits edge and cloud computing paradigms both individually and collaboratively.
dc.description.copyright© Atah Nuh Mih, 2024
dc.format.extentxvi, 140
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/38015
dc.language.isoen
dc.publisherUniversity of New Brunswick
dc.relationEigen Innovations
dc.relationNBIF Pre-AI Voucher Fund
dc.relationNBIF Talent Recruitment Fund
dc.relationCFI Innovation Fund
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineComputer Science
dc.titleUnlocking the benefits of transfer learning in edge-cloud computing environments
dc.typemaster thesis
oaire.license.conditionother
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.C.S.

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