Nuh Mih, Atah2024-06-192024-06-192024-04https://unbscholar.lib.unb.ca/handle/1882/38015Transfer 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.xvi, 140electronicenhttp://purl.org/coar/access_right/c_abf2master thesisCao, HungComputer Science