Graduation Year
2023
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Electrical Engineering
Major Professor
Chang J. Morris, Ph.D.
Committee Member
Ismail Uysal, Ph.D.
Committee Member
Zhuo Lu, Ph.D.
Committee Member
Lu Lu, Ph.D.
Committee Member
Simon Ou, Ph.D.
Keywords
Dimension reduction, Non-IID data, Federated Learning, Privacy Preservation
Abstract
Deep Learning and its applications have become attractive to a lot of research recentlybecause of its capability to capture important information from large amounts of data. While most of the work focuses on finding the best model parameters, improving machine learning performance from data perspective still needs more attention. In this work, we propose techniques to enhance the robustness of deep learning classification by tackling data issue. Specifically, our data processing proposals aim to alleviate the impacts of class-imbalanced data and non- IID data in deep learning classification and federated learning scenarios. In addition, data pre-processing strategies such that dimensionality reduction is also enhanced using a proposed deep learning-based technique for a scenario of data privacy preservation. By conducting several experiments and comparisons, we show that our approaches yield good performance and constantly outperform many state-of-the-art methods.
Scholar Commons Citation
Nguyen, Hung S., "Deep Learning Enhancement and Privacy-Preserving Deep Learning: A Data-Centric Approach" (2023). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/9989
Included in
Artificial Intelligence and Robotics Commons, Electrical and Computer Engineering Commons
