Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Wang, Haifeng
Committee Member
Fan, Lir-Wan
Committee Member
Lee, Seunghan
Committee Member
Sescu, Adrian
Date of Degree
8-8-2023
Document Type
Dissertation - Open Access
Major
Computational Engineering
Degree Name
Doctor of Philosophy (Ph.D)
College
James Worth Bagley College of Engineering
Department
Computational Engineering Program
Abstract
Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a classifier trained at one hospital might be useless to another. We propose using autoencoders to address this problem, transforming important information contained in electrocardiograms to a uniform input, where federated learning can then be used to train a strong classifier for multiple healthcare providers. Furthermore, we propose using statistically-guided hyperparameter tuning to ensure fast convergence of the model.
Recommended Citation
Chorney, Wesley William, "Vertical federated learning using autoencoders with applications in electrocardiograms" (2023). Theses and Dissertations. 5970.
https://scholarsjunction.msstate.edu/td/5970
Included in
Artificial Intelligence and Robotics Commons, Biomedical Commons, Biomedical Informatics Commons