Theses and Dissertations

Issuing Body

Mississippi State University


Wang, Haifeng

Committee Member

Fan, Lir-Wan

Committee Member

Lee, Seunghan

Committee Member

Sescu, Adrian

Date of Degree


Document Type

Dissertation - Open Access


Computational Engineering

Degree Name

Doctor of Philosophy (Ph.D)


James Worth Bagley College of Engineering


Computational Engineering Program


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.