Author ORCID Identifier

https://orcid.org/0009-0001-9895-5176

Date of Award

5-10-2024

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Sergey Plis

Second Advisor

Yanqing Zhang

Third Advisor

Harshvardhan Gazula

Abstract

This thesis explores the integration of Meshnet models with distributed learning techniques to enhance MRI brain scan analysis, with a focus on optimizing brain tissue segmentation while maintaining secure distributed systems. Through refining Meshnet's architecture and training strategies, the goal is to enhance accuracy in identifying brain segmentations. Distributed learning strategies, particularly centralized aggregation, are investigated to enable collaborative model training while ensuring data privacy. Additionally, Coinstac is integrated for secure gradient aggregation from diverse nodes, facilitating collaborative analysis without compromising confidentiality. Implementation of a serverless architecture using public clouds extends global accessibility while upholding robust security measures. The primary aim is to empower professionals with advanced tools for collaborative research.

DOI

https://doi.org/10.57709/36919146

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