Electronic Theses and Dissertations
Date
2024
Document Type
Thesis
Degree Name
Master of Science
Department
Computer Science
Committee Chair
Dipankar Dasgupta
Committee Member
Mohd. Hasan M Ali
Committee Member
Myounggyu M Won
Abstract
Data exhibits a natural distribution, and effective machine learning models rely on substantial datasets. However, much data remains inaccessible due to privacy and security risks in traditional centralized settings, which require data collection on a central server. Federated Learning (FL) addresses this by moving computation to the data source instead of the server. Despite this, FL faces challenges from data and model poisoning attacks due to its distributed nature. Verifying the authenticity of clients in FL is difficult. Proposed solutions include statistical analysis of client updates, hardware-based isolation, Differential Privacy (DP), and Homomorphic Encryption (HE). However, these solutions have limitations and significant trade-offs, such as the privacy-utility trade-off. This research proposes a novel approach to fortify the FL environment using Zero-Trust (ZT) inspired continuous verification of client updates for model poisoning attacks and filter ensembles for data poisoning attacks. Our experiment demonstrates improved results against these attacks.
Library Comment
Dissertation or thesis originally submitted to ProQuest.
Notes
Open Access
Recommended Citation
Pathak, Sagar, "Fortifying Federated Learning: A Comprehensive Analysis and Novel Solutions for Privacy and Security Issues in Federated Learning" (2024). Electronic Theses and Dissertations. 3616.
https://digitalcommons.memphis.edu/etd/3616
Comments
Data is provided by the student.