Master of Science in Computer Science
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Number of Pages
With the onset of the digital era, data privacy is one of the most predominant issues. Decentralized learning is becoming popular as the data can remain within local entities by maintaining privacy. Federated Learning is a decentralized machine learning approach, where multiple clients collaboratively learn a model, without sharing raw data. There are many practical challenges in solving Federated Learning, which include communication set up, data heterogeneity and computational capacity of clients. In this thesis, I explore recent methods of Federated Learning with various settings, such as data distributions and data variability, used in several applications. In addition, I, specifically, examine a design of systematic network topology in a federated framework with computational experiments.
Federated Learning; Data distribution; Data privacy; Machine learning; Analysis
University of Nevada, Las Vegas
Ganapathy, Manjari, "An Introduction to Federated Learning and Its Analysis" (2021). UNLV Theses, Dissertations, Professional Papers, and Capstones. 4146.
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