Date of Award

5-31-2020

Document Type

Thesis

Degree Name

Master of Science in Data Science - (M.S.)

Department

Computer Science

First Advisor

Guiling Wang

Second Advisor

Zhi Wei

Third Advisor

Ioannis Koutis

Abstract

Federated Learning is a form of distributed learning which leverages edge devices for training. It aims to preserve privacy by communicating users’ learning parameters and gradient updates to the global server during the training while keeping the actual data on the users’ devices. The training on global server is performed on these parameters instead of user data directly while fine tuning of the model can be done on client’s devices locally. However, federated learning is not without its shortcomings and in this thesis, we present an overview of the learning paradigm and propose a new federated recommender system framework that utilizes homomorphic encryption. This results in a slight decrease in accuracy metrics but leads to greatly increased user-privacy. We also show that performing computations on encrypted gradients barely affects the recommendation performance while ensuring a more secure means of communicating user gradients to and from the global server.

Included in

Data Science Commons

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