Blockchain-empowered federated learning based solutions for Internet of Things security, privacy, and performance

Bachelor thesis (2023)

Authors

P. Papadopoulos Electrical Engineering, Mathematics and Computer Science

Contributors

M. Conti Cyber Security - EEMCS (mentor)
C. Lal Cyber Security - EEMCS (mentor)
Jorge Martinez (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Panagiotis Papadopoulos
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Published Date

03-02-2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The Internet of Things (IoT) is a rapidly growing technology that connects millions of devices together. However, as more devices connect, the importance of ensuring security, privacy, and performance becomes paramount. Training performance is affected without proper protocols in place, and devices can get compromised. This research focuses on how to enhance IoT security, privacy and performance using federated learning and blockchain. We will first identify the current challenges concerning those three metrics for IoT. Then, we will introduce federated learning and blockchain and explore how to integrate them. Next, we will address a set of related work performed on the field through a series of surveys. Keeping those surveys in mind, we present and compare several novel solutions for various IoT applications that attempt to provide solutions to enhance IoT security. We complete this study by discussing the potential of those solutions, as well as their challenges and then we highlight possible directions for future research in this booming field.