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Introduction:

Federated Learning is a novel machine learning approach that allows multiple edge devices to collaboratively train a shared machine learning model, without exchanging their raw data with a centralized server. This decentralized approach to machine learning offers enhanced privacy and security, making it ideal for edge computing environments where data is sensitive and needs to be processed locally. This thesis will explore the potential of Federated Learning for Edge Computing and its implications for improving the efficiency and scalability of edge devices.

Table of Contents:

Chapter 1: Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Objective of Study
1.4 Limitation of Study
1.5 Scope of Study

Chapter 2: Literature Review
2.1 Overview of Federated Learning
2.2 Edge Computing Technologies
2.3 Federated Learning for Edge Computing
2.4 Challenges and Opportunities

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Implementation of Federated Learning Model

Chapter 4: Discussion of Findings
4.1 Evaluation of Federated Learning Model
4.2 Comparison with Centralized Machine Learning
4.3 Privacy and Security Considerations
4.4 Performance Metrics

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications for Edge Computing
5.3 Future Research Directions

Thesis Overview:

Federated Learning for Edge Computing is a cutting-edge research topic that aims to address the challenges of training machine learning models on resource-constrained edge devices. This thesis will explore the potential of Federated Learning in edge computing environments, where data privacy and security are paramount. By leveraging the collective intelligence of edge devices, Federated Learning has the potential to improve the efficiency and scalability of machine learning models in edge computing scenarios. This thesis will investigate the implementation of Federated Learning models on edge devices, evaluate their performance, and discuss the implications for the future of edge computing.

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