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Introduction

Federated learning has emerged as a promising technique for privacy-preserving machine learning applications in various domains, including healthcare, finance, and smart cities. In the context of smart cities, where data is generated from various sources such as sensors, IoT devices, and urban infrastructure, preserving user privacy while leveraging the data for improving services is of paramount importance. Federated learning enables collaborative model training across decentralized devices without the need to transfer raw data to a central server, thus addressing privacy concerns.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of the study
1.3 Problem Statement
1.4 Objective of the study
1.5 Limitation of the study
1.6 Scope of the study
1.7 Significance of the study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of Federated Learning
2.2 Privacy-Preserving Machine Learning
2.3 Smart City Applications
2.4 Privacy Concerns in Smart Cities
2.5 Existing Approaches for Privacy Preservation
2.6 Federated Learning in Smart Cities
2.7 Challenges in Implementing Federated Learning
2.8 Case Studies on Federated Learning in Smart Cities
2.9 Emerging Trends in Privacy-Preserving Smart City Applications
2.10 Gaps in Current Research

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Selection of Participants
3.5 Validation of Research Framework
3.6 Ethical Considerations
3.7 Pilot Study
3.8 Research Limitations

Chapter 4: Discussion of Findings
4.1 Overview of Data Collected
4.2 Analysis of Privacy Preservation Techniques
4.3 Evaluation of Federated Learning Models
4.4 Comparison with Existing Approaches
4.5 Implications for Smart City Applications
4.6 Recommendations for Future Research
4.7 Practical Implementations
4.8 Policy Implications

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Limitations of the Study
5.5 Suggestions for Future Research
5.6 Conclusion

Thesis Overview

Federated learning has emerged as a novel approach to address privacy concerns in smart city applications by allowing distributed devices to collaboratively train machine learning models without sharing sensitive data. This thesis aims to investigate the application of federated learning for privacy preservation in smart city settings. The study will begin with a comprehensive literature review on federated learning, privacy-preserving machine learning, and smart city applications, followed by an in-depth analysis of existing approaches and challenges.

The research methodology will outline the design of the study, data collection methods, analysis techniques, participant selection, and ethical considerations. The findings will be discussed by evaluating privacy preservation techniques, federated learning models, and their implications for smart cities. The thesis will conclude with a summary of findings, contributions to the field, limitations of the study, suggestions for future research, and practical implications for implementing federated learning in smart city applications.

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