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Introduction

Privacy-preserving federated learning has emerged as a promising approach to address the challenges of privacy and data security in the context of Internet of Things (IoT) devices. With the increasing proliferation of IoT devices in various domains such as healthcare, smart cities, and industrial automation, there is a growing need to develop privacy-preserving mechanisms that allow these devices to collaborate and learn from each other without compromising the privacy of user data. Federated learning enables IoT devices to train machine learning models locally on their data and share only the model updates with a central server, thereby minimizing the exposure of sensitive information. This thesis aims to investigate the design, implementation, and evaluation of privacy-preserving federated learning techniques for IoT systems, with a focus on ensuring data privacy and security while maximizing the efficiency of collaborative learning.

1.1 Introduction

1.2 Background of Study

1.3 Problem Statement

1.4 Objective of Study

1.5 Limitation of Study

1.6 Scope of Study

1.7 Significance of Study

1.8 Structure of the Thesis

1.9 Definition of Terms

Chapter 2: Literature Review

2.1 Overview of Federated Learning in IoT

2.2 Privacy-preserving Techniques in Federated Learning

2.3 Security in Federated Learning Systems

2.4 Challenges in Privacy-preserving Federated Learning

2.5 Existing Solutions and Approaches

2.6 Comparative Analysis of Privacy-preserving Techniques

2.7 Privacy Metrics for Evaluating Federated Learning Systems

2.8 Applications of Federated Learning in IoT

2.9 Future Research Directions

2.10 Summary of Literature Review

Chapter 3: System Design and Methodology

3.1 System Architecture for Privacy-preserving Federated Learning

3.2 Data Collection and Preprocessing

3.3 Model Aggregation and Updates

3.4 Privacy-enhancing Technologies

3.5 Federated Learning Algorithms

3.6 Experimental Setup and Evaluation Metrics

3.7 Performance Evaluation Criteria

3.8 Security Considerations

Chapter 4: System Implementation

4.1 Development Environment

4.2 Data Collection and Simulation

4.3 Model Training and Validation

4.4 Encryption and Secure Communication

4.5 Integration with IoT Devices

4.6 Testing and Validation

4.7 Performance Optimization

4.8 Scalability and Efficiency

Chapter 5: Conclusion and Summary

5.1 Summary of Findings

5.2 Contributions of the Thesis

5.3 Implications for Practice

5.4 Future Research Directions

In this thesis, we will explore the challenges and opportunities of privacy-preserving federated learning in the context of IoT systems. We will review the existing literature, design a secure and efficient system, implement the proposed solutions, and evaluate its performance. The outcomes of this research will contribute to the development of privacy-preserving mechanisms for IoT devices, enhancing their security and enabling more effective collaboration in federated learning scenarios.

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