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Introduction

In recent years, there has been a significant increase in the amount of data generated by various edge devices such as smartphones, IoT devices, and sensors. Edge computing has emerged as a promising paradigm to address the challenges of processing and analyzing this vast amount of data closer to where it is generated. However, privacy concerns have become a major roadblock in the adoption of edge computing.

Privacy-preserving federated learning has gained attention as a potential solution to address these privacy concerns while still allowing for collaborative machine learning models to be trained using data from distributed edge devices. This thesis focuses on exploring privacy-preserving federated learning for edge computing, aiming to develop techniques that ensure the privacy of data while enabling efficient and effective collaborative learning.

Chapter 1: Introduction
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 Introduction to Federated Learning
2.2 Privacy-Preserving Techniques
2.3 Edge Computing
2.4 Federated Learning for Edge Computing
2.5 Privacy Challenges in Edge Computing
2.6 Existing Solutions
2.7 Advantages and Limitations
2.8 Federated Learning Algorithms
2.9 Data Security in Federated Learning
2.10 Privacy Preservation Techniques

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Model Training and Aggregation
3.4 Privacy-Preserving Techniques
3.5 Communication and Synchronization
3.6 Evaluation Metrics
3.7 Experiment Design
3.8 Data Partitioning Strategies

Chapter 4: System Implementation
4.1 Implementation Overview
4.2 Software and Tools
4.3 Data Simulation
4.4 Model Training
4.5 Privacy-Preserving Implementation
4.6 Evaluation Framework
4.7 Results Analysis
4.8 Performance Evaluation

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Future Work
5.4 Conclusion

Thesis Overview on Privacy-Preserving Federated Learning for Edge Computing

Privacy-preserving federated learning for edge computing is a crucial area of research that aims to address the privacy concerns associated with collaborative machine learning models trained on data from distributed edge devices. This thesis explores the challenges and opportunities in ensuring data privacy while enabling effective collaborative learning in edge computing environments.

The literature review provides an overview of federated learning, privacy-preserving techniques, edge computing, and existing solutions in the field. The system design and methodology chapter detail the system architecture, data collection, model training, privacy techniques, and evaluation metrics. The system implementation chapter presents the implementation details, including data simulation, model training, privacy-preserving implementation, and performance evaluation.

The conclusion and summary chapter summarize the findings, contributions, and suggest future research directions in the field of privacy-preserving federated learning for edge computing. This thesis aims to contribute to the development of techniques that ensure data privacy while enabling efficient and effective collaborative learning in edge computing environments.

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