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Table of Contents:

Chapter 1: Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Research Objectives
1.4 Research Questions
1.5 Significance of the Study
1.6 Limitations of the Study
1.7 Scope of the Study

Chapter 2: Literature Review
2.1 Overview of Secure Multi-party Computation
2.2 Overview of Federated Learning
2.3 Previous Studies on Secure Multi-party Computation for Federated Learning
2.4 Gaps in Existing Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Methods
3.4 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Overview of Findings
4.2 Analysis of Findings
4.3 Implications of Findings
4.4 Recommendations for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusions
5.3 Contributions to the Field
5.4 Practical Implications
5.5 Suggestions for Further Research

Overview of Thesis “Secure Multi-party Computation for Federated Learning”:

Secure multi-party computation (SMPC) and federated learning are two emerging technologies that have the potential to revolutionize the way data is shared and processed in a secure and privacy-preserving manner. SMPC allows multiple parties to jointly compute a function while keeping their inputs private, while federated learning enables machine learning models to be trained across decentralized devices without centralizing the data.

This thesis aims to investigate the application of SMPC techniques in the context of federated learning, with a focus on enhancing the privacy and security of the training process. The study will explore current research on SMPC and federated learning, identify gaps in existing literature, and propose a novel method for combining these two technologies.

By conducting a thorough literature review, engaging in empirical research, and analyzing the findings, this thesis will contribute to the growing body of knowledge on secure and privacy-preserving machine learning techniques. The conclusions drawn from this study will provide insights into the potential benefits and challenges of incorporating SMPC into federated learning systems, offering practical recommendations for future research and development in this area.

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