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Introduction

In recent years, federated learning has gained significant attention as a privacy-preserving technique for training machine learning models on distributed data sources. However, one of the key challenges in federated learning is ensuring the privacy of the individual data sources while still allowing for collaborative model training. Secure multi-party computation (MPC) is a cryptographic technique that enables multiple parties to jointly compute a function on their inputs without revealing any sensitive information. By using MPC, federated learning can be enhanced to provide stronger privacy guarantees for the participating parties.

This thesis explores the use of secure multi-party computation for privacy-preserving federated learning. The research aims to address the challenges and limitations of existing federated learning methods by incorporating MPC techniques to ensure strong privacy guarantees for the individual data sources. The primary objective of this study is to demonstrate the feasibility and effectiveness of using MPC in federated learning scenarios. The research will also investigate the limitations and scope of using MPC in federated learning and evaluate the significance of this approach in improving privacy in collaborative machine learning settings.

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 Overview of Federated Learning
2.2 Privacy-Preserving Techniques in Federated Learning
2.3 Secure Multi-Party Computation
2.4 Applications of MPC in Machine Learning
2.5 Challenges in Federated Learning
2.6 Existing Research on MPC for Federated Learning
2.7 Privacy and Security Concerns in Collaborative Machine Learning
2.8 Comparative Analysis of Privacy-Preserving Techniques
2.9 Advancements in Privacy-Preserving Technologies
2.10 Future Directions in Federated Learning Research

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection and Preprocessing
3.3 Secure Multi-Party Computation Implementation
3.4 Model Training and Evaluation
3.5 Privacy and Security Analysis
3.6 Experimental Setup
3.7 Metrics for Evaluation
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of Experimental Results
4.2 Privacy and Security Evaluation
4.3 Comparison with Existing Methods
4.4 Implications for Federated Learning
4.5 Limitations and Challenges
4.6 Recommendations for Future Research
4.7 Practical Applications of MPC in Federated Learning

Chapter 5: Conclusion and Summary
5.1 Recap of Key Findings
5.2 Contributions to the Field
5.3 Implications for Privacy-Preserving Federated Learning
5.4 Future Directions
5.5 Conclusion

Thesis Overview on Secure Multi-Party Computation for Privacy-Preserving Federated Learning

The rapid growth of data-driven technologies has led to an increasing emphasis on privacy protection and data security. In collaborative machine learning settings, such as federated learning, preserving the privacy of individual data sources is essential to maintain trust and ensure compliance with data protection regulations. Secure multi-party computation (MPC) offers a promising solution to the privacy challenges in federated learning by allowing multiple parties to collaborate on model training without sharing their sensitive data.

This thesis investigates the use of MPC techniques for enhancing the privacy guarantees of federated learning systems. By incorporating MPC into federated learning protocols, the research aims to demonstrate the feasibility and effectiveness of this approach in protecting the privacy of individual data sources. Through a comprehensive literature review, an in-depth analysis of existing methods, and experimental validation, this study seeks to provide insights into the potential benefits and limitations of using MPC for privacy-preserving federated learning.

The research methodology involves designing and implementing a secure multi-party computation framework for federated learning, collecting and preprocessing data, training machine learning models using MPC protocols, and evaluating the privacy and security implications of the proposed approach. The findings of this study will contribute to the understanding of how MPC can be leveraged to enhance the privacy of federated learning systems and provide recommendations for future research in this emerging field.

In conclusion, this thesis aims to shed light on the use of secure multi-party computation for privacy-preserving federated learning and its potential implications for collaborative machine learning. By addressing the privacy challenges in federated learning systems, this research seeks to advance the development of secure and privacy-preserving machine learning solutions in data-sensitive environments.

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