Designing a privacy-preserving data aggregation scheme for federated learning systems – Complete Phd and Masters Thesis

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Introduction

With the increasing use of machine learning algorithms in various applications, the need for privacy-preserving data aggregation schemes has become crucial. Federated learning systems allow multiple parties to collaboratively train a machine learning model without sharing their sensitive data with each other. However, ensuring the privacy of participants’ data during the aggregation process remains a challenging task.

This thesis focuses on designing a privacy-preserving data aggregation scheme for federated learning systems. The scheme aims to protect the privacy of individual participants while allowing them to contribute their data to train a shared model effectively. By incorporating cryptographic techniques and differential privacy mechanisms, the proposed scheme aims to address the privacy concerns associated with data aggregation in federated learning systems.

This thesis is organized as follows:

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 Systems
2.2 Privacy Concerns in Data Aggregation
2.3 Cryptographic Techniques for Privacy Preservation
2.4 Differential Privacy Mechanisms
2.5 Existing Privacy-Preserving Data Aggregation Schemes
2.6 Challenges and Limitations of Existing Schemes
2.7 Comparison of Different Approaches
2.8 Current Research Trends in Federated Learning
2.9 Summary of Literature Review
2.10 Gaps in Existing Research

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Experimental Setup
3.4 Privacy Metrics and Evaluation Criteria
3.5 Implementation Details
3.6 Ethical Considerations
3.7 Data Analysis Techniques
3.8 Validation and Verification Procedures

Chapter 4: Discussion of Findings
4.1 Performance Evaluation of the Proposed Scheme
4.2 Comparison with Existing Schemes
4.3 Privacy Analysis and Security Guarantees
4.4 Scalability and Efficiency of the Scheme
4.5 Practical Considerations and Implementation Challenges
4.6 Impact of Noise and Perturbations on Model Accuracy
4.7 Sensitivity Analysis and Robustness Testing
4.8 Interpretation of Results

Chapter 5: Conclusion and Summary
5.1 Summary of Research Findings
5.2 Contributions to the Field
5.3 Implications for Practice and Future Work
5.4 Recommendations for Policy and Decision-Makers
5.5 Conclusion

Thesis Overview

The increasing adoption of machine learning algorithms in various applications has raised concerns about the privacy of sensitive data. Federated learning systems have emerged as a promising solution to address these concerns by allowing multiple parties to collaborate on training a shared model without sharing their raw data. However, ensuring the privacy of participants’ data during the aggregation process remains a challenging task.

This thesis focuses on designing a privacy-preserving data aggregation scheme for federated learning systems. The proposed scheme aims to protect the privacy of individual participants while enabling them to contribute their data to train a shared model effectively. By leveraging cryptographic techniques and differential privacy mechanisms, the scheme aims to address the privacy concerns associated with data aggregation in federated learning systems.

The thesis begins with a literature review that explores the background of federated learning systems, privacy concerns in data aggregation, existing privacy-preserving schemes, and current research trends in the field. The research methodology chapter outlines the design, data collection methods, experimental setup, and evaluation criteria for the proposed scheme. The discussion of findings chapter presents the performance evaluation, privacy analysis, scalability, and efficiency of the scheme, along with practical considerations and implementation challenges.

In conclusion, this thesis contributes to the growing body of research on privacy-preserving data aggregation in federated learning systems. The findings of this study have implications for practice and offer recommendations for policy and decision-makers in the field. Future work will focus on further refining the proposed scheme and exploring its applications in real-world scenarios.

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