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Introduction

Federated learning is a novel decentralized learning paradigm that enables machine learning models to be trained across multiple devices or servers holding local data samples, without the need to share actual data. This approach has gained increasing attention due to its ability to preserve user privacy while enabling predictive services at scale. In this thesis, we aim to explore the potential of federated learning in the context of preserving user privacy while enabling predictive services at scale.

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 Preservation in Federated Learning
2.3 Scalability of Federated Learning
2.4 Predictive Services in Federated Learning
2.5 Applications of Federated Learning
2.6 Challenges in Federated Learning
2.7 Existing Solutions and Approaches
2.8 Comparative Analysis of Federated Learning Approaches
2.9 Future Trends in Federated Learning
2.10 Gaps in Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Analysis
3.4 Model Development
3.5 Privacy-Preserving Techniques
3.6 Evaluation Metrics
3.7 Experiment Setup
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of Privacy-Preserving Techniques
4.2 Scalability Assessment of Federated Learning
4.3 Performance Evaluation of Predictive Services
4.4 Comparison of Federated Learning Models
4.5 User Privacy and Data Security
4.6 Real-World Applications
4.7 Implications for Future Research
4.8 Limitations of the Study

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Recommendations for Practice
5.4 Future Research Directions
5.5 Conclusion

Thesis Overview on Federated Learning Preserving User Privacy while Enabling Predictive Services at Scale

Federated learning is a promising approach that allows machine learning models to be trained across multiple devices or servers without compromising user privacy. This thesis aims to explore the potential of federated learning in preserving user privacy while enabling predictive services at scale. The research will involve a comprehensive literature review, analysis of existing solutions, development of models, and evaluation of privacy-preserving techniques. The findings will contribute to the field by providing insights into the scalability and performance of federated learning models. Ultimately, this thesis aims to address the gap in the literature and provide recommendations for future research in the field of federated learning.

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