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Introduction

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 Collaborative Spam Detection
2.3 Previous studies on Federated Learning for Spam Detection
2.4 Challenges in Collaborative Spam Detection
2.5 Machine Learning Algorithms for Spam Detection
2.6 Federated Learning Models for Spam Detection
2.7 Comparison of Federated Learning with Centralized Learning for Spam Detection
2.8 Applications of Federated Learning in Other Fields
2.9 Future Research Directions in Federated Learning for Spam Detection
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Introduction to Research Methodology
3.2 Data Collection Methods
3.3 Data Preprocessing Techniques
3.4 Federated Learning Model Selection
3.5 Training and Testing of Federated Learning Models
3.6 Evaluation Metrics
3.7 Ethical Considerations
3.8 Limitations of Research Methodology

Chapter 4: Discussion of Findings
4.1 Introduction to Discussion of Findings
4.2 Analysis of Federated Learning Models for Spam Detection
4.3 Comparison of Results with Previous Studies
4.4 Impact of Federated Learning in Collaborative Spam Detection
4.5 Recommendations for Future Research
4.6 Implications for Practice
4.7 Conclusion of Discussion of Findings

Chapter 5: Conclusion and Summary
5.1 Summary of Study
5.2 Contributions to the Field
5.3 Implications for Future Research
5.4 Limitations of the Study
5.5 Final Thoughts

Thesis Overview

Federated learning has emerged as a promising approach for collaborative spam detection, leveraging the power of distributed computing to train machine learning models on decentralized data sources without the need to centralize sensitive information. This thesis explores the effectiveness of federated learning in detecting and preventing spam using a collaborative approach.

Chapter 1 provides the introduction to the study, including the background of the research, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review on federated learning, collaborative spam detection, machine learning algorithms for spam detection, and the applications of federated learning in various fields.

Chapter 3 outlines the research methodology, including data collection methods, data preprocessing techniques, federated learning model selection, training and testing procedures, evaluation metrics, ethical considerations, and limitations. Chapter 4 discusses the findings from the study, analyzing the performance of federated learning models for spam detection and comparing results with previous studies.

Chapter 5 concludes the thesis, summarizing the study, highlighting contributions to the field, suggesting future research directions, discussing limitations, and offering final thoughts on the application of federated learning for collaborative spam detection.

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