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Introduction

Federated learning is a decentralized machine learning technique that allows multiple parties to collaboratively build a common machine learning model without sharing their data. This approach has gained significant attention in recent years due to its potential to address privacy concerns associated with centralized machine learning systems. Personalized advertising is a crucial application of machine learning in the modern digital economy. By leveraging user data, advertisers can tailor their advertisements to individual preferences, increasing the effectiveness of marketing campaigns. However, concerns about data privacy and security have prompted the exploration of alternative approaches, such as federated learning, to enable personalized advertising while protecting user privacy.

Background of Study

The rise of digital advertising has transformed the marketing landscape, enabling businesses to target their audiences with unprecedented precision. Machine learning algorithms play a crucial role in this process, analyzing vast amounts of data to identify patterns and target relevant advertisements to users. However, the centralized nature of traditional machine learning systems raises concerns about data privacy and security. Federated learning offers a promising solution by allowing multiple parties to collaborate on building a machine learning model without sharing sensitive data.

Problem Statement

The increasing demands for personalized advertising in the digital economy have raised concerns about data privacy and security. Traditional machine learning approaches require centralizing data from multiple sources, increasing the risk of data breaches and privacy violations. Federated learning offers a decentralized alternative that allows parties to collaboratively train a machine learning model without sharing raw data. However, there is a need to explore the feasibility and effectiveness of federated learning for personalized advertising applications.

Objective of Study

The primary objective of this thesis is to investigate the potential of federated learning for personalized advertising applications. The study aims to evaluate the feasibility of using federated learning to train personalized advertising models while preserving user privacy. Specific objectives include:
1. Reviewing the existing literature on federated learning and personalized advertising
2. Designing a federated learning system for personalized advertising
3. Implementing the proposed system and evaluating its performance
4. Comparing the performance of federated learning with traditional centralized approaches
5. Assessing the privacy implications of federated learning for personalized advertising

Limitation of Study

While this study aims to provide valuable insights into the feasibility of federated learning for personalized advertising, there are several limitations to consider. The scope of the study may be restricted by time and resource constraints. Additionally, the generalizability of the findings may be limited by the specific implementation details and dataset used in the study.

Scope of Study

This study focuses on exploring the potential of federated learning for personalized advertising applications. The research will involve designing and implementing a federated learning system for training personalized advertising models using a real-world dataset. The study will evaluate the performance of the proposed system in terms of accuracy, efficiency, and privacy preservation.

Significance of Study

The findings of this study are expected to contribute to the growing body of research on federated learning and personalized advertising. By demonstrating the feasibility and effectiveness of using federated learning for personalized advertising applications, this research can help address concerns about data privacy and security in the digital marketing industry.

Structure of the Thesis

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 Personalized Advertising
2.3 Privacy and Security in Advertising
2.4 Federated Learning for Privacy Preservation
2.5 Challenges of Federated Learning
2.6 Existing Studies on Federated Learning for Personalized Advertising
2.7 Comparison with Centralized Learning Approaches
2.8 Performance Metrics for Advertising Models
2.9 Data Privacy Regulations
2.10 Future Trends in Federated Learning

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Federated Learning Algorithm Selection
3.4 Model Training and Aggregation
3.5 Evaluation Metrics
3.6 Privacy Preservation Techniques
3.7 Experimental Setup
3.8 Performance Evaluation

Chapter 4: System Implementation
4.1 Implementation Details
4.2 Dataset Description
4.3 Model Training Process
4.4 Privacy Preservation Mechanisms
4.5 Performance Evaluation Results
4.6 Comparison with Centralized Approach
4.7 Sensitivity Analysis
4.8 Scalability and Efficiency

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Limitations and Future Work
5.4 Practical Implications
5.5 Conclusion

Thesis Overview on Federated Learning for Personalized Advertising

Federated learning is a novel approach to machine learning that allows multiple parties to collaboratively build models without sharing their data. This thesis aims to explore the potential of federated learning for personalized advertising, a critical application in the digital marketing industry. The study will review the existing literature on federated learning and personalized advertising, design and implement a federated learning system, evaluate its performance, and assess privacy implications. By demonstrating the feasibility and effectiveness of federated learning for personalized advertising, this research aims to contribute valuable insights to the field and address concerns about data privacy and security.

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