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Introduction

Federated learning is a novel machine learning approach that enables collaborative training of a global model across multiple decentralized edge devices, such as smartphones, tablets, and IoT devices. This paradigm shift in machine learning has gained significant attention in recent years due to its potential to address privacy concerns, reduce communication costs, and improve model performance in edge computing environments. Mobile edge computing, on the other hand, leverages the computational resources of edge devices to perform data processing tasks closer to the end-users, reducing latency and bandwidth usage.

This thesis aims to explore the intersection of federated learning and mobile edge computing, investigating how the two technologies can be integrated to improve the efficiency and scalability of machine learning models on edge devices. By leveraging the computational power of edge devices and the collaborative learning capabilities of federated learning, we seek to address the challenges of training machine learning models on resource-constrained devices in edge computing environments.

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 Mobile Edge Computing
2.3 Integration of Federated Learning and Mobile Edge Computing
2.4 Privacy and Security in Federated Learning
2.5 Communication Efficiency in Federated Learning
2.6 Performance Evaluation of Federated Learning Models
2.7 Edge Device Resource Management
2.8 Scalability of Federated Learning Models
2.9 Challenges and Opportunities in Federated Learning for Mobile Edge Computing
2.10 Future Research Directions

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

Chapter 4: Discussion of Findings
4.1 Performance Comparison of Federated Learning Models on Edge Devices
4.2 Impact of Edge Device Resource Allocation on Model Training
4.3 Privacy and Security Measures in Federated Learning for Edge Devices
4.4 Scalability and Efficiency of Federated Learning Models in Edge Computing
4.5 Challenges and Limitations of Federated Learning for Edge Devices

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Future Research
5.4 Conclusion

Thesis Overview on Federated Learning for Mobile Edge Computing

Mobile edge computing (MEC) has gained widespread adoption in recent years, enabling edge devices to perform complex data processing tasks closer to the end-users, reducing latency and improving overall performance. At the same time, federated learning has emerged as a promising approach to collaborative model training, enabling edge devices to train a global model without sharing sensitive data with a central server. The integration of federated learning with mobile edge computing presents exciting opportunities for efficient and scalable machine learning on resource-constrained edge devices.

This thesis explores the potential of federated learning for mobile edge computing, aiming to investigate the challenges and opportunities of training machine learning models on edge devices in a collaborative and privacy-preserving manner. By leveraging the computational resources of edge devices and the collaborative learning capabilities of federated learning, we seek to improve the efficiency and scalability of machine learning models in edge computing environments.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive literature review on federated learning, mobile edge computing, their integration, privacy, security, communication efficiency, performance evaluation, resource management, scalability, challenges, and future research directions. Chapter 3 describes the research methodology, including research design, data collection, model development, experiment setup, evaluation metrics, performance evaluation, data privacy measures, and ethical considerations.

Chapter 4 discusses the findings of the study, including a performance comparison of federated learning models on edge devices, impact of edge device resource allocation on model training, privacy and security measures, scalability, efficiency, challenges, and limitations. Chapter 5 concludes the thesis, summarizing the findings, highlighting the contributions, discussing implications for future research, and providing a conclusion.

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