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Introduction:

Federated learning is a decentralized machine learning approach that enables training models across multiple edge devices while keeping the data localized. This allows for improved privacy and reduced latency, making it ideal for edge intelligence applications. In this thesis, we will explore the use of federated learning for edge intelligence and its potential benefits in real-world scenarios.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objectives of the Study
1.4 Limitations of the Study
1.5 Scope of the Study

Chapter 2: Literature Review
2.1 Overview of Federated Learning
2.2 Applications of Federated Learning in Edge Intelligence
2.3 Challenges and Solutions in Federated Learning for Edge Intelligence
2.4 Comparison with Other Machine Learning Approaches

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Model Selection
3.3 Training and Evaluation
3.4 Implementation on Edge Devices

Chapter 4: Discussion of Findings
4.1 Performance Evaluation of Federated Learning for Edge Intelligence
4.2 Privacy and Security Considerations
4.3 Scalability and Deployment Challenges
4.4 Future Research Directions

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

Thesis Overview:

Federated learning is a cutting-edge machine learning technique that allows for training models on decentralized data sources. In the context of edge intelligence, federated learning offers a promising solution to the challenges of latency, privacy, and scalability. This thesis will explore the use of federated learning for edge intelligence, providing a comprehensive overview of the current state of the art and identifying key research directions.

The introduction will set the stage for the study, outlining the background and motivations for using federated learning in edge intelligence. The objectives of the study will be clearly defined, along with the limitations and scope of the research.

The literature review will provide a thorough analysis of existing literature on federated learning and its applications in edge intelligence. This chapter will highlight the benefits and challenges of using federated learning in edge intelligence and compare it with other machine learning approaches.

The research methodology chapter will detail the data collection, model selection, training, and evaluation processes used in the study. This chapter will also discuss the implementation of federated learning on edge devices and any technical considerations.

The discussion of findings chapter will present the results of the study, including performance evaluations, privacy and security considerations, scalability issues, and future research directions. This chapter will provide a comprehensive analysis of the implications of the study for practice and offer recommendations for future work.

The conclusion and summary chapter will summarize the key findings of the thesis, highlighting the contributions to the field and outlining recommendations for future research. This chapter will tie together the main points of the study and offer a final reflection on the use of federated learning for edge intelligence.

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