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Introduction

In the era of big data and connected devices, federated learning has emerged as a promising approach to collaborative machine learning without compromising data privacy. Federated transfer learning extends the concept of federated learning by enabling knowledge transfer between different devices, allowing for more efficient and accurate model training across a network of devices. In this thesis, we explore the potential of federated transfer learning for cross-device federated learning, where models are shared and updated across multiple devices with varying computational capabilities and data distributions.

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 Federated learning
2.2 Transfer learning
2.3 Cross-device federated learning
2.4 Challenges in federated transfer learning
2.5 Existing approaches in federated transfer learning
2.6 Privacy and security concerns in federated learning
2.7 Performance metrics in federated learning
2.8 Applications of federated learning in real-world scenarios
2.9 Future trends in federated learning
2.10 Gaps in current research on federated transfer learning

Chapter 3: Research Methodology
3.1 Data collection and preprocessing
3.2 Model selection and initialization
3.3 Federated transfer learning algorithm design
3.4 Evaluation metrics
3.5 Experimental setup
3.6 Cross-device communication protocols
3.7 Privacy preservation techniques
3.8 Statistical analysis methods

Chapter 4: Discussion of Findings
4.1 Model performance analysis
4.2 Convergence speed of federated transfer learning
4.3 Impact of heterogeneous devices on model accuracy
4.4 Privacy and security considerations
4.5 Comparison with traditional federated learning
4.6 Scalability and efficiency of cross-device federated learning
4.7 Generalizability of models across devices
4.8 Robustness to data distribution shifts

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Implications of research
5.3 Future research directions
5.4 Conclusion

Thesis Overview on Federated transfer learning for cross-device federated learning

Federated learning has gained significant attention in recent years as a privacy-preserving approach to distributed machine learning. However, the traditional federated learning framework assumes that all devices have similar data distributions and computational capabilities. In reality, devices in a federated network may have diverse data sources, hardware specifications, and network conditions, leading to challenges in model convergence and performance. Federated transfer learning addresses these issues by allowing devices to share learned knowledge and leverage pre-trained models to improve model accuracy and convergence speed.

This thesis explores the application of federated transfer learning for cross-device federated learning, where models are trained and updated across multiple devices with varying characteristics. The literature review provides an overview of federated learning, transfer learning, and cross-device federated learning, highlighting the challenges and opportunities in federated transfer learning. The research methodology outlines the data collection, model selection, algorithm design, and evaluation metrics used to evaluate the performance of federated transfer learning in a cross-device setting.

The discussion of findings delves into the analysis of model performance, convergence speed, privacy considerations, and scalability of cross-device federated learning. The conclusion summarizes the key findings of the study, discusses their implications, and suggests future research directions in the field of federated transfer learning. Overall, this thesis contributes to the advancement of federated learning techniques for distributed and privacy-preserving machine learning in cross-device settings.

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