Title: Federated Learning for Edge Computing

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

Literature Review

2.1 Introduction to Federated Learning
2.2 Edge Computing and its Applications
2.3 Federated Learning for Edge Computing
2.4 Challenges in Federated Learning
2.5 Privacy and Security Concerns in Federated Learning
2.6 Existing Solutions and Technologies
2.7 Comparison of Federated Learning and Centralized Learning
2.8 Future Trends in Federated Learning
2.9 Summary of Literature Review
2.10 Research Gaps and Opportunities

System Design and Methodology

3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Federated Learning Algorithms
3.4 Model Aggregation Techniques
3.5 Communication Protocols for Edge Devices
3.6 Performance Evaluation Metrics
3.7 Experiment Setup
3.8 Data Analysis Techniques
3.9 Ethical Considerations
3.10 Summary of System Design and Methodology

System Implementation

4.1 Implementation Environment
4.2 Development Tools and Technologies
4.3 Data Collection and Preparation
4.4 Federated Learning Model Development
4.5 Model Training and Optimization
4.6 Model Evaluation and Testing
4.7 Integration with Edge Devices
4.8 System Performance Evaluation
4.9 Challenges and Solutions
4.10 Summary of System Implementation

Conclusion and Summary

5.1 Summary of Findings
5.2 Achievements of the Study
5.3 Contributions to Existing Literature
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview on Federated Learning for Edge Computing

Federated learning is a decentralized machine learning approach that allows multiple edge devices to collaboratively train a global model without sharing their raw data. This thesis explores the use of federated learning for edge computing, where edge devices such as smartphones, IoT devices, and vehicles can learn from each other without relying on centralized servers. The introduction provides an overview of the research, including the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The literature review examines existing research on federated learning, edge computing, and their intersection, highlighting challenges, privacy concerns, solutions, and future trends. The system design and methodology chapter outlines the architecture, data collection, algorithms, communication protocols, and evaluation metrics used in the study. The system implementation chapter details the development environment, tools, data preparation, model training, and performance evaluation. The conclusion summarizes the findings, contributions, recommendations, and concludes the thesis. Overall, this research aims to advance the understanding and implementation of federated learning for edge computing in real-world applications.

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.