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Introduction:

Federated learning is a decentralized machine learning approach that allows multiple parties to collaboratively build a global model without sharing their private data. This approach has gained significant attention in recent years due to its privacy-preserving nature and ability to leverage data from a large number of edge devices. In the context of Internet of Things (IoT) networks, where a large number of interconnected devices generate massive amounts of data, federated learning can be used for collaborative anomaly detection to improve the security and reliability of these networks.

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 Anomaly Detection in IoT Networks
2.3 Collaborative Anomaly Detection Techniques
2.4 Privacy-Preserving Machine Learning
2.5 Federated Learning for Anomaly Detection
2.6 Applications of Federated Learning in IoT Networks
2.7 Challenges and Limitations of Federated Learning
2.8 Existing Research in Collaborative Anomaly Detection
2.9 Comparison of Federated Learning with Other Approaches
2.10 Summary of Literature Review

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Federated Learning Framework
3.4 Anomaly Detection Algorithm
3.5 Collaborative Training Process
3.6 Model Aggregation
3.7 Performance Evaluation Metrics
3.8 Simulation Environment
3.9 Experiment Design
3.10 Proposed Methodology

Chapter 4: System Implementation
4.1 Selection of IoT Devices
4.2 Implementation of Federated Learning Algorithm
4.3 Integration of Anomaly Detection Model
4.4 Training and Testing Process
4.5 Model Evaluation
4.6 Performance Comparison
4.7 Privacy and Security Measures
4.8 Optimization Techniques
4.9 System Deployment
4.10 Results and Analysis

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

Thesis Overview:

Federated learning has emerged as a promising approach for collaborative anomaly detection in IoT networks, where the privacy of user data is of utmost importance. This thesis aims to investigate the application of federated learning in the context of IoT networks for anomaly detection and to propose a novel framework for collaborative training of machine learning models. The study will begin with a comprehensive literature review on federated learning, anomaly detection in IoT networks, and existing research in collaborative anomaly detection techniques. The system design and methodology chapter will outline the proposed framework, including the system architecture, data collection, preprocessing, federated learning framework, anomaly detection algorithm, and evaluation metrics.

The system implementation chapter will detail the selection of IoT devices, implementation of the federated learning algorithm, integration of the anomaly detection model, training process, model evaluation, and performance comparison. Privacy and security measures, optimization techniques, and system deployment will also be discussed. Finally, the conclusion and summary chapter will provide a summary of findings, contributions of the study, implications for future research, limitations, and recommendations. This thesis will contribute to the advancement of federated learning techniques for collaborative anomaly detection in IoT networks and provide valuable insights for researchers and practitioners in the field.

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