Evaluating Federated Learning Technologies for Privacy-Preserving Machine Learning in IoT Networks

1. Introduction 1.1 Background and Motivation 1.2 Problem Statement 1.3 Objectives of the Study 1.4 Structure of the Thesis 2. Overview of Federated Learning 2.1 Definition and Concepts 2.2 Federated Learning vs. Centralized Learning 2.3 Key Benefits and Challenges 3. Privacy-Preserving Machine Learning 3.1 Importance of Privacy in ML 3.2 Techniques for Privacy Preservation 3.3 Trade-offs in Privacy and Performance 4. Internet of Things (IoT) Networks 4.1 Characteristics of IoT Networks 4.2 Data Generation and Security Concerns 4.3 Role of Machine Learning in IoT 5. Federated Learning in IoT 5.1 Use Cases and Applications 5.2 Architecture and Communication Models 5.3 Challenges and Considerations 6. Evaluation of Federated Learning Technologies 6.1 Criteria for Evaluation 6.2 Methodology for Assessment 6.3 Comparison with Alternative Solutions 7. Case Studies and Experiments 7.1 Description of Case Studies 7.2 Experimental Setup and Process 7.3 Results and Discussions 8. Conclusions and Future Work 8.1 Summary of Findings 8.2 Limitations of the Study 8.3 Recommendations for Future Research