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Introduction

Autonomous drone swarms have gained significant attention in various fields such as surveillance, search and rescue operations, and environmental monitoring. However, the effective coordination and collaboration among drones in a swarm pose a significant challenge. Traditional centralized approaches for controlling drone swarms often suffer from issues such as single points of failure and scalability limitations. Federated reinforcement learning, a decentralized approach where each drone learns independently and collaborates with others to achieve a common goal, has emerged as a promising solution to address these challenges.

This thesis focuses on exploring the application of federated reinforcement learning for autonomous drone swarms. The goal is to investigate how this approach can improve the coordination, scalability, and robustness of drone swarms in various real-world scenarios. By leveraging the power of distributed learning and collaboration, we aim to enhance the overall performance and efficiency of drone swarms in dynamic and complex environments.

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 Autonomous Drone Swarms
2.2 Reinforcement Learning in Drone Swarms
2.3 Federated Learning
2.4 Decentralized Control in Drone Swarms
2.5 Existing Research on Federated Reinforcement Learning for Autonomous Drones
2.6 Challenges and Limitations
2.7 Opportunities for Improvement
2.8 Comparison with Centralized Approaches
2.9 Case Studies
2.10 Future Directions

Chapter 3: Research Methodology

3.1 Research Design
3.2 Data Collection
3.3 Model Development
3.4 Simulation Setup
3.5 Evaluation Metrics
3.6 Experimental Procedures
3.7 Data Analysis Techniques
3.8 Ethical Considerations

Chapter 4: Discussion of Findings

4.1 Performance Evaluation
4.2 Scalability Analysis
4.3 Robustness Testing
4.4 Comparison with Existing Approaches
4.5 Impact of Hyperparameters
4.6 Sensitivity Analysis
4.7 Generalization to Real-world Scenarios
4.8 Limitations and Future Work

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 Research
5.5 Conclusion

Thesis Overview:

The use of autonomous drone swarms has become increasingly popular in various applications such as surveillance, search and rescue operations, and environmental monitoring. However, the coordination and collaboration among drones in a swarm remain a significant challenge. Centralized control approaches often suffer from scalability limitations and single points of failure. Federated reinforcement learning, a decentralized approach where each drone learns independently and collaborates with others, has shown promise in addressing these challenges.

This thesis aims to investigate the application of federated reinforcement learning for autonomous drone swarms. The research will focus on improving the coordination, scalability, and robustness of drone swarms through distributed learning and collaboration. By leveraging the advantages of decentralized control, we aim to enhance the overall performance and efficiency of drone swarms in dynamic and complex environments.

Through a comprehensive literature review, research methodology, discussion of findings, and conclusion, this thesis will provide insights into the potential benefits and challenges of implementing federated reinforcement learning in autonomous drone swarms. The results of this research can contribute to advancing the field of autonomous drone technology and pave the way for future innovations in swarm robotics.

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