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Introduction

Federated learning has emerged as a promising approach to train machine learning models across decentralized networks of devices such as smartphones, edge servers, and autonomous vehicles. This distributed learning paradigm enables collaborative model training without raw data leaving the devices, addressing privacy concerns and bandwidth limitations. In the context of autonomous driving, federated learning has the potential to revolutionize the way training data is collected and utilized to improve the performance and safety of autonomous vehicles.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of the 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 Introduction to Federated Learning
2.2 Federated Learning in Autonomous Driving
2.3 Privacy and Security in Federated Learning
2.4 Challenges and Opportunities in Federated Learning
2.5 Applications of Federated Learning in Autonomous Vehicles
2.6 Existing Research on Federated Learning for Autonomous Driving
2.7 Performance Comparison with Centralized Learning
2.8 Federated Learning Frameworks for Autonomous Vehicles
2.9 Federated Learning Algorithms and Techniques
2.10 Future Trends in Federated Learning for Autonomous Driving

Chapter 3: System Design and Methodology
3.1 System Architecture for Federated Learning in Autonomous Driving
3.2 Data Collection and Preprocessing
3.3 Model Aggregation and Communication Strategies
3.4 Federated Learning Optimization Algorithms
3.5 Federated Averaging and Gradient Descent
3.6 Device Selection and Scheduling
3.7 Hyperparameter Tuning in Federated Learning
3.8 Evaluation Metrics and Benchmarking
3.9 Experimental Setup and Validation

Chapter 4: System Implementation
4.1 Implementation of Federated Learning Framework
4.2 Integration with Autonomous Vehicle Platform
4.3 Training Data Organization and Distribution
4.4 Model Training and Optimization
4.5 Federated Learning Server Configuration
4.6 Data Encryption and Privacy Preservation
4.7 Real-time Performance Monitoring
4.8 Testing and Evaluation of the System

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Directions for Research
5.4 Conclusion

Thesis Overview

Federated learning for autonomous driving is a novel approach that leverages the power of decentralized networks to train machine learning models for autonomous vehicles. This thesis explores the potential of federated learning in improving the performance, privacy, and scalability of autonomous driving systems. The introduction provides a comprehensive overview of the research background, problem statement, objectives, scope, and significance of the study.

The literature review delves into the theoretical foundations of federated learning, its applications in autonomous driving, privacy and security considerations, as well as existing research and future trends in the field. The system design and methodology chapter outlines the architecture, data collection, model aggregation, optimization algorithms, and evaluation metrics for implementing federated learning in autonomous vehicles.

The system implementation chapter details the practical aspects of deploying a federated learning framework on an autonomous vehicle platform, including data organization, model training, server configuration, encryption, monitoring, and testing. The conclusion and summary chapter summarizes the key findings, contributions, and future directions for research in federated learning for autonomous driving.

Overall, this thesis aims to advance the understanding and application of federated learning in the context of autonomous vehicles, paving the way for safer, more efficient, and privacy-preserving autonomous driving systems.

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