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Introduction

Federated learning is a decentralized machine learning approach that allows multiple entities to collaboratively build a shared global model without directly sharing their data. This emerging technology has gained significant attention in recent years due to its potential to address privacy concerns, scalability, and efficiency in machine learning tasks. In the context of autonomous vehicles, federated learning can enable vehicles to learn from each other’s experiences and improve their driving behavior without compromising the privacy of individual drivers. This thesis aims to explore the application of federated learning in autonomous vehicles and its implications for the future of transportation.

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 Two: Literature Review
– Overview of autonomous vehicles
– Introduction to federated learning
– Applications of federated learning in various industries
– Challenges and opportunities of federated learning in autonomous vehicles
– Privacy and security considerations in federated learning
– Previous research on federated learning for autonomous vehicles
– Comparison of federated learning with other machine learning approaches
– Future trends in federated learning for autonomous vehicles
– Case studies of federated learning implementation in autonomous vehicles
– Recommendations for future research in the field

Chapter Three: System Design and Methodology
– Data collection and preprocessing
– Model selection and optimization
– Communication protocol for federated learning
– Federated averaging algorithm implementation
– Evaluation metrics for model performance
– Privacy-preserving techniques for federated learning
– Simulation environment setup
– Experiment design and configuration

Chapter Four: System Implementation
– Federated learning framework setup
– Data partitioning and distribution among vehicles
– Training process and model synchronization
– Performance evaluation of the federated model
– Comparison with centralized learning approach
– Scalability and efficiency analysis
– Privacy protection mechanisms implementation
– Real-world testing and validation

Chapter Five: Conclusion and Summary
– Recap of the research objectives
– Key findings and contributions of the study
– Implications of federated learning for autonomous vehicles
– Limitations and future research directions
– Conclusion on the feasibility and effectiveness of federated learning in autonomous vehicles

Thesis Overview: Federated Learning for Autonomous Vehicles

The advent of autonomous vehicles has revolutionized the transportation industry, promising safer and more efficient means of transportation. However, achieving fully autonomous driving capabilities requires robust machine learning algorithms that can learn from vast amounts of data collected from various sensors and sources. Federated learning offers a promising solution to this challenge by enabling vehicles to collaborate and learn from each other’s experiences without compromising data privacy.

This thesis explores the application of federated learning in autonomous vehicles and its potential to enhance the performance and scalability of machine learning models in a decentralized environment. The research aims to address the following objectives:

1. Investigate the background and challenges of autonomous vehicles and federated learning.
2. Explore the potential benefits and limitations of applying federated learning in autonomous vehicles.
3. Design and implement a federated learning framework for autonomous vehicles.
4. Evaluate the performance and efficiency of the federated learning approach compared to traditional centralized learning methods.
5. Provide recommendations for future research and implementation of federated learning in autonomous vehicles.

By conducting a comprehensive literature review, designing and implementing a federated learning system, and evaluating its performance in a simulated environment, this thesis aims to contribute to the growing body of knowledge on federated learning for autonomous vehicles. The findings of this research have the potential to inform the development of more efficient and privacy-preserving machine learning solutions for autonomous driving systems.

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