Federated Learning for Privacy-Preserving Collaboration in Autonomous Vehicle Networks: Utilizes federated learning to enable privacy-preserving collaboration among autonomous vehicle networks

Authors

  • Dr. Daniel Koppelman Professor of Computer Science, University of Haifa, Israel Author

Keywords:

Traffic optimization, Privacy-preserving collaboration

Abstract

Federated Learning (FL) has emerged as a promising approach to enable privacy-preserving collaboration in various domains, including autonomous vehicles (AVs). This paper presents a comprehensive overview of FL techniques tailored for AV networks, focusing on privacy preservation and collaborative model training. We discuss the unique challenges and opportunities in applying FL to AV networks and propose a framework that leverages FL to enhance collaboration while preserving the privacy of sensitive data. Our framework includes a decentralized learning architecture, secure aggregation protocols, and data encryption techniques to ensure privacy and security in collaborative AV networks. We also provide a case study illustrating the application of FL in a simulated AV network, demonstrating its effectiveness in improving model accuracy without compromising data privacy.

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References

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Published

21-12-2023

How to Cite

[1]
Dr. Daniel Koppelman, “Federated Learning for Privacy-Preserving Collaboration in Autonomous Vehicle Networks: Utilizes federated learning to enable privacy-preserving collaboration among autonomous vehicle networks”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 1–12, Dec. 2023, Accessed: Nov. 06, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/74

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