research-article

Swarm Learning In Autonomous Driving: A Privacy Preserving Approach

Authors:

Abhishek Mishra,

O. P. Joy Jefferson,

Arti AryaAuthors Info & Claims

ICCMS '23: Proceedings of the 2023 15th International Conference on Computer Modeling and Simulation

Pages 271 - 277

https://doi.org/10.1145/3608251.3608271

Published: 17 August 2023 Publication History

Abstract

Autonomous driving technology has made significant progress in recent years due to the development and implementation of cutting-edge models for computer vision and deep learning. These advances have enabled the creation of autonomous vehicles that can navigate roads and make driving decisions without the need for human intervention. However, the use of sensors and cameras in these vehicles has raised concerns about privacy, as they capture a vast amount of data, including location-specific landmarks and personally identifiable information. The identification and obfuscation of such sensitive data during preprocessing can be a costly process. To address these concerns, this paper proposes a Swarm Learning-based training approach for autonomous driving systems. Swarm Learning involves sharing model learnings across nodes rather than raw data, which can help to protect privacy. In addition to addressing privacy concerns, this approach offers performance that is comparable to traditional training methods. It also exhibits improvements over other distributed machine learning techniques such as Federated Learning. Overall, the Swarm Learning approach presents a promising solution for the development of autonomous driving systems that maintain high performance while addressing privacy concerns. By sharing model learnings rather than raw data, Swarm Learning helps to protect sensitive information and reduces the risk of privacy breaches. This approach offers a viable alternative to traditional training methods, enabling the creation of autonomous driving systems that are both effective and respectful of privacy.

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Cited By

Souza RHolm ABiczyk MCastro L(2025)Healthchain - Bidimensional Blockchain for Collaborative and Secure Deep Learning in HealthcareDistributed Computing and Artificial Intelligence, 21st International Conference10.1007/978-3-031-82073-1_10(99-108)Online publication date: 18-Feb-2025
https://doi.org/10.1007/978-3-031-82073-1_10
Gojković MSchranz M(2024)Preserving Privacy in Logistics by Using Swarm Intelligence from the Bottom-Up2024 IEEE 12th International Conference on Intelligent Systems (IS)10.1109/IS61756.2024.10705232(1-7)Online publication date: 29-Aug-2024
https://doi.org/10.1109/IS61756.2024.10705232

Index Terms

Swarm Learning In Autonomous Driving: A Privacy Preserving Approach
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence

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ICCMS '23: Proceedings of the 2023 15th International Conference on Computer Modeling and Simulation

June 2023

293 pages

ISBN:9798400707919

DOI:10.1145/3608251

Copyright © 2023 ACM.

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Publication History

Published: 17 August 2023

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ICCMS 2023

ICCMS 2023: 2023 The 15th International Conference on Computer Modeling and Simulation

June 16 - 18, 2023

Dalian, China

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Cited By

Souza RHolm ABiczyk MCastro L(2025)Healthchain - Bidimensional Blockchain for Collaborative and Secure Deep Learning in HealthcareDistributed Computing and Artificial Intelligence, 21st International Conference10.1007/978-3-031-82073-1_10(99-108)Online publication date: 18-Feb-2025
https://doi.org/10.1007/978-3-031-82073-1_10
Gojković MSchranz M(2024)Preserving Privacy in Logistics by Using Swarm Intelligence from the Bottom-Up2024 IEEE 12th International Conference on Intelligent Systems (IS)10.1109/IS61756.2024.10705232(1-7)Online publication date: 29-Aug-2024
https://doi.org/10.1109/IS61756.2024.10705232

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