Swarm Learning In Autonomous Driving: A Privacy Preserving Approach.

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.