Federated Learning-based neural networks for autonomous driving

Abstract

An emerging technology with the capacity to revolutionize the transportation sector is autonomous driving, offering the promise of heightened safety, efficiency, and convenience. However, the widescale deployment of autonomous vehicles presents a multitude of challenges, notably the necessity for robust and adaptable machine learning (ML) models capable of handling a wide array of dynamic real-world scenarios. Enter Federated Learning (FL), a decentralized ML approach that has gained recognition as a potential solution to these challenges. This paper delves into the primary advantages of FL within the context of autonomous driving. It highlights FL's capacity to seamlessly adapt to edge devices, respond to localized changes, and continually enhance safety and performance. The document substantiates these advantages through numerous case studies and empirical evidence, demonstrating how FL can potentially elevate the vision, decision-making, control systems, data transmission, and learning model capabilities of autonomous vehicles. By harnessing the collective intelligence of autonomous vehicles while preserving data privacy and security, FL holds the potential to propel us closer to a future where safe, efficient, and autonomous transportation becomes an attainable reality.