Empowering safe and secure autonomy: Federated learning in the era of autonomous driving

Abstract

Artificial Intelligence (AI) has a significant impact on empowering autonomous driving systems to perceive and interpret the environment effectively. However, ensuring data privacy and security in autonomous driving systems is a critical challenge. To surmount these hurdles, federated learning has emerged as an effective strategy. Federated learning is a decentralized machine learning approach that facilitates the cooperative training of models across a diverse set of connected devices, enabling them to collectively learn and improve their performance, while preserving data privacy. This approach eliminates the necessity of sharing raw data and only involves sharing model updates with a central aggregator, thereby ensuring privacy and minimizing data exposure. This paper examines the implementation of federated learning in autonomous driving. It explores the principles of federated learning, including decentralized training, local model updates, model aggregation, privacy preservation, iterative learning, and heterogeneity handling. Two specific approaches, Deep Federated Learning (DFL) and End-to-End Federated Learning, are discussed, highlighting their benefits in enhancing privacy and maintaining prediction accuracy. The paper also discusses the applications of federated learning in communication and control aspects of autonomous driving. It emphasizes the scalability, adaptability, edge computing, real-time learning, federated transfer learning, and privacy-preserving data sharing as potential future prospects for federated learning in autonomous driving. Overall, federated learning offers a unique opportunity to address privacy concerns in autonomous driving systems while harnessing the collective intelligence of a fleet of vehicles. It has the potential to revolutionize the field and contribute to the development of safe and secure autonomous driving technologies.