Federated learning in autonomous driving: Progress, challenges, and outlook in perception, prediction, and communication

In the ever-evolving field of autonomous driving, vehicles have evolved into mobile computing centres, accumulating and processing vast amounts of data, including environmental variables, driver behaviours, and preferences. Conventional centralized data processing methods face privacy and security vulnerabilities. To address these challenges, federated learning technology has emerged as a promising alternative, with its decentralized, privacy-preserving architecture. This review explores the application of federated learning in autonomous driving, focusing on perception, prediction, and communication scenarios, including research such as using federated learning to enhance the vehicles ability to predict steering angle, object detection, and multimodal sensor data fusion. In addition, this review investigates the improvement of communication efficiency through techniques such as Distributed Federated Learning (DFL), Selective Federated Reinforcement Learning (SFRL), and Vehicle-to-everything (V2X) communication. The analysis indicated that federated learning holds great promise in autonomous driving, significantly enhancing vehicle performance in perception, prediction, and communication. However, challenges like data heterogeneity and communication costs persist. Future research should prioritize refining aggregation algorithms, minimizing communication overhead, and adapting federated learning to evolving autonomous driving technologies.