Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

TL;DR

This survey reviews federated learning applications in connected and automated vehicles, highlighting frameworks, data security, and challenges to improve privacy and performance in vehicular ML models.

Contribution

It provides a comprehensive analysis of FL frameworks, data sources, security techniques, and challenges specific to CAVs, offering insights for future research directions.

Findings

Analyzes centralized and decentralized FL frameworks for CAVs.

Reviews data security techniques for privacy preservation.

Identifies challenges and future directions for FL in CAVs.

Abstract

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future investigation to further enhance the effectiveness and efficiency of FL in the context of CAV are discussed.