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

Vishnu Pandi Chellapandi, Liangqi Yuan, Christopher G. Brinton, Stanislaw H. Zak, Ziran Wang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)119-137
Number of pages19
JournalIEEE Transactions on Intelligent Vehicles
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Artificial Intelligence
  • Automotive Engineering

Keywords

  • Federated learning
  • connected and automated vehicles
  • data security
  • distributed computing
  • privacy protection

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