Connected autonomous vehicles (CAVs) are transforming the transportation business by incorporating advanced technology such as sensors, communication systems, and artificial intelligence. However, the interconnectedness and complexity of CAVs pose security vulnerabilities, making them possible targets for assaults. Intrusion detection is critical in protecting CAVs from harmful actions. This research investigates the use of federated learning, a privacy-preserving machine learning approach, for intrusion detection in CAVs. Federated Learning (FL) can improve the detection capabilities and robustness of intrusion detection systems in the CAV ecosystem by using the collective capacity of various CAVs while protecting data privacy. This paper provides an in-depth analysis of tailoring FL for collaborative intrusion detection in CAVs, as well as prospective future research areas in this domain. The findings of this study contribute to the advancement of secure and dependable CAV systems, opening the path for the widespread use of connected autonomous vehicles in the transportation industry. All code, data, and experiments are accessible on our Github1 repository.1https://github.com/speedlab-git/FLID
