  Connected and Automated Vehicles (CAVs) are one of the emerging technologies
in the automotive domain that has the potential to alleviate the issues of
accidents, traffic congestion, and pollutant emissions, leading to a safe,
efficient, and sustainable transportation system. Machine learning-based
methods are widely used in CAVs for crucial tasks like perception, motion
planning, and motion control, where machine learning models in CAVs are solely
trained using the local vehicle data, and the performance is not certain when
exposed to new environments or unseen conditions. Federated learning (FL) is an
effective solution for CAVs that enables a collaborative model development with
multiple vehicles in a distributed learning framework. FL enables CAVs to learn
from a wide range of driving environments and improve their overall performance
while ensuring the privacy and security of local vehicle data. In this paper,
we review the progress accomplished by researchers in applying FL to CAVs. A
broader view of the various data modalities and algorithms that have been
implemented on CAVs is provided. Specific applications of FL are reviewed in
detail, and an analysis of the challenges and future scope of research are
presented.
