  Advanced researches on connected vehicles have recently targeted to the
integration of vehicle-to-everything (V2X) networks with Machine Learning (ML)
tools and distributed decision making. Federated learning (FL) is emerging as a
new paradigm to train machine learning (ML) models in distributed systems,
including vehicles in V2X networks. Rather than sharing and uploading the
training data to the server, the updating of model parameters (e.g., neural
networks' weights and biases) is applied by large populations of interconnected
vehicles, acting as local learners. Despite these benefits, the limitation of
existing approaches is the centralized optimization which relies on a server
for aggregation and fusion of local parameters, leading to the drawback of a
single point of failure and scaling issues for increasing V2X network size.
Meanwhile, in intelligent transport scenarios, data collected from onboard
sensors are redundant, which degrades the performance of aggregation. To tackle
these problems, we explore a novel idea of decentralized data processing and
introduce a federated learning framework for in-network vehicles,
C-DFL(Consensus based Decentralized Federated Learning), to tackle federated
learning on connected vehicles and improve learning quality. Extensive
simulations have been implemented to evaluate the performance of C-DFL, that
demonstrates C-DFL outperforms the performance of conventional methods in all
cases.
