  Federated Learning (FL) has become an attractive approach to collaboratively
train Machine Learning (ML) models while data sources' privacy is still
preserved. However, most of existing FL approaches are based on supervised
techniques, which could require resource-intensive activities and human
intervention to obtain labelled datasets. Furthermore, in the scope of
cyberattack detection, such techniques are not able to identify previously
unknown threats. In this direction, this work proposes a novel unsupervised FL
approach for the identification of potential misbehavior in vehicular
environments. We leverage the computing capabilities of public cloud services
for model aggregation purposes, and also as a central repository of misbehavior
events, enabling cross-vehicle learning and collective defense strategies. Our
solution integrates the use of Gaussian Mixture Models (GMM) and Variational
Autoencoders (VAE) on the VeReMi dataset in a federated environment, where each
vehicle is intended to train only with its own data. Furthermore, we use
Restricted Boltzmann Machines (RBM) for pre-training purposes, and Fedplus as
aggregation function to enhance model's convergence. Our approach provides
better performance (more than 80 percent) compared to recent proposals, which
are usually based on supervised techniques and artificial divisions of the
VeReMi dataset.
