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.

翻译：联邦学习（FL）已成为一种在保护数据源隐私的同时协同训练机器学习（ML）模型的有效方法。然而，现有联邦学习方法大多基于监督式技术，需要耗费大量资源且需人工干预以获取标注数据集。此外，在网络攻击检测领域，此类方法无法识别未知威胁。基于此，本研究提出一种新颖的无监督联邦学习方法，用于识别车载环境中的潜在不当行为。我们利用公有云服务的计算能力进行模型聚合，并作为不当行为事件的中枢存储库，从而支持跨车辆学习与集体防御策略。该方案在联邦环境下整合了高斯混合模型（GMM）与变分自编码器（VAE）在VeReMi数据集上的应用，其中每辆车仅使用自身数据进行训练。同时，我们采用受限玻尔兹曼机（RBM）进行预训练，并使用Fedplus聚合函数增强模型收敛性。与近期基于监督式技术及VeReMi数据集人工划分的方案相比，本方法实现了更优性能（超过80%）。