  Federated learning is a technique of decentralized machine learning. that
allows multiple parties to collaborate and learn a shared model without sharing
their raw data. Our paper proposes a federated learning framework for intrusion
detection in Internet of Vehicles (IOVs) using the CIC-IDS 2017 dataset. The
proposed framework employs SMOTE for handling class imbalance, outlier
detection for identifying and removing abnormal observations, and
hyperparameter tuning to optimize the model's performance. The authors
evaluated the proposed framework using various performance metrics and
demonstrated its effectiveness in detecting intrusions with other datasets
(KDD-Cup 99 and UNSW- NB-15) and conventional classifiers. Furthermore, the
proposed framework can protect sensitive data while achieving high intrusion
detection performance.
