Privacy-Preserving Digital Twin Framework for Autonomous and Connected Vehicles

Vehicular Digital Twins (VDTs) are emerging as key components in Autonomous and Connected Vehicles (ACVs), enabling real-time monitoring and threat detection. However, in decentralized environments, collaborative learning among VDTs raises significant privacy concerns such as data leakage, re-identification risks and compromised VDTs. This paper presents a decentralized Federated Learning (FL) framework with Differential Privacy (DP) to enable secure and privacy-preserving threat detection across VDTs using a Bi-directional Long Short-Term Memory Autoencoder (Bi-LSTM AE) within an FL setting. The model is designed to capture temporal dependencies in a time series vehicular dataset without compromising data privacy. The model’s performance was evaluated on two attack scenarios, Denial of Service (DoS) and data replay, and compared against existing LSTM-based models in both federated and non-federated contexts. Without DP, the model achieved an F1-score of about 90% and AUC of 95%, performing better than similar methods. We incorporate DP into the FL process to further enhance privacy and assess its impact across different clients. Although, recall remains high, there is a trade-off in precision and accuracy resulting in a global F1-score of 77%. These results highlight the effectiveness of Bi-LSTM AE for decentralized anomaly detection as well as the critical need to balance privacy with utility in real-world deployments.