Personalized Privacy-Preserving Distributed Artificial Intelligence for Digital-Twin-Driven Vehicle Road Cooperation

The technology of the Internet of Vehicles (IoVs) and Digital Twins (DTs) is driving deeper connectivity between vehicles and road infrastructure. Through the data exchange of IoVs and the simulation of DT technology, vehicle driving decisions, traffic management and road planning are optimized. However, DT models contain a large amount of private vehicle data, causing the risk of privacy leakage. Distributed Artificial Intelligence (AI) methods, particularly Federated Learning (FL) algorithms, ensure data security and privacy by sharing data models rather than sharing private data. Current mainstream algorithms use FL and Local Differential Privacy (LDP) or blockchain approaches to protect data security at the cost of lower model accuracy and larger computation time. In the vehicle road cooperation, we designed a three-layer DT-driven personalized privacy-preserving framework, which includes a physical layer, a DT layer and an application layer. In our proposed framework, to improve the security and performance of DT models, a time-sensitive PLDP-based FL (TimeSenFLDP) mechanism is proposed to achieve different privacy levels of the DT model of vehicles over sharing time steps. Compared with the mainstream algorithm (e.g. DP-SGD), the experiments prove that our proposed algorithm has 18.07%, 16.32% and 7.5% accuracy improvement in FedAvg, FedProx and FedDyn, respectively.