Federated Driver Digital Twin (FDDT): Secure, Adaptive, and Deployment-Oriented Private Models for Connected Vehicles

Driver digital twins (DTs) provide a structured mechanism for capturing individualized behavioral characteristics in connected vehicle systems. However, most existing approaches rely on centralized data access and discriminative learning pipelines, limiting privacy preservation, generative capability, and deployment scalability. In addition, the role of generative latent spaces in preserving driver identity (DI) for downstream inference remains insufficiently explored. In this work, the latent space is explicitly interpreted as a persistent DT state that captures driver-specific behavioral signatures rather than serving solely as a reconstruction intermediate. This paper presents a federated driver digital twin (FDDT) framework composed of three components: SIGNet, which learns driver-aware latent DT representations; IDInferNet, which evaluates DI separability from real latent embeddings; and DT-GDIN, which assesses DI consistency using digital-twin-generated samples. A structured evaluation protocol containing 21 multi evaluation metrics examines latent consistency, inter-driver separability, and inference reliability across both observed and generated contexts. Deployment-oriented profiling further investigates latency, model capacity, and accuracy-efficiency tradeoffs to assess feasibility on automotive edge devices. Experimental results indicate that the proposed pipeline supports DI preservation under federated constraints while remaining compatible with deployment-oriented vehicular inference requirements.