Model Migration in Digital Twin-Empowered Vehicular Edge Computing With AoI-Aware Decentralized Bilevel Learning

The accuracy of digital twin models hinges on the prompt collection of information from the vehicular environment. However, the high mobility of vehicles and the dynamically changing network environment pose significant challenges. Dynamic twin model migration can reduce the Age of Information (AoI) by bringing twin models closer to their vehicles. Existing works rarely consider the inherent differences in optimization cycles between digital twin model migration and data upload, which potentially leads to suboptimal cost efficiency and information freshness. Specifically, real-time vehicular data must be rapidly uploaded to edge servers to ensure the accuracy and timeliness of digital twin models, while frequent migration of twin models over short periods incurs substantial costs. Therefore, we propose a dual-timescale bilevel learning approach, where the upper-layer learning optimizes twin model migration decisions on a long timescale to achieve forward-looking model migration, and the lower-layer learning optimizes data upload and resource allocation decisions on a short timescale to ensure the accuracy and timeliness of digital twin models. Then, we design a multi-agent selective parameter sharing approach based on spatiotemporal dependency correlations to accelerate model convergence and reduce communication costs among agents. Furthermore, through a rigorous theoretical analysis, we prove the convergence of the dual-timescale bilevel learning with broad applicability. Finally, numerical results demonstrate that our algorithm outperforms comparison algorithms in terms of convergence, AoI, and system cost, achieving at least a 21.30% reduction in AoI and a 14.58% reduction in system cost compared to the benchmark algorithms.