Digital Twin-Assisted Adaptive Federated Multi-Agent DRL with GenAI for Optimized Resource Allocation in IoV Networks

In this study, we introduce a digital twin (DT)-assisted IoV framework that combines a semi-synchronous adaptive federated learning (AdFL) method with multi-agent deep reinforcement learning, enhanced by generative artificial intelligence (GenAI) techniques, specifically conditional variational autoencoders (CVAE). This framework optimizes partial task offloading across distributed mobile edge computing (MEC) servers, ensuring scalable and efficient decision-making in diverse vehicular networks. By continuously reflecting the real-time conditions of vehicles and roadside units (RSUs), the DT framework ensures precise resource distribution and adaptive task handling. To handle the complexity of dynamic environments, we develop a global model that includes transformer layers in the federated learning (FL) process, which captures long-range dependencies. A semi-synchronous aggregation mechanism is introduced to maintain a balance between timely updates and model quality. The adaptive federated multi-agent reinforcement learning (AF-MARL) algorithm enables decentralized, collaborative learning among vehicles and RSUs, optimizing overall cost and energy use, reducing delays, and improving task completion rates. Comprehensive simulations show the framework's effectiveness compared to existing methods, emphasizing its potential to revolutionize real-time decision-making in IoV networks.