GenAI-Enhanced Federated Multiagent DRL for Digital-Twin-Assisted IoV Networks

Achieving real-time decision-making and efficient resource management in dynamic, large-scale Internet-of-Vehicles (IoV) networks is a significant challenge due to their inherent complexity and scale. To address this, we propose a digital twin (DT)-assisted IoV framework that integrates a novel semi-synchronous adaptive federated learning (AdFL) approach with multiagent deep reinforcement learning, enhanced by generative artificial intelligence (GenAI) techniques, specifically conditional variational autoencoders (CVAEs). The framework optimizes partial task offloading across distributed mobile-edge computing (MEC) servers, ensuring scalable, efficient, and accurate decision-making in heterogeneous vehicular networks. By continuously mirroring the real-time states of vehicles and roadside units (RSUs), the DT framework enables precise resource allocation and adaptive task management. To tackle the complexities of dynamic environments, we design a global model with transformer layers embedded in the federated learning (FL) process, capturing long-range dependencies. A novel semi-synchronous aggregation mechanism is introduced to balance timely updates with model quality. The proposed adaptive federated multiagent reinforcement learning (AF-MARL) algorithm facilitates decentralized, collaborative learning among vehicles and RSUs, optimizing overall cost, and energy efficiency, reducing delay, and improving task completion rates. Extensive simulations demonstrate the effectiveness of the proposed framework against other existing approaches, highlighting its potential to transform real-time decision-making in IoV networks.