Man Zhang

With the growing popularity of Mobile Edge Computing (MEC) technology applied in Internet of Vehicle (IoV), edge servers deployed at static locations such as macro base stations (MBSes) can provide computation and caching services for vehicles within their coverage area, effectively reducing the task processing delay. However, due to the dense buildings around the MBSes and the hotspot requests within particular area (e.g., traffic intersections), it is a challenging task for Quality of Service (QoS) guarantee in IoV. To address these challenges, unmanned aerial vehicles (UAVs) can be integrated into MEC for IoV by leveraging their advantages of mobile flexibility, low price, and line-of-sight (LoS) communication links. In this paper, we establish a joint UAV-assisted IoV scenario, where both UAVs and MBSes can provide computation and data caching services for smart vehicles. Then, we formulate a joint optimization problem for dynamic data caching and computation offloading, aiming to minimize the average task processing delay and maximize the UAV cache hit ratio. By applying deep reinforcement learning (DRL) techniques, we design an intelligent data caching and computation offloading (IDCCO) algorithm to deal with largescale and continuous state and action spaces. Furthermore, in order to accelerate the convergence speed of DRL model training while protecting the privacy of original user data in IoV, we propose a distributed training mechanism based on Federated Learning (FL), where the DRL model training is performed locally on UAV and global parameter aggregation is performed on MBS. Finally, extensive experiments are conducted, and the experimental results demonstrate the superiority of our approach over several comparative algorithms in shortening the training time, reducing the task processing delay, and maximizing the cache hit ratio.