Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Federated Distillation and Deep Reinforcement Learning

Tengfei Cao, Nianqing Zhang, Xiaoying Wang, Jianqiang Huang

Abstract: With the rapid advancement of intelligent transportation systems (ITS), vehicle edge caching (VEC) technology shows great potential in processing large-scale vehicle data and providing instant services. The high mobility of vehicles restricts the duration of their connection to a vehicle roadside unit (RSU) to a short interval within the unit's service region. It becomes a challenge to accurately predict and update the popular contents during this period and decide the caching location of the popular contents. The transfer of substantial data volumes concurrently presents a significant danger of privacy breaches. To address these difficulties effectively, we proposed a collaborative caching scheme for vehicular edges that considers mobility of vehicles, employing federated distillation (FD) and deep reinforcement learning (DRL). First, we establish a vehicle mobility model using the real-world vehicle movement dataset T-Drive. After that, we propose a framework that integrates recommender systems with federated distillation instead of the traditional autoencoder (AE)-based federated learning scheme to obtain more accurate global models to predict content popularity. Due to the limited storage capacity of a single RSU, in order to maximise the use of edge cache resources, we propose a cooperative caching algorithm based on deep reinforcement learning, which caches these popular contents in multiple RSUs close to the vehicle. Finally, we evaluate the performance of our proposed algorithm on the vehicle mobility model, and the experimental results show that our proposed scheme improves the caching efficiency by 6.7% and reduces the content delivery latency by 11.3% relative to the five baseline schemes on the MovieLens-1 M dataset.

External IDs:dblp:journals/tnse/CaoZWH25