Context-Aware Proactive Edge Caching for Vehicular Edge Computing Based on Asynchronous Federated Learning

Edge caching is a promising technique for effectively reducing backhaul pressure and content access latency in the Internet of Vehicles (IoV). The existing content caching solutions still face the following challenges: 1) contents cached on edge servers are outdated quickly as time and user preferences change; 2) the large amount of vehicle data causes huge communication overheads; and 3) limited storage resources of edge servers. Simultaneously considering these issues to reduce transmission latency is a large-scale 0–1 constraint problem, which is NP-hard, and boosting cache hit rates is a key entry point. In this work, we propose a context-aware proactive caching strategy (CPCS) based on asynchronous federated learning (AFL), which works as follows. To improve the accuracy of content popularity prediction, thus improving the cache hit rate, we combine contextual information between different contents and use long and short-term memory networks to analyze the dynamic preferences of vehicle users. After that, vehicles complete the model training and upload via an asynchronous federation learning to complete the popularity prediction. To explore the problem of local models being outdated in AFL, CPCS integrates model compression algorithms, enhancing system efficiency and prediction accuracy. With the prediction results, CPCS gives a content placement algorithm based on the prediction results to approximate the optimal caching scheme. Simulation results show that the CPCS can improve the cache hit rate by 17% at most compared to existing state-of-the-art caching strategies.