Content Popularity Prediction Based on Integrated Features and Federated Learning

Abstract

Mobile content service has been experiencing an explosive traffic growth in radio access networks. Most of data traffic is contributed by duplicated data transmission due to frequent download of popular contents requested by multiple mobile users. Proactive content caching has been an effective approach to alleviate traffic burden and improve user experience. Content popularity is an important factor that affects proactive caching. However, content popularity is usually unknown in advance. Therefore, predicting content popularity has become an important challenge on MEC oriented content management and orchestration. In this paper, in the networking scenario with one MBS and several SBSs, content popularity prediction is investigated based on integrated features of user and content. Considering user privacy and reducing transmission cost of uploading data for learning, a content popularity prediction algorithm is proposed based on integrated features and federated learning (PPFUC-FL). The proposed algorithm is evaluated with MovieLens dataset. Simulation results indicate that PPFUC-FL has good performance on precision accuracy compared with content popularity obtained from the real dataset.