Content Popularity Prediction in Edge Computing Networks Based on Elastic Federated Learning

This paper focuses on the problem of content popularity prediction in edge computing networks. To enable small base stations (SBSs) to obtain accurate prediction results while protecting user privacy, we propose a novel popularity prediction strategy based on elastic federated learning. First, we propose a cluster segmentation method based on region preference similarity, aggregating users from similar regions into the same cluster by considering content similarity and region distance similarity. This fine-grained segmentation method effectively enables each SBS better to provide personalized services to users in specific groups. Second, we adopt a comprehensive approach that considers multiple contextual features to train the Variational Auto-Encoder(VAE) model in SBS and learn its latent representations to improve the accuracy and reliability of model prediction. Finally, we construct a personalized model for SBS based on elastic federated learning. Specifically, we achieved personalized learning and prevalence prediction of SBS by dynamically adjusting the learning process of each SBS and comprehensively considering the differences between global and local models. Experimental results show that our approach improves the cache hit rate by up to 28.10% compared to existing strategies.