Popular Content Prediction Through Adversarial Autoencoder Using Anonymised Data

The increasing number of connected and autonomous vehicles generates an even greater demand for efficient content delivery in vehicular networks. Estimating the popularity of content is an important task to proactively cache and distribute content throughout the networks to add value to users' experiences and reduce network congestion. This paper presents a novel approach for predicting popular content on vehicular networks based on a Federated Learning-Adversarial Autoencoder model and anonymised data. Unlike prior works that relied on users' raw features, our model protects user privacy through data anonymisation. This allows us to learn from the hidden patterns of content popularity and deliver popular content without compromising user privacy. Experiments showed that our approach exceeded traditional collaborative filtering and deep learning methods in terms of accuracy and robustness, even with sparse data.