Enhanced Smart Advertising through Federated Learning

Smart advertising is growing in popularity and affecting businesses. Smart advertising is a more friendly, interactive, personalised, and creative method of promoting a product, and attempts to delight clients. AROUND is a social networking service that emphasizes smart advertising through an effective recommender system. The system considers user profiles, history, social network connections, mood, and IoT-supported positioning to select the most relevant ads using machine learning technology. Although the current deployment of the AROUND system is based on the cloud, an edge-based architecture provides relevant improvement in terms of system response time. In this paper we extend the edge-based strategy to leverage the potential of federated learning on multiple distributed edge servers. We show that federated learning can take advantage of the distributed nature of the system, and leverage the specificities of local features. In fact, in this research, we propose a novel federated learning solution to provide smart advertising as a classification problem which uses ensemble methods and logistic regression as internal (local) models and meta-heuristic algorithms for federated learning aggregation. As part of the experiments, we prove this technology on a real data set with more than one million registers, and show the efficiency in terms of enhanced accuracy and improved training and response speed.