Enhanced Smart Advertising through Federated Learning

Smart advertising is growing in popularity and
affecting businesses. Smart advertising is a more friendly, inter-
active, 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.