CB-FL: Cluster-Based Federated Learning applied to Quality of Experience modelling
Simone Porcu, Alessandro Floris, Luigi Atzori · 2022
The Federated Learning (FL) approach can be exploited to build a solution to data sparsity and privacy protection issues (e.g., utilization of user-sensitive data) in Quality of Experience (QoE) modelling. In this paper, we investigate whether it is possible to obtain improvements in FL-based inference by grouping data sources to build separate inference systems. To this, we adopted an experimental based approach: firstly, we identified different clusters of users, from a public QoE dataset, based on user-related QoE influence factors and the distributions of the quality rating scores provided by the users; secondly, we developed a Cluster-Based FL QoE pre-dictor and conducted experimental tests to compare the QoE prediction performance with that obtained by a centralised learning approach and a standard FL approach. The obtained results show that the proposed approach achieved the best QoE prediction performance (in terms of accuracy, precision, recall, and F1-Score), followed respectively by the standard FL and the centralised approach.