Improving Personalized Federated Learning-based QoE Assessment using Clustering

Personalized QoE has significant implications for businesses in terms of customer satisfaction, loyalty, and revenue generation. By delivering experiences tailored to individual users, businesses can build stronger relationships, improve customer retention, and gain a competitive edge in the marketplace. In this paper, we have attempted to use a clustering-based approach to enhance personalized QoE assessment via personalized federated learning technique. To achieve this, first, we classify users to different clusters, based on some user-related QoE influencing factors. Second, we employ independent personalized federated learning QoE predictors in clusters to assess the QoE level of the service. We conducted some experiments to compare the performance of our method to the traditional personalized federated learning based QoE assessment approach. The results demonstrate that the proposed approach increases the accuracy of QoE evaluations by about 16% in average.