The ability to forecast Quality of Experience (QoE) metrics will be crucial in several applications and services offered by the future B5G/6G networks. However, QoE timeseries forecasting has not been adequately investigated so far, mainly due to the lack of available realistic datasets. In this paper, we first present a novel QoE forecasting dataset obtained from realistic 5G network simulations and characterized by Quality of Service (QoS) and QoE metrics for a video-streaming application; then, we embrace the topical challenge of trustworthiness in the adoption of AI systems for tackling the QoE prediction task. We show how an eXplainable Artificial Intelligence (XAI) model, namely Decision Tree, can be effectively leveraged for addressing the forecasting problem. Finally, we identify federated learning as a suitable paradigm for privacy-preserving collaborative model training and outline the related challenges from both an algorithmic and 6G network support perspective.

http://ceur-ws.org/Vol-3189/paper_07.pdf We acknowledge the support of: the Italian Ministry of University and Research (MIUR), in the framework of the Cross-Lab project (Departments of Excellence) and PON 2014-2021 "Research and Innovation", DM MUR 1062/2021, Project title: "Progettazione e sperimentazione di algoritmi di federated learning per data stream mining"; the Center for Logistic Systems of Livorno; the EU Commission through the H2020 projects Hexa-X (Grant no. 101015956).