Federated TSK Models for Predicting Quality of Experience in B5G/6G Networks

Real-time applications based on streaming data collected from remote devices, such as smartphones and vehicles, are commonly developed using Artificial Intelligence (AI). Such applications must fulfill different requirements: on one hand, they must ensure good performance and must deliver results in a timely manner; on the other hand, with the objective of being compliant with the AI-specific regulations, they shall preserve data privacy and guarantee a certain level of explainability. In this paper, we describe an AI-based application to predict the Quality of Experience (QoE) for videos acquired by moving vehicles from Beyond 5G and 6G (B5G/6G) network data. To this aim, we exploit a Takagi-Sugeno-Kang (TSK) fuzzy model learned by employing a federated approach, thus meeting, simultaneously, the requests for explainability and data privacy preservation. A thorough experimental analysis, involving also the comparison with an opaque baseline (i.e., a neural network model), is presented and shows that the TSK model can be regarded as a viable solution which guarantees on the one side an optimal trade-off between interpretability and accuracy, and on the other side preserves the data privacy.