DISTINQT: A Distributed Privacy Aware Learning Framework for QoS
  Prediction for Future Mobile and Wireless Networks
Nikolaos Koursioumpas Lina Magoula Ioannis Stavrakakis Nancy Alonistioti M. A. Gutierrez-Estevez Ramin Khalili
Beyond 5G and 6G networks are expected to support new and challenging use
cases and applications that depend on a certain level of Quality of Service
(QoS) to operate smoothly. Predicting the QoS in a timely manner is of high
importance, especially for safety-critical applications as in the case of
vehicular communications. Although until recent years the QoS prediction has
been carried out by centralized Artificial Intelligence (AI) solutions, a
number of privacy, computational, and operational concerns have emerged.
Alternative solutions have been surfaced (e.g. Split Learning, Federated
Learning), distributing AI tasks of reduced complexity across nodes, while
preserving the privacy of the data. However, new challenges rise when it comes
to scalable distributed learning approaches, taking into account the
heterogeneous nature of future wireless networks. The current work proposes
DISTINQT, a privacy-aware distributed learning framework for QoS prediction.
Our framework supports multiple heterogeneous nodes, in terms of data types and
model architectures, by sharing computations across them. This, enables the
incorporation of diverse knowledge into a sole learning process that will
enhance the robustness and generalization capabilities of the final QoS
prediction model. DISTINQT also contributes to data privacy preservation by
encoding any raw input data into a non-linear latent representation before any
transmission. Evaluation results showcase that our framework achieves a
statistically identical performance compared to its centralized version and an
average performance improvement of up to 65% against six state-of-the-art
centralized baseline solutions in the Tele-Operated Driving use case.