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.

DISTINQT 是一个隐私感知的分布式学习框架，用于 QoS 预测，在多个异构节点之间共享计算，并通过将原始输入数据编码为非线性潜在表示来贡献于数据隐私保护。在 Tele-Operated Driving 应用场景，我们的框架相比于集中化版本有相同的性能，并在六个最先进的集中式基准解决方案上平均性能提升了 65％。