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％。

DISTINQT: 质量预测未来移动和无线网络的分布式隐私感知学习框架

DISTINQT: A Distributed Privacy Aware Learning Framework for QoS  Prediction for Future Mobile and Wireless Networks

Quality of Service (QoS) prediction is an essential task in recommendation
systems, where accurately predicting unknown QoS values can improve user
satisfaction. However, existing QoS prediction techniques may perform poorly in
the presence of noise data, such as fake location information or virtual
gateways. In this paper, we propose the Probabilistic Deep Supervision Network
(PDS-Net), a novel framework for QoS prediction that addresses this issue.
PDS-Net utilizes a Gaussian-based probabilistic space to supervise intermediate
layers and learns probability spaces for both known features and true labels.
Moreover, PDS-Net employs a condition-based multitasking loss function to
identify objects with noise data and applies supervision directly to deep
features sampled from the probability space by optimizing the Kullback-Leibler
distance between the probability space of these objects and the real-label
probability space. Thus, PDS-Net effectively reduces errors resulting from the
propagation of corrupted data, leading to more accurate QoS predictions.
Experimental evaluations on two real-world QoS datasets demonstrate that the
proposed PDS-Net outperforms state-of-the-art baselines, validating the
effectiveness of our approach.

通过使用概率深度监督网络 (PDS-Net) 的新框架，该研究解决了噪声数据对于质量服务 (QoS) 预测的负面影响，通过减少错误传播，提高准确性。

概率深度监督网络：一种噪声鲁棒的 QoS 预测方法

Probabilistic Deep Supervision Network: A Noise-Resilient Approach for  QoS Prediction

5G communication system can support the demanding quality-of-service (QoS)
requirements of many advanced vehicle-to-everything (V2X) use cases. However,
the safe and efficient driving, especially of automated vehicles, may be
affected by sudden changes of the provided QoS. For that reason, the prediction
of the QoS changes and the early notification of these predicted changes to the
vehicles have been recently enabled by 5G communication systems. This solution
enables the vehicles to avoid or mitigate the effect of sudden QoS changes at
the application level. This article describes how QoS prediction could be
generated by a 5G communication system and delivered to a V2X application. The
tele-operated driving use case is used as an example to analyze the feasibility
of a QoS prediction scheme. Useful recommendations for the development of a QoS
prediction solution are provided, while open research topics are identified.

本文探讨了 5G 通信系统如何生成和传递 QoS 预测给车联网应用，并以远程驾驶为例分析了 QoS 预测方案的可行性，同时提供了有用的建议和开放的研究方向。