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.

翻译：超越5G和6G网络预计将支持依赖特定服务质量（QoS）水平才能平稳运行的新型挑战性用例与应用。及时预测QoS具有极高重要性，特别是对于车载通信等安全关键型应用。尽管近年来QoS预测一直由集中式人工智能（AI）解决方案执行，但已出现一系列隐私、计算和操作层面的顾虑。替代性解决方案（如分割学习、联邦学习）已陆续出现，这些方案将降低复杂度的AI任务分布至各节点，同时保护数据隐私。然而，在考虑未来无线网络异构特性的前提下，可扩展分布式学习方法仍面临新的挑战。本研究提出DISTINQT——一种面向QoS预测的隐私感知分布式学习框架。我们的框架通过跨节点共享计算的方式，支持包含不同数据类型和模型架构的多个异构节点。这使得多样化的知识能够整合至单一学习过程，从而增强最终QoS预测模型的鲁棒性和泛化能力。DISTINQT还通过在任何传输前将原始输入数据编码为非线性潜在表示，为数据隐私保护做出贡献。评估结果表明，与集中式版本相比，本框架实现了统计上等同的性能；在远程操控驾驶用例中，相较于六种最先进的集中式基线解决方案，平均性能提升高达65%。