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 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 novel multi-headed input 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 highly complex, compressed, and irreversible latent representations before any transmission. Evaluation results showcase that DISTINQT achieves a statistically identical performance compared to its centralized version, while also proving the validity of the privacy preserving claims. DISTINQT manages to achieve a reduction in prediction error of up to 65% on average against six state-of-the-art centralized baseline solutions presented in the Tele-Operated Driving use case.

翻译：超越5G与6G网络预计将支持依赖特定服务质量水平以平稳运行的新型挑战性用例与应用。及时预测服务质量至关重要，尤其对于车载通信等安全关键型应用。尽管直至近年服务质量预测仍通过集中式人工智能解决方案实现，但已浮现出诸多隐私、计算与运营层面的问题。替代性解决方案（如拆分学习、联邦学习）应运而生，其将复杂度降低的人工智能任务分布至各节点执行，同时保护数据隐私。然而，考虑到未来无线网络的异构特性，可扩展的分布式学习方法面临新的挑战。本研究提出DISTINQT——一种新颖的用于服务质量预测的多头输入隐私感知分布式学习框架。该框架通过跨节点共享计算，支持在数据类型与模型架构方面具有异构性的多节点协作，从而将多样化知识整合至单一学习过程中，以增强最终服务质量预测模型的鲁棒性与泛化能力。DISTINQT通过在任何传输前将原始输入数据编码为高度复杂、压缩且不可逆的潜在表示，亦有助于数据隐私保护。评估结果表明，DISTINQT在实现与集中式版本统计性能等同的同时，验证了其隐私保护主张的有效性。在远程驾驶用例中，相较于六种先进的集中式基线解决方案，DISTINQT平均可降低高达65%的预测误差。