论文标题
6G电信生态系统的隐私保护分布式学习框架
Privacy-Preserving Distributed Learning Framework for 6G Telecom Ecosystems
论文作者
论文摘要
我们为6G时代的电信生态系统提出了一个隐私的分布式学习框架，以使ML模型的共享所有权和治理具有保护数据所有者的隐私性。我们通过将其应用于多域多供应商光网络中的传输质量（QOT）估计的用途来证明其好处，在该估计中没有与网络管理系统（NMS）共享单个域的数据。
We present a privacy-preserving distributed learning framework for telecom ecosystems in the 6G-era that enables the vision of shared ownership and governance of ML models, while protecting the privacy of the data owners. We demonstrate its benefits by applying it to the use-case of Quality of Transmission (QoT) estimation in multi-domain multi-vendor optical networks, where no data of individual domains is shared with the network management system (NMS).
