The ubiquitous Internet of Things (IoT) system is a key component of future 6G networks to realize a fully connected world. Extensive efforts have been made to provide on-demand traffic scheduling in IoT networks through machine learning algorithms. However, the current learning approaches are hindered by the heterogeneous information in ubiquitous IoT systems since the data are collected from different domains (e.g., space, air, ground, and ocean). To uncover the complete picture of ubiquitous IoT, this article presents a novel federated imitation learning framework for traffic prediction without compromising privacy. This framework contains a knowledge-sharing module to imitate the status of cross-domain models. After that, we design a distributed resource allocation algorithm, where the IoT devices cooperatively make association decisions using matching theory. Simulation results reveal that our proposed approach outperforms state-of-the-art federated transfer learning and achieves desirable traffic scheduling performance in a cross-domain environment.

中文翻译：

---

联邦模仿学习：6G 泛在物联网流量调度的跨领域知识共享框架


无处不在的物联网系统是未来6G网络实现万物互联的关键组成部分。人们已经做出了大量努力，通过机器学习算法在物联网网络中提供按需流量调度。然而，由于数据是从不同领域（例如太空、空中、地面和海洋）收集的，当前的学习方法受到无处不在的物联网系统中异构信息的阻碍。为了揭示无处不在的物联网的全貌，本文提出了一种新颖的联合模仿学习框架，用于在不损害隐私的情况下进行流量预测。该框架包含一个知识共享模块来模仿跨领域模型的状况。之后，我们设计了一种分布式资源分配算法，其中物联网设备使用匹配理论协作做出关联决策。仿真结果表明，我们提出的方法优于最先进的联合迁移学习，并在跨域环境中实现了理想的流量调度性能。