Communication-Efficient Federated Learning and Permissioned Blockchain for Digital Twin Edge Networks

【Author】 Lu, Yunlong; Huang, Xiaohong; Zhang, Ke; Maharjan, Sabita; Zhang, Yan

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Emerging technologies, such as mobile-edge computing (MEC) and next-generation communications are crucial for enabling rapid development and deployment of the Internet of Things (IoT). With the increasing scale of IoT networks, how to optimize the network and allocate the limited resources to provide high-quality services remains a major concern. The existing work in this direction mainly relies on models that are of less practical value for resource-limited IoT networks, and can hardly simulate the dynamic systems in real time. In this article, we integrate digital twins with edge networks and propose the digital twin edge networks (DITENs) to fill the gap between physical edge networks and digital systems. Then, we propose a blockchain-empowered federated learning scheme to strengthen communication security and data privacy protection in DITEN. Furthermore, to improve the efficiency of the integrated scheme, we propose an asynchronous aggregation scheme and use digital twin empowered reinforcement learning to schedule relaying users and allocate spectrum resources. Theoretical analysis and numerical results confirm that the proposed scheme can considerably enhance both communication efficiency and data security for IoT applications.

【Keywords】Artificial intelligence; Optimization; Data privacy; Internet of Things; Edge computing; Resource management; Blockchain; communication efficiency; digital twin; edge networks; federated learning

【发表时间】2021 2022-02-15

【收录时间】2022-01-02

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