Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks

By Yunlong Lu, Xiaohong Huang, Ke Zhang, Sabita Maharjan, Yan Zhang. IEEE Transactions on Industrial Informatics, 2020

440 citations recorded by the source corpus.

Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.

Subfields: Artificial Intelligence, Health Informatics, Information Systems

View Paper: https://www.semanticscholar.org/paper/29fa1ff9ef99ccd7d08829143d1f24feab0a6e5f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Health Informatics

Qiang Yang

In Information Systems

Citations in the corpus

A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs (2024, background): “…addressing distributed computing and AI applications such as automated driving [11], autonomous collaboration between devices for medical assistance [12], offloading heavy computational operations across devices and networks, and the creation and prediction with digital twins [13], [14].”
6G Internet of Things: A Comprehensive Survey (2021, methodology): “For example, in intelligent 6G-based healthcare, wearable sensors and mobile devices should be able to simultaneously run AI functions to achieve edge intelligence and implement data transmission with URLLC.”
A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, background): “To preserve data privacy, the authors in [311] extend the learning-based prediction scheme of [310] to FL to improve system reliability and security.”
Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond (2021, background): “, low-earth-orbit (LEO) satellite networks [223], [224], Terahertz (THz) communications and sensing [225], [226], digital twins [227], [228], orthogonal time frequency space (OTFS) modulation [229]–[231], and more.”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, methodology): “To evaluate the performance of the proposed FLchain scheme, real-world MNIST dataset and Fashion-MNIST dataset [43] are used in a simulated environment with 100 end users and 4 BSs.”