Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula都市数字工程中心）
Yunlong Lu, Xiaohong Huang, Ke Zhang, Sabita Maharjan, Yan Zhang
Emerging technologies such as digital twins and 6th Generation mobile networks (6G) 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 paper, 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 multi-agent 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 method.
In this paper, we study a new latency optimization problem for blockchain-based federated learning (BFL) in multi-server edge computing. In this system model, distributed mobile devices (MDs) communicate with a set of edge servers (ESs) to handle both machine learning (ML) model training and block mining simultaneously. To assist the ML model training for resource-constrained MDs, we develop an offloading strategy that enables MDs to transmit their data to one of the associated ESs. We then propose a new decentralized ML model aggregation solution at the edge layer based on a consensus mechani…
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing
Knight Foundation School of Computing and Information Sciences, Florida International University (FIU)（佛罗里达国际大学奈特基金会计算与信息科学学院）；School of Computing, Southern Illinois University（南伊利诺伊大学计算学院）；Enterprise and Logistics Engineering program at FIU（佛罗里达国际大学企业与物流工程项目）
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness
Guangdong University of Technology（广东工业大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Nanyang Technological University（南洋理工大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子工程与光电技术学院）；School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；Jiangsu Second Normal University（江苏第二师范学院）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）；Department of Electronic Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；National Mobile Communication Research Laboratory, Southeast University（东南大学国家移动通信研究实验室）
Northwestern Polytechnical University（西北工业大学）；Xidian University（西安电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（西穆拉都会数字工程中心）
Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial devices to achieve Industry 4.0 benefits. In this paper, we consider a new architecture of digital twin empowered Industrial IoT where digital twins capture the characteristics of industrial devices to assist federated learning. Noticing that digital twins may bring estimation deviations from the actual value of device state, a trusted based aggregation is proposed in federated learning to alleviate the effects of such deviation. We adaptively adjust the aggregation fre…
Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks
School of Science and Technology, Hong Kong Metropolitan University（香港都会大学科学技术学院）；School of Intelligent Systems Science and Engineering, Jinan University（暨南大学智能系统科学与工程学院）；Zhejiang University/University of Illinois Urbana Champaign Institute, Zhejiang University（浙江大学浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究院）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究实验室）；Department of Informatics, University of Oslo（奥斯陆大学信息学系）
University of Waterloo（滑铁卢大学）；Carleton University（卡尔顿大学）；Toronto Metropolitan University（多伦多都会大学）
In this paper, we design a resource management scheme to support stateful applications, which will be prevalent in 6G networks. Different from stateless applications, stateful applications require context data while executing computing tasks from user terminals (UTs). Using a multi-tier computing paradigm with servers deployed at the core network, gateways, and base stations to support stateful applications, we aim to optimize long-term resource reservation by jointly minimizing the usage of computing, storage, and communication resources and the cost from reconfiguring resource reservation. T…
Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西大学智能组网与场景系统重点实验室（桂林电子科技大学信息与通信学院））；Research Institute of Highway Ministry of Transport（交通运输部公路科学研究院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Architecture and Transportation Engineering, Guilin University of Electronic Technology（桂林电子科技大学建筑与交通工程学院）
University of Electronic Science and Technology of China（电子科技大学）；Simula Metropolitan Center for Digital Engineering（Simula都市数字工程中心）；University of Oslo（奥斯陆大学）
Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of data and multimedia content to be cached in proximity to vehicles. However, high mobility of vehicles and dynamic wireless channel condition make it challenge to design an optimal content caching policy. Further, with much sensitive personal information, vehicles may be not willing to caching their contents to an untrusted caching provider. Deep Reinforcement Learning (DRL) is an emerging technique to solve the problem with high-dimensional and time-varying features. Permission blockchain is able to establish a sec…
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；Department of Computing, Hong Kong Polytechnic University（香港理工大学计算机系）；Data61, CSIRO（数据61，CSIRO）；Department of Electronics Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）
Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学通信与信息工程学院）；Electronic Information and Networking Research Institute, Chongqing University of Posts and Telecommunications（重庆邮电大学电子信息与网络研究院）；Concordia University（康考迪亚大学）
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA)（加泰罗尼亚电信技术中心（CTTC/CERCA））
Motivated by the heterogeneous nature of devices participating in large-scale Federated Learning (FL) optimization, we focus on an asynchronous server-less FL solution empowered by blockchain technology. In contrast to mostly adopted FL approaches, which assume synchronous operation, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates. The asynchronous setting fits well with the federated optimization idea in practical large-scale settings with heterogeneous clients. Thus, it potentially leads to higher efficiency in terms of communication…
PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Security
Department of Civil Engineering, University of Texas at Arlington（德克萨斯大学阿灵顿分校土木工程系）；Department of Information Systems, Statistics and Management Science, The University of Alabama（阿拉巴马大学信息系统、统计与管理科学系）；College of Arts and Letters, School of Art, Florida Atlantic University（佛罗里达国际大学艺术与文学学院、艺术学院）；Department of Environment and Energy, Science and Research Branch, Islamic Azad University（伊斯兰阿扎德大学环境与能源系、科学与研究分支）；Department of Quantity Surveying, University of Malaya（马来大学工程量系）；Department of Computer Engineering, Islamic Azad University of Hamadan（伊斯兰阿扎德大学哈马丹分校计算机工程系）；Department of Civil, Construction, and Environmental Engineering, University of Alabama（阿拉巴马大学土木、建筑与环境工程系）
Lero – the Irish Software Research Centre（Lero——爱尔兰软件研究中心）；National University of Ireland（爱尔兰国立大学）
The knowledge, embodied in machine learning models for intelligent systems, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labelling, network training, and fine-tuning of models. Sharing and reuse of these elaborated models between intelligent systems deployed in a different environment, which is known as transfer learning, would facilitate the adoption of services for the users and accelerates the uptake of intelligent systems in environments such as smart building and smart city applications. In this context, the communication and kn…
Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；Department of Electrical and Computer Engineering, Auburn University（奥本大学电气与计算机工程系）
A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Nanyang Technological University（南洋理工大学）；Guangdong University of Technology（广东工业大学）；Concordia University（康考迪亚大学）；University of Waterloo（滑铁卢大学）
Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment
Shanghai Key Laboratory of Intelligent Information Processing Lab, Fudan University, China（上海智能信息处理实验室，复旦大学，中国）；Turku Intelligent Embedded and Robotic Systems (TIERS) Lab, Faculty of Technology, University of Turku, Finland（图尔库智能嵌入式与机器人系统（TIERS）实验室，技术学院，图尔库大学，芬兰）；Department of Engineering and Architecture, University of Parma, Italy（工程与建筑系，帕尔马大学，意大利）；TIERS Lab, University of Turku, Finland（TIERS实验室，图尔库大学，芬兰）；School of Information Science and Technology, Fudan University, Shanghai, China（信息科学与技术学院，复旦大学，上海，中国）；Robotics and Autonomous Systems at the University of Turku（图尔库大学的机器人与自主系统）
Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for the Metaverse applications and hence, real-time digital twinning of real-world scenes is increasing. Nevertheless, the replication of 2D physical world images into 3D virtual objects is computationally intensive and requires computation offloading. The disparity in transmitted object dimension (2D as opposed to 3D) leads to asymmetric data sizes in uplink (UL) and downlink (DL). To ensure the reliability and low latency of the system, we…
Nanyang Technological University（南洋理工大学）；Guangdong University of Technology（广东工业大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Sungkyunkwan University（成均馆大学）；University of Sydney（悉尼大学）
With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth from 2022. Not limited to content generation, DGMs are also widely adopted in Internet of Things, Metaverse, and digital twin, due to their outstanding ability to represent complex patterns and generate plausible samples. In this article, we explore the applications of DGMs in a crucial task, i.e., improving the efficiency of wireless network management. Specifically, we firstly overview the generative AI, as well as three representative DGMs. Then, a DGM-empowered…
