An Improved Federated Learning Algorithm for Privacy Preserving in Cybertwin-Driven 6G System

doi:10.1109/TII.2022.3149516

	An Improved Federated Learning Algorithm for Privacy Preserving in Cybertwin-Driven 6G System
	Miao Yang1,2,3 ; Ximin Wang2 ; Hua Qian 1,2,3; Yongxin Zhu 4; Hongbin Zhu2 ; Mohsen Guizani 5; Victor Chang 6
	2022-10-01
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (IF:11.7[JCR-2023],11.4[5-Year])
ISSN	1941-0050
卷号	18 期号:10
发表状态	已发表
DOI	10.1109/TII.2022.3149516
摘要	With the expected explosive use of the Internet of Everything in sixth generation (6G), the cybertwin network is able to convert user information to digital assets and provide extensive services. However, protecting and enhancing privacy of the processed and transmitted data in cybertwin-driven 6G is still in its infancy. Federated learning (FL) is a nascent distributed machine learning paradigm that is able to facilitate privacy protection in cybertwin networks. In a cybertwin network, imbalanced data distribution of the clients can increase the bias of the global model and sacrifice the performance of the FL model. Prior research work dealing with imbalanced data requires extra data information exchanged between clients and the server, which increases the risk of privacy leakage. To avoid privacy leakage, we design an estimation algorithm to determine the distribution of local data collected at the clients without the awareness of specific raw data. We consider two scenarios in FL: 1) the server could receive the individual trained model for each selected device and 2) the server could receive the aggregated model from the selected clients. We formulate two device selection problems to improve the training performance of the aforementioned scenarios. We develop two online learning algorithms to tackle the selection problems for both individual model uploading and aggregated model uploading. The proposed algorithms are conducted on the server, thereby avoiding privacy leakage and extra computation at the clients. We validate the effectiveness of the proposed client selection algorithms with sufficient experiments in cybertwin-driven 6G networks.
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收录类别	SCI ; SCIE ; EI
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/159574
专题	信息科学与技术学院信息科学与技术学院_PI研究组_周勇组信息科学与技术学院_硕士生信息科学与技术学院_博士生
作者单位	1.Shanghai Advanced Research Institute, Chinese Academy of Sciences (CAS), Shanghai, China 2.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 3.University of Chinese Academy of Sciences, Beijing, China 4.Shanghai Advanced Research Institute, CAS, Shanghai, China 5.Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE 6.Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, U.K.
第一作者单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Miao Yang,Ximin Wang,Hua Qian,et al. An Improved Federated Learning Algorithm for Privacy Preserving in Cybertwin-Driven 6G System[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2022,18(10).
APA	Miao Yang.,Ximin Wang.,Hua Qian.,Yongxin Zhu.,Hongbin Zhu.,...&Victor Chang.(2022).An Improved Federated Learning Algorithm for Privacy Preserving in Cybertwin-Driven 6G System.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,18(10).
MLA	Miao Yang,et al."An Improved Federated Learning Algorithm for Privacy Preserving in Cybertwin-Driven 6G System".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 18.10(2022).