Differentially Private Federated Learning via Reconfigurable Intelligent Surface

doi:10.1109/JIOT.2022.3168066

	Differentially Private Federated Learning via Reconfigurable Intelligent Surface
	Yuhan Yang; Yong Zhou; Youlong Wu; Yuanming Shi
	2022-10-15
发表期刊	IEEE INTERNET OF THINGS JOURNAL
ISSN	2327-4662
EISSN	2327-4662
卷号	9 期号:20
发表状态	已发表
DOI	10.1109/JIOT.2022.3168066
摘要	Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them. It spans a wide scope of applications from Internet-of-Things (IoT) to biomedical engineering and drug discovery. To support low-latency and high-privacy FL over wireless networks, in this paper, we propose a reconfigurable intelligent surface (RIS) empowered over-the-air FL system to alleviate the dilemma between learning accuracy and privacy. This is achieved by simultaneously exploiting the channel propagation reconfigurability with RIS for boosting the receive signal power, as well as waveform superposition property with over-the-air computation (AirComp) for fast model aggregation. By considering a practical scenario where high-dimensional local model updates are transmitted across multiple communication blocks, we characterize the convergence behaviors of the differentially private federated optimization algorithm. We further formulate a system optimization problem to optimize the learning accuracy while satisfying privacy and power constraints via the joint design of transmit power, artificial noise, and phase shifts at RIS, for which a two-step alternating minimization framework is developed. Simulation results validate our systematic, theoretical, and algorithmic achievements and demonstrate that RIS can achieve a better trade-off between privacy and accuracy for over-the-air FL systems. IEEE
关键词	Bioinformatics Constraint satisfaction problems Internet of things Learning systems Optimization Atmospheric modeling Biomedical monitoring Computational modelling Differential privacies Differential privacy. Federated learning Over the airs Over-the-air computation Performances evaluation Privacy Reconfigurable Reconfigurable intelligent surface Wireless communications
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收录类别	EI ; SCI ; SCIE
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[U20A20159] ; National Nature Science Foundation of China (NSFC)[61901267] ; Natural Science Foundation of Shanghai[21ZR1442700]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000865097300020
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20221812061978
EI主题词	Economic and social effects
EI分类号	461.8.2 Bioinformatics ; 722.3 Data Communication, Equipment and Techniques ; 723 Computer Software, Data Handling and Applications ; 921 Mathematics ; 921.5 Optimization Techniques ; 971 Social Sciences
原始文献类型	Article in Press
来源库	IEEE
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/180951
专题	信息科学与技术学院信息科学与技术学院_PI研究组_吴幼龙组信息科学与技术学院_PI研究组_石远明组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
作者单位	School of Information Science and Technology, ShanghaiTech University, Shanghai, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Yuhan Yang,Yong Zhou,Youlong Wu,et al. Differentially Private Federated Learning via Reconfigurable Intelligent Surface[J]. IEEE INTERNET OF THINGS JOURNAL,2022,9(20).
APA	Yuhan Yang,Yong Zhou,Youlong Wu,&Yuanming Shi.(2022).Differentially Private Federated Learning via Reconfigurable Intelligent Surface.IEEE INTERNET OF THINGS JOURNAL,9(20).
MLA	Yuhan Yang,et al."Differentially Private Federated Learning via Reconfigurable Intelligent Surface".IEEE INTERNET OF THINGS JOURNAL 9.20(2022).