Trustworthy Federated Learning via Blockchain

doi:10.1109/JIOT.2022.3201117

	Trustworthy Federated Learning via Blockchain
	Yang, Zhanpeng1,2,3 ; Shi, Yuanming1 ; Zhou, Yong1 ; Wang, Zixin1 ; Yang, Kai 4
	2023
发表期刊	IEEE INTERNET OF THINGS JOURNAL
ISSN	2327-4662
EISSN	2327-4662
卷号	10 期号:1 页码:1-1
发表状态	已发表
DOI	10.1109/JIOT.2022.3201117
摘要	The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of long-term average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by deep reinforcement learning based algorithm compared with baseline algorithms. IEEE
关键词	Blockchain Deep learning Energy utilization Fault tolerance Internet protocols Learning algorithms Markov processes Network architecture Network security Reinforcement learning Resource allocation Safety engineering Block-chain Computational modelling Consensus protocols Federated learning Long-term latency minimization Minimisation Resource management Resources allocation Trustworthy artificial intelligence Wireless communications
URL	查看原文
收录类别	EI ; SCI ; SCOPUS
语种	英语
资助项目	Shanghai Rising-Star Program[22QA1406100] ; Natural Science Foundation of Shanghai[21ZR1442700] ; National Natural Science Foundation of China (NSFC)["U20A20159","62001294"]
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:000911309300008
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20223712721086
EI主题词	Optimization
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 525.3 Energy Utilization ; 722.3 Data Communication, Equipment and Techniques ; 723 Computer Software, Data Handling and Applications ; 723.3 Database Systems ; 723.4 Artificial Intelligence ; 723.4.2 Machine Learning ; 912.2 Management ; 914 Safety Engineering ; 921.5 Optimization Techniques ; 922.1 Probability Theory
原始文献类型	Article in Press
来源库	IEEE
引用统计	被引频次：37[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/229866
专题	信息科学与技术学院_硕士生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_博士生
通讯作者	Shi, Yuanming
作者单位	1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 200050, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.JD Technol Grp, Beijing 100176, Peoples R China
第一作者单位	信息科学与技术学院
通讯作者单位	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	Yang, Zhanpeng,Shi, Yuanming,Zhou, Yong,et al. Trustworthy Federated Learning via Blockchain[J]. IEEE INTERNET OF THINGS JOURNAL,2023,10(1):1-1.
APA	Yang, Zhanpeng,Shi, Yuanming,Zhou, Yong,Wang, Zixin,&Yang, Kai.(2023).Trustworthy Federated Learning via Blockchain.IEEE INTERNET OF THINGS JOURNAL,10(1),1-1.
MLA	Yang, Zhanpeng,et al."Trustworthy Federated Learning via Blockchain".IEEE INTERNET OF THINGS JOURNAL 10.1(2023):1-1.