CFSL: A Credible Federated Self-Learning Framework

doi:10.1109/JIOT.2023.3286398

	CFSL: A Credible Federated Self-Learning Framework
	Zhang, Weishan 1; Bao, Zhicheng 1; Liu, Yuru 1; Xu, Liang 2; Lu, Qinghua 3; Ning, Huansheng 2; Wang, Xiao4 ; Yang, Su 5; Wang, Fei-Yue4 ; Li, Zengxiang 6
发表期刊	IEEE INTERNET OF THINGS JOURNAL
ISSN	2327-4662
	2023-12-15
卷号	10 期号:24 页码:21349-21362
通讯作者	Zhang, Weishan(zhangws@upc.edu.cn)
摘要	Federated learning can collaboratively train AI models while protecting data privacy. In practical industry environment, non-independent and identically distributed (Non-IID) characteristics of data affect the effectiveness of federated learning. Personalized federated learning can help resolve this, but it cannot adapt to unknown data. In addition, practical applications also call for trusted training environment and remain stable when there are security threats. In this article, we propose a credible federated self-learning (CFSL), based on the idea of hypernetwork supported by blockchain to achieve secured, credible, personalized federated self-learning, especially, for unknown data in Non-IID environment. Extensive experiments on three Non-IID data sets demonstrate the capabilities on adaptive resilience for security attacks and on accuracy of recognizing unknown objects, with good performance at the same time. CFSL outperforms the existing personalized federated learning methods, with an increase in average accuracy by 4.11%.
关键词	Federated learning Data models Training Internet of Things Blockchains Adaptation models Smart contracts Blockchain consensus federated learning personalization self-learning
DOI	10.1109/JIOT.2023.3286398
关键词[WOS]	INTERNET
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
项目资助者	National Natural Science Foundation of China
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:001142524100004
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55441
专题	多模态人工智能系统全国重点实验室
通讯作者	Zhang, Weishan
作者单位	1.China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China 2.Beijing Univ Sci & Technol, Coll Comp & Commun Engn, Beijing 100083, Peoples R China 3.CSIRO, Data61, Sydney, NSW 2070, Australia 4.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China 5.Fudan Univ, Coll Comp Sci, Shanghai 200437, Peoples R China 6.ENN Grp, Inst Digital Res, Langfang 065001, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Weishan,Bao, Zhicheng,Liu, Yuru,et al. CFSL: A Credible Federated Self-Learning Framework[J]. IEEE INTERNET OF THINGS JOURNAL,2023,10(24):21349-21362.
APA	Zhang, Weishan.,Bao, Zhicheng.,Liu, Yuru.,Xu, Liang.,Lu, Qinghua.,...&Li, Zengxiang.(2023).CFSL: A Credible Federated Self-Learning Framework.IEEE INTERNET OF THINGS JOURNAL,10(24),21349-21362.
MLA	Zhang, Weishan,et al."CFSL: A Credible Federated Self-Learning Framework".IEEE INTERNET OF THINGS JOURNAL 10.24(2023):21349-21362.