中国科学院机构知识库网格系统: CFSL: A Credible Federated Self-Learning Framework

CFSL: A Credible Federated Self-Learning Framework

文献类型：期刊论文


作者	Zhang, Weishan 6; Bao, Zhicheng 6; Liu, Yuru 6; Xu, Liang 5; Lu, Qinghua 4; Ning, Huansheng 5; Wang, Xiao3 ; Yang, Su 2; Wang, Fei-Yue3 ; Li, Zengxiang 1
刊名	IEEE INTERNET OF THINGS JOURNAL
出版日期	2023-12-15
卷号	10 期号:24 页码:21349-21362
ISSN号	2327-4662
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
通讯作者	Zhang, Weishan(zhangws@upc.edu.cn)
英文摘要	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%.
WOS关键词	INTERNET
资助项目	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001142524100004
资助机构	National Natural Science Foundation of China
源URL	[http://ir.ia.ac.cn/handle/173211/55440]
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.ENN Grp, Inst Digital Res, Langfang 065001, Peoples R China 2.Fudan Univ, Coll Comp Sci, Shanghai 200437, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China 4.CSIRO, Data61, Sydney, NSW 2070, Australia 5.Beijing Univ Sci & Technol, Coll Comp & Commun Engn, Beijing 100083, Peoples R China 6.China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, 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.

入库方式： OAI收割

来源：自动化研究所

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CFSL: A Credible Federated Self-Learning Framework

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