Knowledge Commons of Institute of Automation,CAS
CFSL: A Credible Federated Self-Learning Framework | |
Zhang, Weishan1; Bao, Zhicheng1; Liu, Yuru1; Xu, Liang2; Lu, Qinghua3; Ning, Huansheng2; Wang, Xiao4; Yang, Su5; Wang, Fei-Yue4; Li, Zengxiang6 | |
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 | |
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. |
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