R(2)Fed: Resilient Reinforcement Federated Learning for Industrial Applications
文献类型：期刊论文
| 作者 | Zhang, Weishan2,7; Yu, Fa2,3; Wang, Xiao1,4 ; Zeng, Xingjie2; Zhao, Hongwei2; Tian, Yonglin5; Wang, Fei-Yue5; Li, Longfei6; Li, Zengxiang3
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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| 出版日期 | 2023-08-01 |
| 卷号 | 19期号:8页码:8829-8840 |
| 关键词 | Federated learning non-independent identically distributed (non-IID) data reinforcement learning |
| ISSN号 | 1551-3203 |
| DOI | 10.1109/TII.2022.3222369 |
| 通讯作者 | Zhang, Weishan(zhangws@upc.edu.cn) |
| 英文摘要 | Federated learning has become an emerging hot research field in industry because of its ability to perform large-scale distributed learning while preserving data privacy. However, recent studies have shown that in the actual use of federated learning, there are device heterogeneity and data not identically and independently distributed (Non-IID) characteristics between client nodes, which will affect the effect of federated learning. In this work, we propose resilient reinforcement federated learning (R(2)Fed), a R(2)Fed method, which applies reinforcement learning to federated learning and uses reinforcement learning for weighted fusion of client models instead of average fusion. We conduct experiments on object detection, object classification, and sentiment classification tasks in the context of Non-IID and heterogeneity, and the experimental results show that the R(2)Fed method outperforms traditional federated learning, increasing the average accuracy by 4.7%. Experiments also demonstrate that R(2)Fed is resilient to federation attacks. |
| 资助项目 | National Natural Science Foundation of China[62072469] ; Opening Project of the State Key Laboratory for Management and Control Complex Systems, Institute of Automation, Chinese Academy of Sciences[20210114] |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001030673600026 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Opening Project of the State Key Laboratory for Management and Control Complex Systems, Institute of Automation, Chinese Academy of Sciences |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53900]  |
| 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.Qingdao Acad Intelligent Ind QAII, Qingdao 230031, Peoples R China 2.China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China 3.ENN Grp, Inst Digital Res, Langfang 065000, Peoples R China 4.Anhui Univ, Sch Artificial Intelligence, Hefei 266114, Anhui, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing 100086, Peoples R China 6.Space Star Technol Co Ltd, Beijing 100095, Peoples R China 7.Qingdao Acad Intelligent Ind QAII, Qingdao 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Weishan,Yu, Fa,Wang, Xiao,et al. R(2)Fed: Resilient Reinforcement Federated Learning for Industrial Applications[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2023,19(8):8829-8840. |
| APA | Zhang, Weishan.,Yu, Fa.,Wang, Xiao.,Zeng, Xingjie.,Zhao, Hongwei.,...&Li, Zengxiang.(2023).R(2)Fed: Resilient Reinforcement Federated Learning for Industrial Applications.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,19(8),8829-8840. |
| MLA | Zhang, Weishan,et al."R(2)Fed: Resilient Reinforcement Federated Learning for Industrial Applications".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 19.8(2023):8829-8840. |
入库方式： OAI收割
来源：自动化研究所
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