A survey on federated learning: challenges and applications
文献类型：期刊论文
| 作者 | Wen, Jie3; Zhang, Zhixia3; Lan, Yang2; Cui, Zhihua2; Cai, Jianghui2; Zhang, Wensheng1
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| 刊名 | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
 |
| 出版日期 | 2022-11-11 |
| 页码 | 23 |
| ISSN号 | 1868-8071 |
| 关键词 | Federated learning Machine learning Privacy protection Personalized federated learning |
| DOI | 10.1007/s13042-022-01647-y |
| 通讯作者 | Cui, Zhihua(cuizhihua@tyustedu.cn) |
| 英文摘要 | Federated learning (FL) is a secure distributed machine learning paradigm that addresses the issue of data silos in building a joint model. Its unique distributed training mode and the advantages of security aggregation mechanism are very suitable for various practical applications with strict privacy requirements. However, with the deployment of FL mode into practical application, some bottlenecks appear in the FL training process, which affects the performance and efficiency of the FL model in practical applications. Therefore, more researchers have paid attention to the challenges of FL and sought for various effective research methods to solve these current bottlenecks. And various research achievements of FL have been made to promote the intelligent development of all application areas with privacy restriction. This paper systematically introduces the current researches in FL from five aspects: the basics knowledge of FL, privacy and security protection mechanisms in FL, communication overhead challenges and heterogeneity problems of FL. Furthermore, we make a comprehensive summary of the research in practical applications and prospect the future research directions of FL. |
| WOS关键词 | OBJECTIVE EVOLUTIONARY ALGORITHM ; OPTIMIZATION ALGORITHM ; INTRUSION DETECTION ; ENHANCING SECURITY ; BLOCKCHAIN ; FRAMEWORK ; IMAGE ; MODEL ; RECOMMENDATION ; CLASSIFICATION |
| 资助项目 | National Key Research and Development Program of China[2018YFC1604000] ; National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; Science and Technology Development Foundation of the Central Guiding Local[YDZJSX2021A038] ; China University Industry-University-Research Collaborative Innovation Fund (Future Network Innovation Research and Application Project)[2021FNA04014] ; Outstanding Innovation Project for Graduate Students of Taiyuan University of Science and Technology[XCX211004] ; Outstanding Innovation Project for Graduate Students of Taiyuan University of Science and Technology[XCX212081] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | SPRINGER HEIDELBERG |
| WOS记录号 | WOS:000881886400002 |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Science and Technology Development Foundation of the Central Guiding Local ; China University Industry-University-Research Collaborative Innovation Fund (Future Network Innovation Research and Application Project) ; Outstanding Innovation Project for Graduate Students of Taiyuan University of Science and Technology |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50684]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Cui, Zhihua |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing, Peoples R China 2.Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan, Peoples R China 3.Taiyuan Univ Sci & Technol, Sch Elect Informat Engn, Taiyuan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wen, Jie,Zhang, Zhixia,Lan, Yang,et al. A survey on federated learning: challenges and applications[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2022:23. |
| APA | Wen, Jie,Zhang, Zhixia,Lan, Yang,Cui, Zhihua,Cai, Jianghui,&Zhang, Wensheng.(2022).A survey on federated learning: challenges and applications.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,23. |
| MLA | Wen, Jie,et al."A survey on federated learning: challenges and applications".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2022):23. |
入库方式： OAI收割
来源：自动化研究所
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