A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

作     者：Deepak Upreti Eunmok Yang Hyunil Kim Changho Seo 

作者机构：Department of Convergence ScienceKongju National UniversityChungcheongnam-doGongju-si32588South Korea Department of Information SecurityCryptologyand MathematicsKookmin UniversitySeoul02707South Korea Department of Information and Communication EngineeringChosun UniversityGwangju61452South Korea Basic Science Research InstitutionKongju National UniversityChungcheongnam-doGongju-si32588South Korea 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第140卷第9期

页      面：2239-2274页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 12[管理学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0803[工学-光学工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by a research fund from Chosun University 2023 

主　　题：Federated learning artificial intelligence machine learning privacy healthcare 

摘      要：Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and *** involves constructing machine learning models using datasets spread across several data centers,including medical facilities,clinical research facilities,Internet of Things devices,and even mobile *** main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information,reducing the risk of data loss,privacy breaches,or data *** application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources,such as patient records,medical imaging,wearable devices,and clinical research *** research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in *** evaluates the effectiveness of federated learning strategies in the field of *** offers a systematic analysis of federated learning in the healthcare domain,encompassing the evaluation metrics *** addition,this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.