文献类型： 会议论文
第一作者： Snehlata Mishra
作者： Snehlata Mishra 1 ; Ritu Tondon 1 ; Narendra Pal Singh Rathore 2 ;
作者机构： 1.Computer Science & Engineering, SAGE University, Indore, India
2.Computer Science & Engineering, Acropolis Institute of Technology and Reserach, Indore, India
关键词： Training;Federated learning;Reviews;Soft sensors;Distributed databases;Collaboration;Medical services;Data models;Drug discovery;Medical diagnostic imaging
会议名称： International Conference on Advances in Computing Research on Science Engineering and Technology
主办单位：
页码： 1-5
摘要： Federated learning has become a game-changing paradigm in healthcare, offering secure and private federated model training across dispersed data sources. This thorough analysis examines the uses, developments, and challenges of federated learning in healthcare. Beginning with an elucidation of its fundamental principles, we examine its potential to revolutionize healthcare delivery by facilitating collaborative model training without data sharing. We survey diverse applications, including disease diagnosis, personalized treatment recommendations, drug discovery, and population health management, highlighting federated learning's impact on improving patient outcomes and healthcare efficiency. Technological advancements, such as differential privacy and secure aggregation, driving federated learning adoption, are explored. Despite its promise, federated learning faces challenges including data heterogeneity, model communication overhead, privacy concerns, and regulatory barriers. We discuss these challenges and propose solutions and future research directions. This review provides a comprehensive understanding of federated learning's role in healthcare, addressing opportunities, limitations, and future prospects.
分类号： tp3-53
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