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A Survey of Federated Learning From Data Perspective in the Healthcare Domain: Challenges, Methods, and Future Directions

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dc.citedby8
dc.contributor.authorTaha Z.K.en_US
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorKadirgama K.en_US
dc.contributor.authorBenedict F.en_US
dc.contributor.authorTan J.D.en_US
dc.contributor.authorBalasubramaniam Y.A.L.en_US
dc.contributor.authorid57202301078en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid12761486500en_US
dc.contributor.authorid57194591957en_US
dc.contributor.authorid38863172300en_US
dc.contributor.authorid57189520843en_US
dc.date.accessioned2024-10-14T03:21:37Z
dc.date.available2024-10-14T03:21:37Z
dc.date.issued2023
dc.description.abstractRecent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients. DL needs more data and diversity to build a more accurate system. To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges. To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed. FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy. This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcareen_US
dc.description.abstract(2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiencyen_US
dc.description.abstractand (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications. An extensive review of the literature on FL from a data-centric perspective was conducted. We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and January 2023. A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers. In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification. We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2023.3267964
dc.identifier.epage45735
dc.identifier.scopus2-s2.0-85153534096
dc.identifier.spage45711
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85153534096&doi=10.1109%2fACCESS.2023.3267964&partnerID=40&md5=65c93989d4a56d78861605d42848a85c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34673
dc.identifier.volume11
dc.pagecount24
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.subjectEnergy
dc.subjectfederated learning
dc.subjectnon-independent identical distribution
dc.subjectprivacy and security
dc.subjectComputer aided diagnosis
dc.subjectComputer aided instruction
dc.subjectData privacy
dc.subjectDeep learning
dc.subjectHealth care
dc.subjectIndustrial research
dc.subjectEnergy
dc.subjectFederated learning
dc.subjectHealth care application
dc.subjectIndependent identical distributions
dc.subjectMedical services
dc.subjectNon-independent identical distribution
dc.subjectPrivacy
dc.subjectPrivacy and security
dc.subjectSecurity
dc.subjectInternet of things
dc.titleA Survey of Federated Learning From Data Perspective in the Healthcare Domain: Challenges, Methods, and Future Directionsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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