Secure future healthcare applications through federated learning approaches

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dc.authorid0000-0002-8076-3678
dc.contributor.authorTabassum, Maliha
dc.contributor.authorKuzlu, Murat
dc.contributor.authorÇatak, Ferhat Özgür
dc.contributor.authorSarp, Salih
dc.contributor.authorŞahinbaş, Kevser
dc.date.accessioned2024-01-16T10:37:42Z
dc.date.available2024-01-16T10:37:42Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractThe healthcare field is so sensitive to data privacy and security due to including medical and personal information. Almost all healthcare applications are required to increase data security and privacy, which use traditional machine learning approaches relying on centralized systems, both computing resources and the entirety of the data. Federated learning, a sort of machine learning technique, has been used to exactly address this issue. The training data is disseminated across numerous devices in federated learning, and the learning process is collaborative. There are numerous privacy attacks on Deep Learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in healthcare applications that use sensitive medical data. This paper provides a comprehensive review of federated learning on future healthcare applications. It also discusses the types of federated learning along with its implementation in healthcare applications.
dc.identifier.citationTabassum, M., Kuzlu, M., Çatak, F. Ö., Sarp, S. ve Şahinbaş, K. (2024). Secure future healthcare applications through federated learning approaches. 2nd International Conference on Advanced Engineering, Technology and Applications, ICAETA içinde (214-225. ss.). Istanbul, 10-11 March 2023. https://dx.doi.org/10.1007/978-3-031-50920-9_17
dc.identifier.doi10.1007/978-3-031-50920-9_17
dc.identifier.endpage225
dc.identifier.isbn9783031509193
dc.identifier.issn1865-0929
dc.identifier.scopus2-s2.0-85180759418
dc.identifier.scopusqualityQ4
dc.identifier.startpage214
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-031-50920-9_17
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12150
dc.identifier.volume1983
dc.indekslendigikaynakScopus
dc.institutionauthorŞahinbaş, Kevser
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartof2nd International Conference on Advanced Engineering, Technology and Applications, ICAETAen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Intelligence
dc.subjectDeep Learning
dc.subjectFederated Learning
dc.subjectHealthcare
dc.subjectMachine Learning
dc.subjectPrivacy
dc.titleSecure future healthcare applications through federated learning approaches
dc.typeConference Object

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