A modern approach to data privacy with federated learning

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dc.authorid0000-0001-8519-7612
dc.contributor.authorKalkavan, Ziya Can
dc.contributor.authorŞahinaslan, Ender
dc.contributor.authorŞahinaslan, Önder
dc.contributor.authorid122635
dc.date.accessioned2023-09-28T13:31:00Z
dc.date.available2023-09-28T13:31:00Z
dc.date.issued2023
dc.departmentFakülteler, Mühendislik, Mimarlık ve Tasarım Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractToday, information technologies and their usage areas are increasing day by day. Advanced technologies such as the internet of things, smart devices and applications, machine learning and arti cial intelligence are a driving force in the spread of their usage areas. The increase in prevalence and use also increases the production and sharing of data. This increase causes various security problems and concerns in terms of data privacy. Therefore, a balance has to be struck between the need for data sharing and its security. For this purpose, the use of federated learning methods has been examined. Traditional data sharing methods focus on centralized solutions for the processing of private and sensitive data of data subjects, but this causes various problems and raises concerns in the sharing of sensitive data. In the federated learning model, it trains locally without data sharing. It has a distributed arti cial intelligence approach that can run di erent resources together. Thus, it o ers an alternative solution that can help address data privacy concerns arising from the traditional method. In this study, the basic principles, usage areas, advantages and difficulties of federated learning, which is also accepted as a modern approach in data privacy, are discussed. The data and examples obtained in the study will be presented.
dc.identifier.citationKalkavan, Z. C., Şahinaslan, E., Şahinaslan, Ö. (2023). A modern approach to data privacy with federated learning. Ö. Değer ve H. Çakallı (Ed.), 7th international conference of mathematical sciences ICMS 2023 (72. ss.). Istanbul: Maltepe University.
dc.identifier.isbn9786052124291
dc.identifier.urihttps://dspace.mudanya.edu.tr/handle/20.500.14362/162
dc.institutionauthorŞahinaslan, Ender
dc.language.isoen
dc.publisherMaltepe University
dc.relation.journal7th international conference of mathematical sciences ICMS 2023
dc.relation.publicationcategoryKonferans Ögesi- Uluslararası- Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectInformation security
dc.subjectFederated learning
dc.subjectData privacy
dc.subjectTechnology and innovation
dc.subjectArtificial intelligence
dc.titleA modern approach to data privacy with federated learning
dc.typeSunum
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