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AuthorAbdulrahman, Sawsan
AuthorTout, Hanine
AuthorOuld-Slimane, Hakima
AuthorMourad, Azzam
AuthorTalhi, Chamseddine
AuthorGuizani, Mohsen
Available date2022-11-06T11:28:46Z
Publication Date2021-04-01
Publication NameIEEE Internet of Things Journal
Identifierhttp://dx.doi.org/10.1109/JIOT.2020.3030072
CitationAbdulRahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2020). A survey on federated learning: The journey from centralized to distributed on-site learning and beyond. IEEE Internet of Things Journal, 8(7), 5476-5497.‏
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103307200&origin=inward
URIhttp://hdl.handle.net/10576/35858
AbstractDriven by privacy concerns and the visions of deep learning, the last four years have witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An emerging model, called federated learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privacy-preserving decentralized approach, which keeps raw data on devices and involves local ML training while eliminating data communication overhead. A federation of the learned and shared models is then performed on a central server to aggregate and share the built knowledge among participants. This article starts by examining and comparing different ML-based deployment architectures, followed by in-depth and in-breadth investigation on FL. Compared to the existing reviews in the field, we provide in this survey a new classification of FL topics and research fields based on thorough analysis of the main technical challenges and current related work. In this context, we elaborate comprehensive taxonomies covering various challenging aspects, contributions, and trends in the literature, including core system models and designs, application areas, privacy and security, and resource management. Furthermore, we discuss important challenges and open research directions toward more robust FL systems.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectArtificial intelligence (AI)
deep learning (DL)
distributed intelligence
federated learning (FL) applications
FL
machine learning (ML)
privacy
resource management
security
TitleA survey on federated learning: The journey from centralized to distributed on-site learning and beyond
TypeArticle
Pagination5476-5497
Issue Number7
Volume Number8
dc.accessType Abstract Only


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