A review of privacy-preserving federated learning, deep learning, and machine learning IIoT and IoTs solutions

Book chapter


Obarafor, Victor, Qi, Man and Zhang, L. 2023. A review of privacy-preserving federated learning, deep learning, and machine learning IIoT and IoTs solutions. in: 2023 8th IEEE International Conference on Signal and Image Processing (ICSIP) Wuxi, China IEEE. pp. 1074-1078
AuthorsObarafor, Victor, Qi, Man and Zhang, L.
Abstract

Internet of Things (IoT) is a growing computing trend that encompasses every connected thing. Over the recent years, IoT has recorded an exponential growth, leading to billions of smart devices, and still increasing. In contrast to other computing devices, some IoTs generate large amount of data, however, this has become a source of concern as data could contain users’ privacy which should be protected at all costs against any potential security breach incident. Securing IoT is very significant with its continuous adoption and use, hence, researchers have proposed several security mechanisms and techniques to safeguard and protect IoT systems and devices. Notwithstanding, there are some research gaps that are yet to be addressed irrespective of the relevant contributions made in protection of users’ privacy and confidentiality using IoTs. In this paper, the researcher solely focused on a review of AI approaches leveraged by researchers in protecting the device and data security aspects of privacy specifically for de-centralised architecture based industrial IoT systems (IIOTs) as they are generating large amount of data and are safety critical. The results achieved, unresolved issues and recommendations for future research are contained in this review.

KeywordsInternet of things; Data security; Federated learning; Privacy
Page range1074-1078
Year2023
Book title2023 8th IEEE International Conference on Signal and Image Processing (ICSIP)
PublisherIEEE
Output statusPublished
Place of publicationWuxi, China
ISBN9798350397932
Publication dates
Online08 Jul 2023
Publication process dates
Deposited18 Oct 2023
Digital Object Identifier (DOI)https://doi.org/10.1109/icsip57908.2023.10270935
Official URLhttps://ieeexplore.ieee.org/document/10270935
Related URLhttp://icispp.org/index.html
FunderCanterbury Christ Church University
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Journal2023 8th International Conference on Signal and Image Processing (ICSIP)
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