Abstract
Federated learning (FL) is a recent development in artificial intelligence, which is typically based on the concept of decentralized data. As cyberattacks are frequently happening in the various applications deployed in real time, most industrialists are hesitating to move forward in adopting the technology of the Internet of Everything. This article aims to provide an extensive study on how FL could be utilized for providing better cybersecurity and prevent various cyberattacks in real time. We present an extensive survey of the various FL models currently developed by researchers for providing authentication, privacy, trust management, and attack detection. We also discuss few real-time use cases that have been deployed recently and how FL is adopted in them for preserving privacy of data and improving the performance of the system. Based on the study, we conclude this article with some prominent challenges and future directions on which the researchers can focus for adopting FL in real-time scenarios.
Original language | English |
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Pages (from-to) | 3501 - 3509 |
Number of pages | 9 |
Journal | IEEE Transactions on Industrial Informatics |
Volume | 18 |
Issue number | 5 |
Early online date | Oct 2021 |
DOIs | |
Publication status | Published - 1 May 2022 |
Bibliographical note
Funding Information:The work of Mamoun Alazab was supported by the National Research Foundation of Korea under Grant NRF- 2021S1A5A2A03064391. The work of Quoc-Viet Pham was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) under Grant NRF-2019R1C1C1006143 and in part by BK21 Four, Korean Southeast Center for the 4th Industrial Revolution Leader Education. Paper no. TII-21-2601.
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