Federated Learning for Cybersecurity: Concepts, Challenges and Future Directions

Mamoun Alazab, Swarna Priya R M, Parimala M, Praveen Reddy, Thippa Reddy Gadekallu, Quoc Viet Pham

    Research output: Contribution to journalArticlepeer-review


    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 languageEnglish
    Pages (from-to)3501 - 3509
    Number of pages9
    JournalIEEE Transactions on Industrial Informatics
    Issue number5
    Early online dateOct 2021
    Publication statusPublished - 1 May 2022


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