Federated Learning for Cybersecurity: Concepts, Challenges, and Future Directions

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 5; pp. 3501 - 3509
• Main Authors: Alazab, Mamoun, RM, Swarna Priya, M, Parimala, Maddikunta, Praveen Kumar Reddy, Gadekallu, Thippa Reddy, Pham, Quoc-Viet
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-05-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Ash; Attack detection; authentication; Blockchains; cyberattacks; Cybersecurity; Data models; Data privacy; decentralized; Federated learning; federated learning (FL); Hospitals; Medical diagnostic imaging; Privacy; Real time; Security; trust management
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3119038

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.