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

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.

Automated summary

This article presents the application of federated learning in enhancing cybersecurity and preventing cyberattacks in real-time scenarios. The authors conducted a comprehensive survey on various federated learning models developed by researchers for authentication, privacy, trust management, and attack detection. Real-time use cases and the adoption of federated learning for data privacy and system performance improvement are also discussed. The article concludes with prominent challenges and future directions for researchers to focus on.