Federated Learning in Cybersecurity: Privacy-Preserving AI for Threat Detection

Abstract

With the increasing frequency and sophistication of cyberattacks, traditional centralized machine learning models are facing challenges in maintaining both security and privacy. Federated Learning (FL) has emerged as a promising solution, enabling distributed machine learning while ensuring data privacy by keeping the data decentralized and local. This research investigates the application of Federated Learning in the field of cybersecurity, particularly for threat detection. It delves into the mechanisms by which FL preserves user privacy, provides robust defense strategies, and enhances the accuracy of detection systems. Furthermore, we explore various case studies, conduct experiments comparing FL-based models with conventional centralized approaches, and analyze the performance metrics of these systems in real-world environments. Our findings suggest that FL not only improves the privacy of sensitive data but also facilitates more accurate and scalable threat detection without compromising system performance.