Privacy-Preserving Collaborative Intelligence for IoT Cybersecurity: A Federated Learning Approach

Abstract

The exponential growth of Internet of Things (IoT) devices has created unprecedented challenges in cybersecurity, particularly in maintaining privacy while ensuring effective threat detection across distributed networks. This article presents a comprehensive analysis of federated learning (FL) approaches for privacy-preserving cyber threat detection in IoT environments. Through extensive review of current literature and methodologies, we examine how federated learning paradigms address the dual challenge of maintaining data privacy while enabling collaborative threat intelligence across distributed IoT networks. Our analysis reveals that federated learning frameworks can achieve up to 94% accuracy in intrusion detection while preserving data locality and privacy constraints. The findings demonstrate significant potential for scalable, privacy-aware cybersecurity solutions in modern IoT ecosystems.