Federated Learning Approaches for Privacy-Preserving Security Analytics in Distributed IoT Environments

The proliferation of Internet of Things (IoT) devices across diverse and distributed environments has amplified the need for robust, scalable, and privacy-aware security analytics. Traditional centralized machine learning models struggle to address these concerns due to limitations in data sharing, latency, and privacy compliance. This paper explores federated learning (FL) as a transformative approach for enabling collaborative security analytics while preserving data privacy at the edge. We investigate how FL architectures can effectively detect threats, anomalies, and intrusions across heterogeneous IoT networks without transferring raw data to a central server. The study presents a comparative evaluation of state-of-the-art FL methods tailored for security use cases, highlights strategies for handling non-IID data and device heterogeneity, and discusses the implications of communication overhead and adversarial risks in real-world deployments. Our findings demonstrate that FL not only reduces privacy risks but also enhances model robustness and adaptability in dynamic, resource-constrained IoT ecosystems. This research contributes a comprehensive perspective on how federated learning can evolve into a cornerstone of next-generation, privacy-preserving cybersecurity frameworks for the Internet of Things.