Federated Learning Enabled Cybersecurity Architecture for Scalable Internet of Things and Cloud Computing Platforms

Abstract

The rapid proliferation of Internet of Things (IoT) devices and cloud computing platforms has expanded attack surfaces, creating significant cybersecurity challenges. Traditional centralized cybersecurity solutions struggle to process massive distributed data while preserving privacy, leading to potential vulnerabilities and compliance issues. This research proposes a Federated Learning (FL) enabled cybersecurity architecture designed to provide scalable, privacy-preserving, and adaptive protection for IoT and cloud environments. The framework enables collaborative model training across distributed edge devices and cloud nodes without sharing raw data, ensuring data privacy and regulatory compliance. Core components include federated aggregation, anomaly detection using machine learning models, intrusion detection, secure communication protocols, and adaptive threat mitigation. Experimental evaluations on simulated IoT and cloud datasets demonstrate that the framework achieves high detection accuracy, reduces communication overhead, and preserves data confidentiality compared to traditional centralized approaches. The FL-enabled architecture supports dynamic scalability, real-time threat detection, and collaborative intelligence across heterogeneous platforms. This research highlights the potential of federated learning in enhancing cybersecurity for distributed systems, providing a practical blueprint for secure, resilient, and scalable IoT-cloud infrastructures.