Hybrid Quantum-Classical Frameworks for IoT Security: Bridging AI, Federated Learning, and Cybersecurity

The exponential growth of Internet of Things (IoT) ecosystems has introduced unprecedented security challenges, necessitating innovative solutions that transcend classical cybersecurity paradigms. This article proposes a hybrid quantum-classical framework tailored for IoT security, synergizing quantum computing, AI-driven threat detection, and federated learning to address evolving cyber threats. By integrating quantum key distribution (QKD) for ultra-secure communication, decentralized, federated learning for privacy-preserving threat analysis, and quantum enhanced AI models for real-time intrusion detection, the framework establishes a multi-layered defense mechanism against sophisticated attacks. A critical literature review identifies gaps in scalability, interoperability, and practical deployment of quantum-classical systems in IoT environments. The proposed framework demonstrates a 23% improvement in threat detection accuracy and a 40% reduction in communication overhead compared to classical federated learning models validated through simulations on heterogeneous IoT datasets. Ethical considerations, including algorithmic bias and quantum technology accessibility, are rigorously analyzed to ensure responsible deployment. This study advances the theoretical foundations of hybrid quantum classical systems. It provides actionable insights for policymakers and practitioners, bridging the gap between theoretical innovation and real-world IoT security applications.