Q-DeepCuckoo: A Quantum-Assisted Federated Framework for Dynamic CH Optimization in 6G-IoT Networks

The DeepCuckoo framework is a pioneering approach for energy-efficient cluster head (CH) selection within 5G/WSNs, employing a hybrid cuckoo search-deep learning optimization strategy. Nevertheless, its centralized architecture faces substantial challenges in terms of scalability and real-time adaptability in emerging 6G-IoT networks. These networks require fundamentally novel approaches owing to the requirements for ultralow latency, privacy preservation, and terahertz channel dynamics. We introduce Quantum-Enhanced DeepCuckoo (Q-DeepCuckoo), an innovative framework that integrates quantum-accelerated cuckoo search, utilizing quantum walks to escape local optima 35% more rapidly than traditional methods; hierarchical federated learning, which enables decentralized, privacy-preserving model training across edge devices; and 6G-native optimization, incorporating THz channel models and AI-driven air interface adaptation. Rigorous simulations conducted on 3GPP 6G-NR datasets demonstrate a 28% increase in energy efficiency compared to the original DeepCuckoo in ultra-dense deployments (10⁶ nodes/km²), a 90% reduction in federated learning communication overhead through quantum-assisted gradient compression, sub-millisecond decision latency (0.75ms) that complies with 3GPP URLLC standards in high mobility scenarios, and a 40% enhancement in packet delivery rate during THz channel blockages. By integrating quantum optimization with federated edge intelligence, Q-DeepCuckoo establishes a new paradigm for scalable and future-proof CH selection in 6G-IoT ecosystems.