Q-AFL: A Quantum-Inspired Adaptive Federated Learning Framework for Wireless Network Optimization

Wireless networks, especially with the evolution toward 6G and beyond, face unprecedented demands for efficiency, security, and adaptability in handling massive data exchanges across diverse, distributed, and resource-constrained devices. Conventional centralized learning paradigms present significant limitations due to high communication overhead, privacy concerns, and suboptimal adaptability to dynamic network environments. To overcome these challenges, this research proposes a novel Quantum-inspired Adaptive Federated Learning (Q-AFL) framework designed specifically to optimize wireless network performance. By integrating quantum-inspired optimization methods with adaptive federated learning algorithms, Q-AFL dynamically adjusts model aggregation intervals, learning rates, and resource allocation to enhance network responsiveness and efficiency. Quantum-inspired optimization techniques, including Quantum-inspired Particle Swarm Optimization (QiPSO) and Quantum-inspired Genetic Algorithms (QiGA), are leveraged to precisely optimize critical parameters within the federated learning process. This quantum-inspired approach significantly reduces communication overhead, improves computational efficiency, and enhances privacy preservation by minimizing unnecessary data transmissions among nodes. Extensive simulation studies using NS-3 validate the effectiveness of the proposed Q-AFL framework, demonstrating substantial improvements in throughput, latency, energy efficiency, and scalability compared to traditional federated learning and centralized machine learning solutions. The outcomes highlight Q-AFL's potential as a transformative approach for the next generation of high-performance wireless networks.