Q-AFL: A Quantum-Inspired Adaptive Federated Learning Framework for Wireless Network Optimization
Year: 2025
Type: article
Abstract: 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.
Author: K. Ramesh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410868934