Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions

Abstract

The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. Quantum computing can enhance encryption algorithms to make IoT data more secure for intelligent IoT applications. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.