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

作者：Danish Javeed, Muhammad Shahid Saeed, Ijaz Ahmad, Muhammad Adil, Prabhat Kumar, A.K.M. Najmul Islam · 发表于：Future Generation Computer Systems · 年份：2024 · DOI：10.1016/j.future.2024.06.023 · 被引用次数：96 · 研究领域：Privacy-Preserving Technologies in Data、Internet Traffic Analysis and Secure E-voting、Cooperative Communication and Network Coding

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. To enable several secure, intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. 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 syst...