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.

中文翻译：

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用于物联网安全的量子联合学习和 6G 无线网络：概念、挑战和未来方向


物联网 (IoT) 通过实现无缝设备交互，给各个行业带来了革命性的变化。然而，物联网设备的激增也引发了重大的安全和隐私问题。由于物联网网络的独特特征，例如异构性、可扩展性和资源限制，传统的安全措施往往无法解决这些问题。本综述采用主题探索的方法进行综合分析，研究量子计算、联邦学习和6G无线网络的融合。这种新颖的交叉点旨在显着提高物联网生态系统内的安全性和隐私性。量子计算可以增强加密算法，使物联网数据对于智能物联网应用更加安全。联邦学习是一种去中心化的机器学习方法，允许物联网设备学习共享模型，同时将所有训练数据保留在原始设备上，从而增强隐私性。当与 6G 网络的高速、低延迟功能集成时，这种协同作用变得更加重要，它可以促进许多物联网设备之间的实时、安全的数据处理和通信。其次，我们讨论最新进展，提供先进解决方案、可用数据集和关键性能指标的最新概述，并总结保护物联网系统安全的重要见解、挑战和趋势。第三，我们设计了一个将量子计算集成到联邦学习中的概念框架，适用于 6G 网络。最后，我们重点介绍了量子技术和6G网络的未来进展，并总结了其对物联网安全的影响，为物联网安全领域的研究人员和从业者铺平了道路。