Journal Article10.1016/j.future.2024.06.023
Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions
Danish Javeed,Muhammad Shahid Saeed,Ijaz Ahmad,Muhammad Adil,Prabhat Kumar,A.K.M. Najmul Islam +5 more
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TL;DR: Quantum-empowered federated learning and 6G wireless networks for IoT security converge to enhance security and privacy within the IoT ecosystem.
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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. 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 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.
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Citations
Security of Federated Learning in 6G Era: A Review on Conceptual Techniques and Software Platforms used for Research and Analysis
Syed Hussain Ali Kazmi,Faizan Qamar,Rosilah Hassan,Kashif Nisar,Mohammed Azmi Al-Betar +4 more
TL;DR: This study reviews conceptual techniques and software platforms for securing Federated Learning in 6G networks, highlighting requirements, challenges, and open research issues in ensuring data privacy, security, and scalability in distributed and heterogeneous systems.
10
An Applied Analysis of Securing 5G/6G Core Networks with Post-Quantum Key Encapsulation Methods
Paul Scalise,Ramón Serramito García,Matthew Boeding,Michael Hempel,Hamid Sharif +4 more
TL;DR: This study integrates post-quantum key encapsulation methods into 5G/6G core networks, demonstrating negligible latency and bandwidth impact, and significantly enhancing cybersecurity, with implications for secure VNF communications and zero-trust approaches in 5G networks.
3
Securing data and preserving privacy in cloud IoT-based technologies an analysis of assessing threats and developing effective safeguard
Mayank Pathak,Kamta Nath Mishra,Satya P. Singh +2 more
TL;DR: This study analyzes security threats in cloud IoT-based technologies, identifying existing method limitations and proposing advancements in protocols, leveraging blockchain, machine learning, fog, and edge computing to ensure secure end-to-end data transmission and mitigate data privacy risks.
2
Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network
Javaid A Malik,Sagheer Abbas,Muhammad Saleem,Rahat Qudsi +3 more
TL;DR: Next-generation intrusion detection in smart vehicle networks leveraging federated learning and blockchain technologies improves accuracy and privacy. The framework utilizes SVM model for optimal performance.
2
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