Niyato, Dusit

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 2,565

A survey of mobile cloud computing: architecture, applications, and approaches

Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013) · Wireless Communications and Mobile Computing

IoT and Edge/Fog ComputingCloud Computing and Resource Management

Cited by 2,391

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 1,498

Ambient Backscatter Communications: A Contemporary Survey

Van Huynh, N., Hoang, D. T., Lu, X., Niyato, D., Wang, P., & Kim, D. I. (2018) · IEEE Communications Surveys & Tutorials

Energy Harvesting in Wireless NetworksWireless Communication Security Techniques

Cited by 932

Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S. (2020) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataTraffic Prediction and Management Techniques

Cited by 712

Federated learning meets blockchain in edge computing: Opportunities and challenges

Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 661

Reliable Federated Learning for Mobile Networks

Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M. (2020) · IEEE Wireless Communications

Privacy-Preserving Technologies in DataBlockchain Technology Applications and Security

Cited by 617

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., & Liu, Y. (2021) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataBlockchain Technology Applications and Security

Cited by 613

Toward secure blockchain-enabled internet of vehicles: optimizing consensus management using reputation and contract theory

Kang, J., Xiong, Z., Niyato, D., Ye, D., Kim, D. I., & Zhao, J. (2019) · IEEE Transactions on Vehicular Technology

Blockchain Technology Applications and SecurityVehicular Ad Hoc Networks (VANETs)

Cited by 604

Federated learning for 6G communications: Challenges, methods, and future directions

Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., & Niyato, D. (2020) · China Communications

Privacy-Preserving Technologies in DataAdvanced Wireless Communication Technologies

Cited by 464

Local Differential Privacy-Based Federated Learning for Internet of Things

Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., & Lam, K.-Y. (2021) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataInternet Traffic Analysis and Secure E-voting

Cited by 456

6G Visions: Mobile ultra-broadband, super internet-of-things, and artificial intelligence

Zhang, L., Liang, Y.-C., & Niyato, D. (2019) · China Communications

Advanced Wireless Communication TechnologiesMillimeter-Wave Propagation and Modeling

Cited by 354

A Secure Federated Learning Framework for 5G Networks

Liu, Y., Peng, J., Kang, J., Iliyasu, A. M., Niyato, D., & El-Latif, A. A. A. (2020) · IEEE Wireless Communications

Privacy-Preserving Technologies in DataBlockchain Technology Applications and Security

Cited by 290

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

Kang, J., Xiong, Z., Niyato, D., Yu, H., Liang, Y.-C., & Kim, D. I. (2019) · 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

Privacy-Preserving Technologies in DataAdvanced MIMO Systems Optimization

Cited by 255

Optimal Auction for Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach

Luong, N. C., Xiong, Z., Wang, P., & Niyato, D. (2018) · 2018 IEEE International Conference on Communications (ICC)

Auction Theory and ApplicationsBlockchain Technology Applications and Security

Cited by 229

RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications

Shi, E., Zhang, J., Du, H., Ai, B., Yuen, C., Niyato, D., Letaief, K. B., & Shen, X. (2024) · Proceedings of the IEEE

Advanced Wireless Communication TechnologiesMillimeter-Wave Propagation and Modeling

Cited by 224

Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning

Ilahi, I., Usama, M., Qadir, J., Janjua, M. U., Al-Fuqaha, A., Hoang, D. T., & Niyato, D. (2022) · IEEE Transactions on Artificial Intelligence

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 170

Federated Learning for Industrial Internet of Things in Future Industries

Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Wireless Communications

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 166

Interactive AI With Retrieval-Augmented Generation for Next Generation Networking

Zhang, R., Du, H., Liu, Y., Niyato, D., Kang, J., Sun, S., Shen, X., & Poor, H. V. (2024) · IEEE Network

Software-Defined Networks and 5GAdvanced Data and IoT Technologies

Cited by 147

Efficient Federated Learning With Spike Neural Networks for Traffic Sign Recognition

Xie, K., Zhang, Z., Li, B., Kang, J., Niyato, D., Xie, S., & Wu, Y. (2022) · IEEE Transactions on Vehicular Technology

Privacy-Preserving Technologies in DataAdvanced Neural Network Applications

Cited by 128

Lead federated neuromorphic learning for wireless edge artificial intelligence

Yang, H., Lam, K.-Y., Xiao, L., Xiong, Z., Hu, H., Niyato, D., & Vincent Poor, H. (2022) · Nature Communications

Privacy-Preserving Technologies in DataAdvanced Memory and Neural Computing

Cited by 127

Deep Activity Recognition Models with Triaxial Accelerometers

Alsheikh, M. A., Selim, A., Niyato, D., Doyle, L., Lin, S., & Tan, H.-P. (2015) · arXiv

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 126

A sustainable incentive scheme for federated learning

Yu, H., Liu, Z., Liu, Y., Chen, T., Cong, M., Weng, X., Niyato, D., & Yang, Q. (2020) · IEEE Intelligent Systems

Privacy-Preserving Technologies in DataBlockchain Technology Applications and Security

Cited by 123

Optimizing Task Assignment for Reliable Blockchain-Empowered Federated Edge Learning

Kang, J., Xiong, Z., Li, X., Zhang, Y., Niyato, D., Leung, C., & Miao, C. (2021) · IEEE Transactions on Vehicular Technology

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 81