Yu, Han

Privacy and Robustness in Federated Learning: Attacks and Defenses

Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 548

A Survey of Zero-Shot Learning: Settings, Methods, and Applications

Wang, W., Zheng, V. W., Yu, H., & Miao, C. (2019) · ACM Transactions on Intelligent Systems and Technology

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 528

Threats to Federated Learning

Lyu, L., Yu, H., Zhao, J., & Yang, Q. (2020) · Lecture Notes in Computer Science

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 444

FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

Liu, Y., Huang, A., Luo, Y., Huang, H., Liu, Y., Chen, Y., Feng, L., Chen, T., Yu, H., & Yang, Q. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Privacy-Preserving Technologies in DataAdvanced Neural Network Applications

Cited by 310

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

Towards Out-Of-Distribution Generalization: A Survey

Liu, J., Shen, Z., He, Y., Zhang, X., Xu, R., Yu, H., & Cui, P. (2021) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 253

Towards Fair and Privacy-Preserving Federated Deep Models

Lyu, L., Yu, J., Nandakumar, K., Li, Y., Ma, X., Jin, J., Yu, H., & Ng, K. S. (2020) · IEEE Transactions on Parallel and Distributed Systems

Privacy-Preserving Technologies in DataBlockchain Technology Applications and Security

Cited by 250

Threats to federated learning A survey

Lyu, L., Yu, H., & Yang, Q. (2020) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 221

Building Ethics into Artificial Intelligence

Yu, H., Shen, Z., Miao, C., Leung, C., Lesser, V. R., & Yang, Q. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

Ethics and Social Impacts of AIEthics in Business and Education

Cited by 210

Towards AI-Powered Personalization in MOOC Learning

Yu, H., Miao, C., Leung, C., & White, T. J. (2017) · npj Science of Learning

Online Learning and AnalyticsArtificial Intelligence in Education

Cited by 131

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

Multi-Participant Multi-Class Vertical Federated Learning

Feng, S., & Yu, H. (2020) · arXiv

Privacy-Preserving Technologies in DataMachine Learning and Data Classification

Cited by 66

Practical Attribute Reconstruction Attack Against Federated Learning

Chen, C., Lyu, L., Yu, H., & Chen, G. (2024) · IEEE Transactions on Big Data

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 30

Automatic Product Copywriting for E-commerce

Zhang, X., Zou, Y., Zhang, H., Zhou, J., Diao, S., Chen, J., Ding, Z., He, Z., He, X., Xiao, Y., et al. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingNatural Language Processing Techniques

Cited by 24