Han Yu
Cited by
Cited by
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
Foundations and Trends in Machine Learning (FnTML) 14 (1–2), 1–210, 2021
Towards personalized federated learning
AZ Tan, H Yu, L Cui, Q Yang
IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 34 (12 …, 2023
A survey of zero-shot learning: Settings, methods and applications
W Wang, VW Zheng, H Yu, C Miao
ACM Transactions on Intelligent Systems and Technology (TIST) 10 (2), 13:1–13:19, 2019
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
J Wang, W Feng, Y Chen, H Yu, M Huang, PS Yu
The 26th ACM International Conference on Multimedia (ACM MM'18), 402–410, 2018
Threats to federated learning
L Lyu, H Yu, J Zhao, Q Yang
Federated Learning: Privacy and Incentive, 3-16, 2020
A survey of trust and reputation management systems in wireless communications
H Yu, Z Shen, C Miao, C Leung, D Niyato
Proceedings of the IEEE 98 (10), 1755–1772, 2010
Privacy and robustness in federated learning: Attacks and defenses
L Lyu, H Yu, X Ma, C Chen, L Sun, J Zhao, Q Yang, PS Yu
IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 35 (7 …, 2024
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Y Liu, A Huang, Y Luo, H Huang, Y Liu, Y Chen, L Feng, T Chen, H Yu, ...
The 32nd Annual Conference on Innovative Applications of AI (IAAI-20), 13172 …, 2020
Building Ethics into Artificial Intelligence
H Yu, Z Shen, C Miao, C Leung, VR Lesser, Q Yang
The 27th International Joint Conference on Artificial Intelligence (IJCAI'18 …, 2018
A survey of multi-agent trust management systems
H Yu, Z Shen, C Leung, C Miao, VR Lesser
IEEE Access 1 (1), 35–50, 2013
Incentive design for efficient federated learning in mobile networks: A contract theory approach
J Kang, Z Xiong, D Niyato, H Yu, YC Liang, DI Kim
2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS), 1–5, 2019
Transfer learning with dynamic distribution adaptation
J Wang, Y Chen, W Feng, H Yu, M Huang, Q Yang
ACM Transactions on Intelligent Systems and Technology (TIST) 11 (1), 6:1–6:25, 2020
Towards fair and privacy-preserving federated deep models
L Lyu, J Yu, K Nandakumar, Y Li, X Ma, J Jin, H Yu, KS Ng
IEEE Transactions on Parallel and Distributed Systems (TPDS) 31 (11), 2524–2541, 2020
A Fairness-aware Incentive Scheme for Federated Learning
H Yu, Z Liu, Y Liu, T Chen, M Cong, X Weng, D Niyato, Q Yang
The 3rd AAAI/ACM Conference on AI, Ethics, and Society (AIES-20), 393–399, 2020
Deep Model for Dropout Prediction in MOOCs
W Wang, H Yu, C Miao
The 2nd International Conference on Crowd Science and Engineering (ICCSE'17 …, 2017
Collaborative fairness in federated learning
L Lyu, X Xu, Q Wang, H Yu
Federated Learning: Privacy and Incentive, 189-204, 2020
A study on factors affecting service quality and loyalty intention in mobile banking
Q Zhou, FJ Lim, H Yu, G Xu, X Ren, D Liu, X Wang, X Mai, H Xu
Journal of Retailing and Consumer Services (JRCS) 60 (102424), doi:10.1016/j …, 2021
Easy Transfer Learning by Exploiting Intra-domain Structures
J Wang, Y Chen, H Yu, M Huang, Q Yang
The 2019 IEEE International Conference on Multimedia and Expo (ICME'19 …, 2019
Federated Learning
Q Yang, Y Liu, Y Cheng, Y Kang, T Chen, H Yu
Springer, Cham, 2020
Towards a trust aware cognitive radio architecture
T Qin, H Yu, C Leung, Z Shen, C Miao
ACM SIGMOBILE Mobile Computing and Communications Review 13 (2), 86–95, 2009
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