| A secure federated transfer learning framework Y Liu, Y Kang, C Xing, T Chen, Q Yang IEEE Intelligent Systems 35 (4), 70-82, 2020 | 266 | 2020 |
| Federated learning Q Yang, Y Liu, Y Cheng, Y Kang, T Chen, H Yu Synthesis Lectures on Artificial Intelligence and Machine Learning 13 (3), 1-207, 2019 | 265 | 2019 |
| Fedml: A research library and benchmark for federated machine learning C He, S Li, J So, X Zeng, M Zhang, H Wang, X Wang, P Vepakomma, ... arXiv preprint arXiv:2007.13518, 2020 | 129 | 2020 |
| A communication efficient collaborative learning framework for distributed features Y Liu, Y Kang, X Zhang, L Li, Y Cheng, T Chen, M Hong, Q Yang arXiv preprint arXiv:1912.11187, 2019 | 86* | 2019 |
| Secure and efficient federated transfer learning S Sharma, C Xing, Y Liu, Y Kang 2019 IEEE International Conference on Big Data (Big Data), 2569-2576, 2019 | 40 | 2019 |
| FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training Y Kang, Y Liu, X Liang ACM Transactions on Intelligent Systems and Technology (TIST) 13 (4), 1-16, 2022 | 22* | 2022 |
| Extensible Dynamic Form approach for supplier discovery Y Kang, J Kim, Y Peng 2011 IEEE International Conference on Information Reuse & Integration, 83-87, 2011 | 6* | 2011 |
| Federated deep learning with bayesian privacy H Gu, L Fan, B Li, Y Kang, Y Yao, Q Yang arXiv preprint arXiv:2109.13012, 2021 | 5 | 2021 |
| Defending Batch-Level Label Inference and Replacement Attacks in Vertical Federated Learning T Zou, Y Liu, Y Kang, W Liu, Y He, Z Yi, Q Yang, YQ Zhang IEEE Transactions on Big Data, 2022 | 4* | 2022 |
| Privacy-preserving Federated Adversarial Domain Adaptation over Feature Groups for Interpretability Y Kang, Y He, J Luo, T Fan, Y Liu, Q Yang IEEE Transactions on Big Data, 2022 | 3 | 2022 |
| FedCG: Leverage conditional gan for protecting privacy and maintaining competitive performance in federated learning Y Wu, Y Kang, J Luo, Y He, Q Yang Proceedings of the Thirty-First International Joint Conference on Artificial …, 2022 | 2 | 2022 |
| SecureBoost+: A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning W Chen, G Ma, T Fan, Y Kang, Q Xu, Q Yang arXiv preprint arXiv:2110.10927, 2021 | 1 | 2021 |
| Ontology-Based Dynamic Forms for Manufacturing Capability Information Collection Y Peng, Y Kang IFIP International Conference on Advances in Production Management Systems …, 2013 | 1 | 2013 |
| FedIPR: Ownership Verification for Federated Deep Neural Network Models B Li, L Fan, H Gu, J Li, Q Yang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | | 2022 |
| Bridging the Gap between Data Integration and ML Systems R Hai, Y Kang, C Koutras, A Ionescu, A Katsifodimos arXiv preprint arXiv:2205.09681, 2022 | | 2022 |
| A semantic resolution framework for integrating manufacturing service capability data Y Kang University of Maryland, Baltimore County, 2015 | | 2015 |
| Research into Bank Loan Risk Based on UDM and Self-adaptive RBF Neural Network K Yan 2007 Second International Conference on Bio-Inspired Computing: Theories and …, 2007 | | 2007 |
| 群体智能中的联邦学习算法综述 杨强， 童咏昕， 王晏晟， 范力欣， 王薇， 陈雷， 王魏， 康焱 智能科学与技术学报 4 (1), 29-44, 0 | | |
| iPlant Semantic Web Platform uses SSWAP (Simple Semantic Web Architecture and Protocol) to enable Semantic Pipelines across Distributed Web and High Performance Computing Resources DDG Gessler, B Bulka, E Sirin, Y Kang, P Klinov, H Vasquez-Gross, J Yu, ... | | |
