Poisoning attack in federated learning using generative adversarial nets J Zhang, J Chen, D Wu, B Chen, S Yu TrustCom 2019, 2019 | 229 | 2019 |
Recent advances in video-based human action recognition using deep learning: A review D Wu, N Sharma, M Blumenstein IJCNN 2017, 2017 | 186 | 2017 |
Multi-task network anomaly detection using federated learning Y Zhao, J Chen, D Wu, J Teng, S Yu SOICT 2019, 2019 | 185 | 2019 |
A survey on latest botnet attack and defense L Zhang, S Yu, D Wu, P Watters TrustCom 2011, 2011 | 122 | 2011 |
From distributed machine learning to federated learning: In the view of data privacy and security S Shen, T Zhu, D Wu, W Wang, W Zhou Concurrency and Computation: Practice and Experience 34 (16), e6002, 2022 | 102 | 2022 |
PDGAN: A novel poisoning defense method in federated learning using generative adversarial network Y Zhao, J Chen, J Zhang, D Wu, J Teng, S Yu ICA3PP 2019, 2020 | 92 | 2020 |
Robust feature-based automated multi-view human action recognition system KP Chou, M Prasad, D Wu, N Sharma, DL Li, YF Lin, M Blumenstein, ... IEEE Access 6, 15283-15296, 2018 | 77 | 2018 |
Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks Y Zhao, J Chen, J Zhang, D Wu, M Blumenstein, S Yu Concurrency and Computation: Practice and Experience 34 (7), e5906, 2022 | 58 | 2022 |
Fooling intrusion detection systems using adversarially autoencoder J Chen, D Wu, Y Zhao, N Sharma, M Blumenstein, S Yu Digital Communications and Networks 7 (3), 453-460, 2021 | 38 | 2021 |
VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems J Zhang, Y Liu, D Wu, S Lou, B Chen, S Yu Digital Communications and Networks 9 (4), 981-989, 2023 | 33 | 2023 |
Defending poisoning attacks in federated learning via adversarial training method J Zhang, D Wu, C Liu, B Chen FCS 2020, 2020 | 25 | 2020 |
Network anomaly detection using federated learning and transfer learning Y Zhao, J Chen, Q Guo, J Teng, D Wu SPDE 2020, 2020 | 24 | 2020 |
Adversarial action data augmentation for similar gesture action recognition D Wu, J Chen, N Sharma, S Pan, G Long, M Blumenstein IJCNN 2019, 2019 | 21 | 2019 |
On addressing the imbalance problem: a correlated KNN approach for network traffic classification D Wu, X Chen, C Chen, J Zhang, Y Xiang, W Zhou NSS 2014, 2014 | 19 | 2014 |
Privacy inference attack and defense in centralized and federated learning: A comprehensive survey B Rao, J Zhang, D Wu, C Zhu, X Sun, B Chen IEEE Transactions on Artificial Intelligence, 2024 | 14 | 2024 |
A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning D Wu, N Wang, J Zhang, Y Zhang, Y Xiang, L Gao IJCNN 2022, 2022 | 13 | 2022 |
Defending against membership inference attacks in federated learning via adversarial example Y Xie, B Chen, J Zhang, D Wu MSN 2021, 2021 | 12 | 2021 |
A systematic literature review on explainability for machine/deep learning-based software engineering research S Cao, X Sun, R Widyasari, D Lo, X Wu, L Bo, J Zhang, B Li, W Liu, D Wu, ... arXiv preprint arXiv:2401.14617, 2024 | 7 | 2024 |
A Comprehensive Survey on Machine Learning Driven Material Defect Detection: Challenges, Solutions, and Future Prospects J Bai, D Wu, T Shelley, P Schubel, D Twine, J Russell, X Zeng, J Zhang arXiv preprint arXiv:2406.07880, 2024 | 6 | 2024 |
FedInverse: Evaluating Privacy Leakage in Federated Learning D Wu, J Bai, Y Song, J Chen, W Zhou, Y Xiang, A Sajjanhar ICLR 2024, 2023 | 6 | 2023 |