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Di Wu
Di Wu
Senior Lecturer/Associate Professor in North America, University of Southern Queensland
Verified email at unisq.edu.au - Homepage
Title
Cited by
Cited by
Year
Poisoning attack in federated learning using generative adversarial nets
J Zhang, J Chen, D Wu, B Chen, S Yu
TrustCom 2019, 2019
2292019
Recent advances in video-based human action recognition using deep learning: A review
D Wu, N Sharma, M Blumenstein
IJCNN 2017, 2017
1862017
Multi-task network anomaly detection using federated learning
Y Zhao, J Chen, D Wu, J Teng, S Yu
SOICT 2019, 2019
1852019
A survey on latest botnet attack and defense
L Zhang, S Yu, D Wu, P Watters
TrustCom 2011, 2011
1222011
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
1022022
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
922020
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
772018
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
582022
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
382021
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
332023
Defending poisoning attacks in federated learning via adversarial training method
J Zhang, D Wu, C Liu, B Chen
FCS 2020, 2020
252020
Network anomaly detection using federated learning and transfer learning
Y Zhao, J Chen, Q Guo, J Teng, D Wu
SPDE 2020, 2020
242020
Adversarial action data augmentation for similar gesture action recognition
D Wu, J Chen, N Sharma, S Pan, G Long, M Blumenstein
IJCNN 2019, 2019
212019
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
192014
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
142024
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
132022
Defending against membership inference attacks in federated learning via adversarial example
Y Xie, B Chen, J Zhang, D Wu
MSN 2021, 2021
122021
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
72024
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
62024
FedInverse: Evaluating Privacy Leakage in Federated Learning
D Wu, J Bai, Y Song, J Chen, W Zhou, Y Xiang, A Sajjanhar
ICLR 2024, 2023
62023
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