Minghong Fang
Minghong Fang
Zweryfikowany adres z duke.edu - Strona główna
Cytowane przez
Cytowane przez
Local model poisoning attacks to {Byzantine-Robust} federated learning
M Fang, X Cao, J Jia, N Gong
29th USENIX security symposium (USENIX Security 20), 1605-1622, 2020
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
X Cao, M Fang, J Liu, NZ Gong
ISOC Network and Distributed System Security Symposium (NDSS), 2021
Achieving linear speedup with partial worker participation in non-iid federated learning
H Yang, M Fang, J Liu
International Conference on Learning Representations (ICLR), 2021
Poisoning attacks to graph-based recommender systems
M Fang, G Yang, NZ Gong, J Liu
Proceedings of the 34th annual computer security applications conference …, 2018
Influence function based data poisoning attacks to top-n recommender systems
M Fang, NZ Gong, J Liu
Proceedings of The Web Conference 2020, 3019-3025, 2020
Byzantine-resilient stochastic gradient descent for distributed learning: A lipschitz-inspired coordinate-wise median approach
H Yang, X Zhang, M Fang, J Liu
2019 IEEE 58th Conference on Decision and Control (CDC), 5832-5837, 2019
Data poisoning attacks and defenses to crowdsourcing systems
M Fang, M Sun, Q Li, NZ Gong, J Tian, J Liu
Proceedings of the web conference 2021, 969-980, 2021
Private and communication-efficient edge learning: a sparse differential gaussian-masking distributed SGD approach
X Zhang, M Fang, J Liu, Z Zhu
Proceedings of the Twenty-First International Symposium on Theory …, 2020
Toward low-cost and stable blockchain networks
M Fang, J Liu
ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-6, 2020
AFLGuard: Byzantine-robust Asynchronous Federated Learning
M Fang, J Liu, NZ Gong, ES Bentley
Annual Computer Security Applications Conference (ACSAC), 2022
Machine learning-based modeling approaches for estimating pyrolysis products of varied biomass and operating conditions
J Shen, M Yan, M Fang, X Gao
Bioresource Technology Reports, 2022
Net-fleet: Achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data
X Zhang, M Fang, Z Liu, H Yang, J Liu, Z Zhu
Proceedings of the Twenty-Third International Symposium on Theory …, 2022
Prioritizing disease-causing genes based on network diffusion and rank concordance
M Fang, X Hu, T He, Y Wang, J Zhao, X Shen, J Yuan
2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2014
Fairroad: Achieving fairness for recommender systems with optimized antidote data
M Fang, J Liu, M Momma, Y Sun
Proceedings of the 27th ACM on Symposium on Access Control Models and …, 2022
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Y Xu, M Yin, M Fang, NZ Gong
Proceedings of The Web Conference 2024, 2024
A novel disease gene prediction method based on PPI network
J Zhao, T He, X Hu, Y Wang, X Shen, M Fang, J Yuan
2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2014
Poisoning Federated Recommender Systems with Fake Users
M Yin, Y Xu, M Fang, NZ Gong
Proceedings of The Web Conference 2024, 2024
IPCert: Provably Robust Intellectual Property Protection for Machine Learning
Z Jiang, M Fang, NZ Gong
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
Adaptive multi-hierarchical signSGD for communication-efficient distributed optimization
H Yang, X Zhang, M Fang, J Liu
2020 IEEE 21st International Workshop on Signal Processing Advances in …, 2020
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
H Yang, P Qiu, P Khanduri, M Fang, J Liu
arXiv preprint arXiv:2405.02745, 2024
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