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 | 963 | 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 | 430 | 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 | 229 | 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 | 215 | 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 | 146 | 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 | 42 | 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 | 37 | 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 | 24 | 2020 |
Toward low-cost and stable blockchain networks M Fang, J Liu ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-6, 2020 | 15 | 2020 |
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 | 13 | 2022 |
AFLGuard: Byzantine-robust Asynchronous Federated Learning M Fang, J Liu, NZ Gong, ES Bentley Annual Computer Security Applications Conference (ACSAC), 2022 | 12 | 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 | 9 | 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 | 9 | 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 | 5 | 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 | 2 | 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 | 2 | 2014 |
Poisoning Federated Recommender Systems with Fake Users M Yin, Y Xu, M Fang, NZ Gong Proceedings of The Web Conference 2024, 2024 | 1 | 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 | 1 | 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 | 1 | 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 | | 2024 |