| 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 | 1382 | 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 | 715 | 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 | 309 | 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 | 249 | 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 | 177 | 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 | 55 | 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 | 47 | 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 | 33 | 2020 |
| AFLGuard: Byzantine-robust Asynchronous Federated Learning M Fang, J Liu, NZ Gong, ES Bentley Annual Computer Security Applications Conference (ACSAC), 2022 | 32 | 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 | 21 | 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 | 16 | 2022 |
| Toward low-cost and stable blockchain networks M Fang, J Liu ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-6, 2020 | 16 | 2020 |
| Poisoning Federated Recommender Systems with Fake Users M Yin, Y Xu, M Fang, NZ Gong Proceedings of The Web Conference 2024, 2024 | 12 | 2024 |
| Byzantine-Robust Decentralized Federated Learning M Fang, Z Zhang, Hairi, P Khanduri, J Liu, S Lu, Y Liu, N Gong ACM Conference on Computer and Communications Security (CCS), 2024 | 9 | 2024 |
| GradSafe: Detecting Unsafe Prompts for LLMs via Safety-Critical Gradient Analysis Y Xie, M Fang, R Pi, N Gong Annual Meeting of the Association for Computational Linguistics (ACL), 2024 | 9* | 2024 |
| 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 |
| Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction Z Zhang, M Fang, J Huang, Y Liu Proceedings of IFIP/IEEE Networking 2024, 2024 | 7 | 2024 |
| Poisonedfl: Model poisoning attacks to federated learning via multi-round consistency Y Xie, M Fang, NZ Gong arXiv preprint arXiv:2404.15611, 2024 | 7 | 2024 |
| 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 | 5 | 2024 |
| FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error XIE Yueqi, M Fang, NZ Gong Forty-first International Conference on Machine Learning, 2024 | 5 | 2024 |
Neil Zhenqiang GongAssociate Professor, Duke UniversityEmail yang diverifikasi di duke.edu