| GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis Y Xie, M Fang, R Pi, N Gong Proceedings of the 62nd Annual Meeting of the Association for Computational …, 2024 | | 2024 |
| Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks Z Zhang, M Fang, M Chen, G Li, X Lin, Y Liu IEEE Internet of Things Journal, 2024 | | 2024 |
| Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning Y Jia, M Fang, H Liu, J Zhang, NZ Gong arXiv preprint arXiv:2407.07221, 2024 | | 2024 |
| Byzantine-Robust Decentralized Federated Learning M Fang, Z Zhang, Hairi, P Khanduri, J Liu, S Lu, Y Liu, N Gong arXiv preprint arXiv:2406.10416, 2024 | 1 | 2024 |
| 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 |
| PoisonedFL: Model Poisoning Attacks to Federated Learning via Multi-Round Consistency Y Xie, M Fang, NZ Gong arXiv preprint arXiv:2404.15611, 2024 | 2 | 2024 |
| Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction Z Zhang, M Fang, J Huang, Y Liu Proceedings of IFIP/IEEE Networking 2024, 2024 | 2 | 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 | 3 | 2024 |
| GradSafe: Detecting Unsafe Prompts for LLMs via Safety-Critical Gradient Analysis Y Xie, M Fang, R Pi, N Gong arXiv preprint arXiv:2402.13494, 2024 | 3 | 2024 |
| Poisoning Federated Recommender Systems with Fake Users M Yin, Y Xu, M Fang, NZ Gong Proceedings of The Web Conference 2024, 2024 | 3 | 2024 |
| Competitive Advantage Attacks to Decentralized Federated Learning Y Jia, M Fang, NZ Gong arXiv preprint arXiv:2310.13862, 2023 | | 2023 |
| 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 | 2 | 2023 |
| 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 | 10 | 2022 |
| 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 | 6 | 2022 |
| AFLGuard: Byzantine-robust Asynchronous Federated Learning M Fang, J Liu, NZ Gong, ES Bentley Annual Computer Security Applications Conference (ACSAC), 2022 | 18 | 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 | 15 | 2022 |
| 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 | 42 | 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 | 263 | 2021 |
| 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 | 552 | 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 | 27 | 2020 |
Neil Zhenqiang GongAssociate Professor, Duke University在 duke.edu 的电子邮件经过验证
