Publications [Google Scholar]

• Competitive Advantage Attacks to Decentralized Federated Learning. PDF

  Yuqi Jia, Minghong Fang, and Neil Zhenqiang Gong.

  Preprint, 2023.

• IPCert: Provably Robust Intellectual Property Protection for Machine Learning. PDF

  Zhengyuan Jiang, Minghong Fang, and Neil Zhenqiang Gong.

  In Proc. ICCV Workshops, 2023.

• Machine learning-based modeling approaches for estimating pyrolysis products of varied biomass and operating conditions. PDF

  Jiangfeng Shen, Mengguo Yan, Minghong Fang, and Xi Gao.

  In Bioresource Technology Reports, 2022.

• AFLGuard: Byzantine-robust Asynchronous Federated Learning. PDF

  Minghong Fang, Jia Liu, Neil Zhenqiang Gong, and Elizabeth S. Bentley.

  In Proc. ACM ACSAC, 2022 (acceptance rate: 24.1%).

• NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data. PDF

  Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, and Zhengyuan Zhu.

  In Proc. ACM MobiHoc, 2022 (acceptance rate: 19.8%).

• FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data. PDF

  Minghong Fang, Jia Liu, Michinari Momma, and Yi Sun.

  In Proc. ACM SACMAT, 2022.

• Data Poisoning Attacks and Defenses to Crowdsourcing Systems. PDF

  Minghong Fang, Minghao Sun, Qi Li, Neil Zhenqiang Gong, Jin Tian, and Jia Liu.

  In Proc. The Web Conference (WWW), 2021 (acceptance rate: 20.6%).

• Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning. PDF

  Haibo Yang, Minghong Fang, and Jia Liu.

  In Proc. ICLR, 2021 (acceptance rate: 28.7%).

• FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. PDF

  Xiaoyu Cao*, Minghong Fang*, Jia Liu, and Neil Zhenqiang Gong.

  In Proc. NDSS, 2021 (*co-primary authors, acceptance rate: 15.2%).

• Adaptive Multi-Hierarchical signSGD for Communication-Efficient Distributed Optimization. PDF

  Haibo Yang, Xin Zhang, Minghong Fang, and Jia Liu.

  In Proc. IEEE SPAWC, Special Session on Distributed Signal Processing for Coding and Communications, 2020 (Invited Paper).

• Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach. PDF

  Xin Zhang, Minghong Fang, Jia Liu, and Zhengyuan Zhu.

  In Proc. ACM MobiHoc, 2020 (acceptance rate: 15%).

• Influence Function based Data Poisoning Attacks to Top-N Recommender Systems. PDF

  Minghong Fang, Neil Zhenqiang Gong, and Jia Liu.

  In Proc. The Web Conference (WWW), 2020 (acceptance rate: 25%).

• Toward Low-Cost and Stable Blockchain Networks. PDF

  Minghong Fang and Jia Liu.

  In Proc. IEEE ICC, 2020.

• Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. PDF

  Minghong Fang*, Xiaoyu Cao*, Jinyuan Jia, and Neil Zhenqiang Gong.

  In Proc. USENIX Security Symposium, 2020 (*co-primary authors, acceptance rate: 16.1%).

  Measured as one of the Normalized Top-100 Security Papers since 1981.

• Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach. PDF

  Haibo Yang, Xin Zhang, Minghong Fang, and Jia Liu.

  In Proc. IEEE CDC, 2019.

• Poisoning Attacks to Graph-Based Recommender Systems. PDF

  Minghong Fang, Guolei Yang, Neil Zhenqiang Gong, and Jia Liu.

  In Proc. ACSAC, 2018 (acceptance rate: 20.1%).

• Prioritizing Disease-Causing Genes Based on Network Diffusion and Rank Concordance. PDF

  Minghong Fang, Xiaohua Hu, Tingting He, Yan Wang, Junmin Zhao, Xianjun Shen, and Jie Yuan.

  In Proc. IEEE BIBM, 2014 (acceptance rate: 19%).

• A Novel Disease Gene Prediction Method Based on PPI Network. PDF

  Junmin Zhao, Tingting He, Xiaohua Hu, Yan Wang, Xianjun Shen, Minghong Fang, and Jie Yuan.

  In Proc. IEEE BIBM, 2014 (acceptance rate: 19%).