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Publications [Google Scholar]

  • Byzantine-Robust Decentralized Federated Learning. Created with Fabric.js 1.7.22 PDF

    Minghong Fang, Zifan Zhang, Hairi, Prashant Khanduri, Jia (Kevin) Liu, Songtao Lu, Yuchen Liu, and Neil Gong.

    In Proc. ACM CCS, 2024.

  • Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks. Created with Fabric.js 1.7.22 PDF

    Zifan Zhang, Minghong Fang, Mingzhe Chen, Gaolei Li, Xi Lin, and Yuchen Liu.

    In IEEE Internet of Things Journal, 2024.

  • Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation. Created with Fabric.js 1.7.22 PDF

    Haibo Yang, Peiwen Qiu, Prashant Khanduri, Minghong Fang, and Jia Liu.

    In Proc. ICML, 2024.

  • FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error. Created with Fabric.js 1.7.22 PDF Code

    Yueqi XIE, Minghong Fang, and Neil Zhenqiang Gong.

    In Proc. ICML, 2024.

  • Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction. Created with Fabric.js 1.7.22 PDF

    Zifan Zhang, Minghong Fang, Jiayuan Huang, and Yuchen Liu.

    In Proc. IFIP/IEEE Networking, 2024.

    Best Paper Runner-up Award

  • PoisonedFL: Model Poisoning Attacks to Federated Learning via Multi-Round Consistency. Created with Fabric.js 1.7.22 PDF

    Yueqi Xie, Minghong Fang, and Neil Zhenqiang Gong.

    Preprint, 2024.

  • GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis. Created with Fabric.js 1.7.22 PDF Code

    Yueqi Xie, Minghong Fang, Renjie Pi, and Neil Gong.

    In Proc. ACL, 2024.

  • Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks. Created with Fabric.js 1.7.22 PDF

    Yichang Xu*, Ming Yin*, Minghong Fang, and Neil Zhenqiang Gong.

    In Proc. The Web Conference (WWW), 2024 (*co-primary authors).

    Yichang Xu and Ming Yin are undergraduate students mentored by me.

  • Poisoning Federated Recommender Systems with Fake Users. Created with Fabric.js 1.7.22 PDF

    Ming Yin*, Yichang Xu*, Minghong Fang, and Neil Zhenqiang Gong.

    In Proc. The Web Conference (WWW), 2024 (*co-primary authors).

    Ming Yin and Yichang Xu are undergraduate students mentored by me.

  • Competitive Advantage Attacks to Decentralized Federated Learning. Created with Fabric.js 1.7.22 PDF

    Yuqi Jia, Minghong Fang, and Neil Zhenqiang Gong.

    Preprint, 2023.

  • IPCert: Provably Robust Intellectual Property Protection for Machine Learning. Created with Fabric.js 1.7.22 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. Created with Fabric.js 1.7.22 PDF

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

    In Bioresource Technology Reports, 2022.

  • AFLGuard: Byzantine-robust Asynchronous Federated Learning. Created with Fabric.js 1.7.22 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. Created with Fabric.js 1.7.22 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. Created with Fabric.js 1.7.22 PDF

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

    In Proc. ACM SACMAT, 2022.

  • Data Poisoning Attacks and Defenses to Crowdsourcing Systems. Created with Fabric.js 1.7.22 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. Created with Fabric.js 1.7.22 PDF

    Haibo Yang, Minghong Fang, and Jia Liu.

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

  • FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. Created with Fabric.js 1.7.22 PDF Code

    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. Created with Fabric.js 1.7.22 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. Created with Fabric.js 1.7.22 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. Created with Fabric.js 1.7.22 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. Created with Fabric.js 1.7.22 PDF

    Minghong Fang and Jia Liu.

    In Proc. IEEE ICC, 2020.

  • Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. Created with Fabric.js 1.7.22 PDF Code

    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. Created with Fabric.js 1.7.22 PDF

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

    In Proc. IEEE CDC, 2019.

  • Poisoning Attacks to Graph-Based Recommender Systems. Created with Fabric.js 1.7.22 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. Created with Fabric.js 1.7.22 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. Created with Fabric.js 1.7.22 PDF

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

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