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Jingwei Sun

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jingwei.sun@duke.edu

Durham, NC, USA

I am currently a Ph.D. candidate in the Electrical and Computer Engineering department at Duke University under the supervision of Professor Yiran Chen and Hai “Helen” Li in Duke CEI Lab. My research interests lie in machine learning systems and edge computing, with a focus on collaborative intelligent systems and on-device AI with the awareness of effciency, security and privacy.

selected publications

  1. Knowledge Graph Tuning: Real-time Large Language Model Personalization based on Human Feedback
    Jingwei Sun, Zhixu Du, and Yiran Chen
    arXiv preprint arXiv:2405.19686, 2024
  2. FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models

    Best Paper Award in Federated Learning on the Edge, 2024 AAAI Spring Series Symposium

    Jingwei Sun, Ziyue Xu, Hongxu Yin, and 6 more authors
    Proceedings of the 41th International Conference on Machine Learning, 2024
  3. Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
    Jingwei Sun, Ziyue Xu, Dong Yang, and 6 more authors
    2023
  4. SenSys
    FedSEA: A Semi-Asynchronous Federated Learning Framework for Extremely Heterogeneous Devices
    Jingwei Sun, Ang Li, Lin Duan, and 8 more authors
    2022
  5. Soteria: Provable defense against privacy leakage in federated learning from representation perspective
    Jingwei Sun, Ang Li, Binghui Wang, and 3 more authors
    In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021
  6. Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective
    Jingwei Sun, Ang Li, Louis DiValentin, and 3 more authors
    Advances in Neural Information Processing Systems, 2021
  7. SenSys
    Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking
    Ang Li, Jingwei Sun, Xiao Zeng, and 3 more authors
    In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, 2021