Selected Research

Selected publications on distributed machine learning, efficiency, and privacy. Full publication list on Google Scholar.

Published Work

• 2025

  Gabriel Thompson, Kai Yue, Chau-Wai Wong, and Huaiyu Dai. “NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel.” ICML 2025.

  Code

• 2023

  Kai Yue, Richeng Jin, Chau-Wai Wong, Dror Baron, and Huaiyu Dai. “Gradient Obfuscation Gives a False Sense of Security in Federated Learning.” USENIX Security Symposium, 2023.

    paper   slides   presentation   code

• 2022

  Kai Yue, Richeng Jin, Ryan Pilgrim, Chau-Wai Wong, Dror Baron, Huaiyu Dai. “Neural Tangent Kernel Empowered Federated Learning.” ICML 2022.

    paper   poster   slides   news   presentation   code

  Kai Yue, Richeng Jin, Chau-Wai Wong, and Huaiyu Dai. “Communication-Efficient Federated Learning via Predictive Coding.” IEEE Journal of Selected Topics in Signal Processing (2022).

    paper   news   hackster.io   code

  
  

  Kai Yue, Richeng Jin, Chau-Wai Wong, and Huaiyu Dai. “Federated Learning via Plurality Vote.” IEEE Transactions on Neural Networks and Learning Systems, 2022.

    paper   code

Contact for questions about this work. See each linked repository for code availability and license terms.