Zhe Li

Conference Papers

1. Achieving Extremely Low Communication Overhead in Federated Learning via Zeroth-Order SignSGD.
   Zhe Li, Bicheng Ying, Dandan Liang, Zidong Liu, Rui Li, and Haibo Yang.
   Asilomar Conference on Signals, Systems, and Computers 2025. (Invited Paper)
   

2. Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable.
   Bicheng Ying, Zhe Li, and Haibo Yang.
   Advances in Neural Information Processing Systems (NeurIPS) 2025. (acceptance rate: 24.52%)
   [Code]

3. Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach.
   Dandan Liang, Jianing Zhang, Evan Chen, Zhe Li, Rui Li, and Haibo Yang.
   Advances in Neural Information Processing Systems (NeurIPS) 2025. (acceptance rate: 24.52%)
   

4. Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization.
   Zhe Li, Bicheng Ying, Zidong Liu, Chaosheng Dong, and Haibo Yang.
   International Conference on Learning Representations (ICLR) 2025. (acceptance rate: 32.08%)
   [Code] [FL@FM-NeurIPS'24 Workshop Version]

5. FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning.
   Zhe Li, Seyedsina Nabavirazavi, Bicheng Ying, Sitharama Iyengar, and Haibo Yang.
   International Joint Conference on Artificial Intelligence (IJCAI) 2025. (acceptance rate: 19.3%)
   [Code]

Journal Papers

1. Exploring LLMs' Potential for Privacy Leakage Detection in Android App Logs: An Empirical Study.
   Zhiyuan Chen, Vanessa Nava-Camal, Tiash Roy, Zhe Li, Yiming Tang, Xueling Zhang, and Haibo Yang.
   IEEE Software.
   

Preprints and Submitted Papers

• Reconciling Hessian-Informed Acceleration and Scalar-Only Communication for Efficient Federated Zeroth-Order Fine-Tuning.
  Zhe Li, Bicheng Ying, Zidong Liu, Chaosheng Dong, and Haibo Yang.
  Under review.