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Zhe Li

Orcid: 0009-0003-4703-4339

Affiliations:
  • Rochester Institute of Technology, Rochester, NY, USA


According to our database1, Zhe Li authored at least 10 papers between 2024 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Learning Dynamics of Zeroth-Order Optimization: A Kernel Perspective.
[BibTeX]
[DOI]
Zhe Li
,
Bicheng Ying
,
Zidong Liu
,
Haibo Yang
CoRR, May, 2026

HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training.
[BibTeX]
[DOI]
Aakriti Lnu
,
Zhe Li
,
Dandan Liang
,
Chao Huang
,
Rui Li
,
Haibo Yang
CoRR, January, 2026

Exploring Large Language Models' Potential for Privacy Leakage Detection in Android App Logs: An Empirical Study.
[BibTeX]
[DOI]
Zhiyuan Chen
,
Vanessa Nava-Camal
,
Tiash Roy
,
Zhe Li
,
Yiming Tang
,
Xueling Zhang
,
Haibo Yang
IEEE Softw., 2026

2025
Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach.
[BibTeX]
[DOI]
Dandan Liang
,
Jianing Zhang
,
Evan Chen
,
Zhe Li
,
Rui Li
,
Haibo Yang
CoRR, October, 2025

Reconciling Hessian-Informed Acceleration and Scalar-Only Communication for Efficient Federated Zeroth-Order Fine-Tuning.
[BibTeX]
[DOI]
Zhe Li
,
Bicheng Ying
,
Zidong Liu
,
Chaosheng Dong
,
Haibo Yang
CoRR, June, 2025

From Interpretation to Correction: A Decentralized Optimization Framework for Exact Convergence in Federated Learning.
[BibTeX]
[DOI]
Bicheng Ying
,
Zhe Li
,
Haibo Yang
CoRR, March, 2025

FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning.
[BibTeX]
[DOI]
Zhe Li
,
Seyedsina Nabavirazavi
,
Bicheng Ying
,
S. Sitharama Iyengar
,
Haibo Yang
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025

Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization.
[BibTeX]
[DOI]
Zhe Li
,
Bicheng Ying
,
Zidong Liu
,
Chaosheng Dong
,
Haibo Yang
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Achieving Extremely Low Communication Overhead in Federated Learning via Zeroth-Order SignSGD.
[BibTeX]
[DOI]
Zhe Li
,
Bicheng Ying
,
Dandan Liang
,
Zidong Liu
,
Rui Li
,
Haibo Yang
Proceedings of the 59th Asilomar Conference on Signals, 2025

2024
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization.
[BibTeX]
[DOI]
Zhe Li
,
Bicheng Ying
,
Zidong Liu
,
Haibo Yang
CoRR, 2024


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