Author

Lele Zheng

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Preprint Aug 2026

Noise-Aware Shrinkage for Differentially Private Zeroth-Order Fine-Tuning of Large Language Models

Differentially private zeroth-order optimization (DP-ZO) enables memory-efficient private fine-tuning of large language models using only forward evaluations. Existing aggregation-based DP-ZO methods reconstruct model updates at a fixed scale, ignoring that the strength of useful signals varies throughout training. Con...

Le-Le Zheng, Wei-Feng Kong, Xinyi Zhang et al. · 0 citations

Preprint Sep 2026

One Pipeline Does Not Fit All: TAILOR, a Type- and State-Aware Framework for CVE Reproduction

Growing vulnerability disclosure and widespread software reuse increase security teams'need for reproducible evidence to diagnose vulnerabilities, validate patches, and build regression tests. Producing such evidence at scale requires automated end-to-end CVE reproduction. Existing methods typically process different C...

Ji He, Huang Zhang, Li-Jie Zheng et al. · 0 citations

Preprint Aug 2026

From Bilinear to Linear: Differentially Private Federated LoRA via Low-Dimensional Parameterization

Federated Low-Rank Adaptation (LoRA) provides an efficient solution for finetuning large language models across distributed and privacy-sensitive data. However, despite avoiding raw data sharing, federated LoRA remains vulnerable to privacy leakage through transmitted model updates. Differential privacy (DP) mitigates...

Le-Le Zheng, Rui Hu, Tao Zhang et al. · 0 citations

Preprint Aug 2026

FedGSA: Geometry-Consistent Subspace Aggregation for Differentially Private Federated LoRA

FedGSA, a geometry-consistent aggregation framework for differentially private federated LoRA, is proposed and it is proved that FedGSA incurs no additional privacy loss beyond client-side DP training and establishes its convergence under standard assumptions.

Le-Le Zheng, Rui Hu, Tao Zhang et al. · 0 citations