Author

Lele Zheng

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Preprint Aug 2026

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

SAGE is proposed, a noise-aware shrinkage method that adaptively attenuates privatized estimates according to their estimated signal quality, and shows that shrinkage reduces the quadratic update-risk term faster than the linear descent term, preserving useful descent while limiting the influence of noise-dominated upd...

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

TAILOR broadens the coverage of automated CVE reproduction and provides auditable evidence for vulnerability diagnosis and defense and ablation experiments show that the two control levels respectively mitigate execution-path mismatch and missing Web prerequisite state.

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

FedHSIP reformulates all LoRA parameters into a shared low-dimensional trainable vector, enabling clients to optimize and communicate only low-dimensional updates, and transforms federated LoRA from a bilinear factor aggregation problem into a unified linear parameter space, thereby eliminating aggregation mismatch and...

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