3 papers
cs.LG2026
Noise-Aware Shrinkage for Differentially Private Zeroth-Order Fine-Tuning of Large Language Models
Lele Zheng, Weifeng Kong, Xinyi Zhang +3
Differentially private zeroth-order optimization (DP-ZO) enables memory-efficient private fine-tuning of large language models using only forward evaluations. Existing aggregation-…
cs.CR2026
FedGSA: Geometry-Consistent Subspace Aggregation for Differentially Private Federated LoRA
Lele Zheng, Ruijie Hu, Tao Zhang +2
Low-Rank Adaptation (LoRA) enables communication-efficient federated fine-tuning of pretrained language models. However, integrating differential privacy (DP) into federated LoRA r…
cs.LG2026
Differentially Private Subspace Fine-Tuning for Large Language Models
Lele Zheng, Xiang Wang, Tao Zhang +3
Fine-tuning large language models on downstream tasks is crucial for realizing their cross-domain potential but often relies on sensitive data, raising privacy concerns. Differenti…