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cs.LG2026
When Privacy Hurts Mergeability: Geometry-Aware Model Merging under Differential Privacy
Jin Liu, Junkang Liu, Ning Xi +4
Model merging promises to construct a single multi-task model from independently fine-tuned task models without accessing the original task data. This makes it attractive when task…
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.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…