2 citations · 2 across the 6 of their papers we have counts for
6 papers
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-…
Cognitive Agency Surrender: Defending Epistemic Sovereignty via Scaffolded AI Friction
Kuangzhe Xu, Yu Shen, Longjie Yan +1
The proliferation of Generative Artificial Intelligence has transformed benign cognitive offloading into a systemic risk of cognitive agency surrender. Driven by the commercial dog…
When Convenience Becomes Risk: A Semantic View of Under-Specification in Host-Acting Agents
Di Lu, Yongzhi Liao, Xutong Mu +5
Host-acting agents promise a convenient interaction model in which users specify goals and the system determines how to realize them. We argue that this convenience introduces a di…
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…
Extracting Spatiotemporal Data from Gradients with Large Language Models
Lele Zheng, Yang Cao, Renhe Jiang +4
Recent works show that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primar…
Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
Lele Zheng, Yang Cao, Renhe Jiang +4
Spatiotemporal federated learning has recently raised intensive studies due to its ability to train valuable models with only shared gradients in various location-based services. O…