3 citations · 7 across the 11 of their papers we have counts for
11 papers
CT-OpenMax: A Novel Open-Set WiFi RF Fingerprinting Method via Center Constrained Learning and Confidence-Guided Tail Modeling
Yuanyu Zhang, Junjie Yang, Ji He +3
Radio frequency fingerprinting (RFF) enables device authentication from transmitter-specific hardware imperfections, but practical deployment requires cross-environment open-set re…
Security and Privacy in Large-Model-Driven Embodied Agents: Attacks, Defenses, and Future Directions
Lele Zheng, Tong Chen, Ke Cheng +5
Large-model-driven embodied agents integrate foundation models with perception, reasoning, planning, and physical action, extending conventional model-level risks into embodied clo…
From Bilinear to Linear: Differentially Private Federated LoRA via Low-Dimensional Parameterization
Lele Zheng, Ruijie Hu, Tao Zhang +2
Federated Low-Rank Adaptation (LoRA) provides an efficient solution for finetuning large language models across distributed and privacy-sensitive data. However, despite avoiding ra…
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-…
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…
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…
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions
Hao Du, Shang Liu, Lele Zheng +3
Fine-tuning has emerged as a critical process in leveraging Large Language Models (LLMs) for specific downstream tasks, enabling these models to achieve state-of-the-art performanc…
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…
Graph Analysis in Decentralized Online Social Networks with Fine-Grained Privacy Protection
Lele Zheng, Bowen Deng, Tao Zhang +2
Graph analysts cannot directly obtain the global structure in decentralized social networks, and analyzing such a network requires collecting local views of the social graph from i…