4 citations · 16 across the 43 of their papers we have counts for
9 papers · 1 filter
RedTopic: Toward Topic-Diverse Red Teaming of Large Language Models
Jiale Ding, Xiang Zheng, Yutao Wu +5
As large language models (LLMs) are increasingly deployed as black-box components in real-world applications, red teaming has become essential for identifying potential risks. It t…
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
Yong Xie, Weijie Zheng, Hanxun Huang +2
As deep learning models are increasingly deployed in safety-critical applications, evaluating their vulnerabilities to adversarial perturbations is essential for ensuring their rel…
SIDE: Surrogate Conditional Data Extraction from Diffusion Models
Yunhao Chen, Shujie Wang, Difan Zou +1
As diffusion probabilistic models (DPMs) become central to Generative AI (GenAI), understanding their memorization behavior is essential for evaluating risks such as data leakage,…
Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models
Zezhou Wang, Yaxin Du, Xingjun Ma +3
Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data…
CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems
Ziming Zhao, Zhenwei Wang, Tiehua Zhang +7
In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and r…
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
Liuzhi Zhou, Yu He, Kun Zhai +6
Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients while preserving data privacy. However,…
ModelLock: Locking Your Model With a Spell
Yifeng Gao, Yuhua Sun, Xingjun Ma +2
This paper presents a novel model protection paradigm ModelLock that locks (destroys) the performance of a model on normal clean data so as to make it unusable or unextractable wit…
FedEGG: Federated Learning with Explicit Global Guidance
Kun Zhai, Yifeng Gao, Difan Zou +4
Federated Learning (FL) holds great potential for diverse applications owing to its privacy-preserving nature. However, its convergence is often challenged by non-IID data distribu…
Shortcuts Everywhere and Nowhere: Exploring Multi-Trigger Backdoor Attacks
Yige Li, Jiabo He, Hanxun Huang +3
Backdoor attacks have become a significant threat to the pre-training and deployment of deep neural networks (DNNs). Although numerous methods for detecting and mitigating backdoor…