5 citations · 7 across the 14 of their papers we have counts for
15 papers
Turning Black Box into White Box: Dataset Distillation Leaks
Huajie Chen, Tianqing Zhu, Yuchen Zhong +7
Dataset distillation compresses a large real dataset into a small synthetic one, enabling models trained on the synthetic data to achieve performance comparable to those trained on…
Forgetting Similar Samples: Can Machine Unlearning Do it Better?
Heng Xu, Tianqing Zhu, Dayong Ye +3
Machine unlearning, a process enabling pre-trained models to remove the influence of specific training samples, has attracted significant attention in recent years. Although extens…
VIPER Strike: Defeating Visual Reasoning CAPTCHAs via Structured Vision-Language Inference
Minfeng Qi, Dongyang He, Qin Wang +1
Visual Reasoning CAPTCHAs (VRCs) combine visual scenes with natural-language queries that demand compositional inference over objects, attributes, and spatial relations. They are i…
The Trust Paradox in LLM-Based Multi-Agent Systems: When Collaboration Becomes a Security Vulnerability
Zijie Xu, Minfeng Qi, Shiqing Wu +4
Multi-agent systems powered by large language models are advancing rapidly, yet the tension between mutual trust and security remains underexplored. We introduce and empirically va…
Collaborative Text-to-Image Generation via Multi-Agent Reinforcement Learning and Semantic Fusion
Jiabao Shi, Minfeng Qi, Lefeng Zhang +5
Multimodal text-to-image generation remains constrained by the difficulty of maintaining semantic alignment and professional-level detail across diverse visual domains. We propose…
Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach
Minfeng Qi, Tianqing Zhu, Lefeng Zhang +2
Large Language Models (LLMs) have enabled the emergence of autonomous agents capable of complex reasoning, planning, and interaction. However, coordinating such agents at scale rem…
Vertical Federated Unlearning via Backdoor Certification
Mengde Han, Tianqing Zhu, Lefeng Zhang +2
Vertical Federated Learning (VFL) offers a novel paradigm in machine learning, enabling distinct entities to train models cooperatively while maintaining data privacy. This method…
Game-Theoretic Machine Unlearning: Mitigating Extra Privacy Leakage
Hengzhu Liu, Tianqing Zhu, Lefeng Zhang +1
With the extensive use of machine learning technologies, data providers encounter increasing privacy risks. Recent legislation, such as GDPR, obligates organizations to remove requ…
QUEEN: Query Unlearning against Model Extraction
Huajie Chen, Tianqing Zhu, Lefeng Zhang +4
Model extraction attacks currently pose a non-negligible threat to the security and privacy of deep learning models. By querying the model with a small dataset and usingthe query r…
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
Heng Xu, Tianqing Zhu, Lefeng Zhang +2
Federated learning is a promising privacy-preserving paradigm for distributed machine learning. In this context, there is sometimes a need for a specialized process called machine…
Towards Efficient Target-Level Machine Unlearning Based on Essential Graph
Heng Xu, Tianqing Zhu, Lefeng Zhang +2
Machine unlearning is an emerging technology that has come to attract widespread attention. A number of factors, including regulations and laws, privacy, and usability concerns, ha…
Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs
Heng Xu, Tianqing Zhu, Lefeng Zhang +1
Machine unlearning enables pre-trained models to eliminate the effects of partial training samples. Previous research has mainly focused on proposing efficient unlearning strategie…
Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning
Xuhan Zuo, Minghao Wang, Tianqing Zhu +4
The development of Large Language Models (LLMs) faces a significant challenge: the exhausting of publicly available fresh data. This is because training a LLM needs a large demandi…
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
Xuhan Zuo, Minghao Wang, Tianqing Zhu +3
With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imp…
Machine Unlearning: A Survey
Heng Xu, Tianqing Zhu, Lefeng Zhang +2
Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision,…