58 citations · 147 across the 67 of their papers we have counts for
24 papers · 1 filter
Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration
Xuhan Zuo, Minghao Wang, Tianqing Zhu +2
Large language models (LLMs) have transformed the way computers understand and process human language, but using them effectively across different organizations remains still diffi…
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…
Defending Against Neural Network Model Inversion Attacks via Data Poisoning
Shuai Zhou, Dayong Ye, Tianqing Zhu +1
Model inversion attacks pose a significant privacy threat to machine learning models by reconstructing sensitive data from their outputs. While various defenses have been proposed…
Large Language Models Merging for Enhancing the Link Stealing Attack on Graph Neural Networks
Faqian Guan, Tianqing Zhu, Wenhan Chang +2
Graph Neural Networks (GNNs), specifically designed to process the graph data, have achieved remarkable success in various applications. Link stealing attacks on graph data pose a…
New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook
Meng Yang, Tianqing Zhu, Chi Liu +3
Thanks to the explosive growth of data and the development of computational resources, it is possible to build pre-trained models that can achieve outstanding performance on variou…
Machine Unlearning on Pre-trained Models by Residual Feature Alignment Using LoRA
Laiqiao Qin, Tianqing Zhu, Linlin Wang +1
Machine unlearning is an emerging technology that removes a subset of the training data from a trained model without significantly affecting the model performance on the remaining…
Evaluating of Machine Unlearning: Robustness Verification Without Prior Modifications
Heng Xu, Tianqing Zhu, Wanlei Zhou
Machine unlearning, a process enabling pre-trained models to remove the influence of specific training samples, has attracted significant attention in recent years. While extensive…
Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
Wenhan Chang, Tianqing Zhu, Ping Xiong +3
In the rapid advancement of artificial intelligence, privacy protection has become crucial, giving rise to machine unlearning. Machine unlearning is a technique that removes specif…
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
Shang Wang, Tianqing Zhu, Dayong Ye +1
The deployment of large language models (LLMs) like ChatGPT and Gemini has shown their powerful natural language generation capabilities. However, these models can inadvertently le…
The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies
Feng He, Tianqing Zhu, Dayong Ye +3
Inspired by the rapid development of Large Language Models (LLMs), LLM agents have evolved to perform complex tasks. LLM agents are now extensively applied across various domains,…
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…
Large Language Models for Link Stealing Attacks Against Graph Neural Networks
Faqian Guan, Tianqing Zhu, Hui Sun +2
Graph data contains rich node features and unique edge information, which have been applied across various domains, such as citation networks or recommendation systems. Graph Neura…
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…
Don't Forget Too Much: Towards Machine Unlearning on Feature Level
Heng Xu, Tianqing Zhu, Wanlei Zhou +1
Machine unlearning enables pre-trained models to remove the effect of certain portions of training data. Previous machine unlearning schemes have mainly focused on unlearning a clu…
Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives
Linlin Wang, Tianqing Zhu, Wanlei Zhou +1
Federated learning is fast becoming a popular paradigm for applications involving mobile devices, banking systems, healthcare, and IoT systems. Hence, over the past five years, res…
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
Laiqiao Qin, Tianqing Zhu, Wanlei Zhou +1
Federated Learning (FL) is a distributed and privacy-preserving machine learning paradigm that coordinates multiple clients to train a model while keeping the raw data localized. H…
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…
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security
Youyang Qu, Ming Liu, Tianqing Zhu +3
Federated Learning (FL) offers a promising paradigm for training Large Language Models (LLMs) in a decentralized manner while preserving data privacy and minimizing communication o…
Unique Security and Privacy Threats of Large Language Models: A Comprehensive Survey
Shang Wang, Tianqing Zhu, Bo Liu +4
With the rapid development of artificial intelligence, large language models (LLMs) have made remarkable advancements in natural language processing. These models are trained on va…
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…
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning
Wenhan Chang, Tianqing Zhu, Heng Xu +2
Machine unlearning aims to remove the influence of specified training data or knowledge from a trained model without requiring full retraining. This capability is particularly impo…
Machine Unlearning via Null Space Calibration
Huiqiang Chen, Tianqing Zhu, Xin Yu +1
Machine unlearning aims to enable models to forget specific data instances when receiving deletion requests. Current research centres on efficient unlearning to erase the influence…