2 citations · 2 across the 16 of their papers we have counts for
9 papers · 1 filter
Bridging the Copyright Gap: Do Large Vision-Language Models Recognize and Respect Copyrighted Content?
Naen Xu, Jinghuai Zhang, Changjiang Li +7
Large vision-language models (LVLMs) have achieved remarkable advancements in multimodal reasoning tasks. However, their widespread accessibility raises critical concerns about pot…
FedAU2: Attribute Unlearning for User-Level Federated Recommender Systems with Adaptive and Robust Adversarial Training
Yuyuan Li, Junjie Fang, Fengyuan Yu +7
Federated Recommender Systems (FedRecs) leverage federated learning to protect user privacy by retaining data locally. However, user embeddings in FedRecs often encode sensitive at…
UFO: Unfair-to-Fair Evolving Mitigates Unfairness in LLM-based Recommender Systems via Self-Play Fine-tuning
Jiaming Zhang, Yuyuan Li, Xiaohua Feng +3
Large language model-based Recommender Systems (LRSs) have demonstrated superior recommendation performance by integrating pre-training with Supervised Fine-Tuning (SFT). However,…
TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models
Li Zhang, Zhongxuan Han, XiaoHua Feng +5
Efficient and lightweight adaptation of pre-trained Vision-Language Models (VLMs) to downstream tasks through collaborative interactions between local clients and a central server…
DP-GENG : Differentially Private Dataset Distillation Guided by DP-Generated Data
Shuo Shi, Jinghuai Zhang, Shijie Jiang +5
Dataset distillation (DD) compresses large datasets into smaller ones while preserving the performance of models trained on them. Although DD is often assumed to enhance data priva…
LEGO: A Lightweight and Efficient Multiple-Attribute Unlearning Framework for Recommender Systems
Fengyuan Yu, Yuyuan Li, Xiaohua Feng +3
With the growing demand for safeguarding sensitive user information in recommender systems, recommendation attribute unlearning is receiving increasing attention. Existing studies…
Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols
Xiaohua Feng, Jiaming Zhang, Fengyuan Yu +7
With the rapid advancement of generative models, privacy, copyright, safety, and reliability risks have attracted growing attention. To mitigate these risks, machine unlearning has…
A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures
Dezhang Kong, Shi Lin, Zhenhua Xu +16
In recent years, Large-Language-Model-driven AI agents have exhibited unprecedented intelligence and adaptability. Nowadays, agents are undergoing a new round of evolution. They no…
RAID: An In-Training Defense against Attribute Inference Attacks in Recommender Systems
Xiaohua Feng, Yuyuan Li, Fengyuan Yu +5
In various networks and mobile applications, users are highly susceptible to attribute inference attacks, with particularly prevalent occurrences in recommender systems. Attackers…