7 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
Demystifying the Optimal Fair Classifier in Multi-Class Classification
Li Zhang, Yuyuan Li, XiaoHua Feng +3
Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent i…
Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendation
Fengyuan Yu, Xiaohua Feng, Yuyuan Li +3
Federated recommender systems enable collaborative model training while keeping user interaction data local and sharing only essential model parameters, thereby mitigating privacy…
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…
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…
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…