26 citations · 81 across the 29 of their papers we have counts for
6 papers · 1 filter
WassFFed: Wasserstein Fair Federated Learning
Zhongxuan Han, Li Zhang, Chaochao Chen +4
Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training da…
FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
Xinting Liao, Weiming Liu, Pengyang Zhou +6
Federated learning (FL) is a promising machine learning paradigm that collaborates with client models to capture global knowledge. However, deploying FL models in real-world scenar…
PermLLM: Private Inference of Large Language Models within 3 Seconds under WAN
Fei Zheng, Chaochao Chen, Zhongxuan Han +1
The emergence of ChatGPT marks the arrival of the large language model (LLM) era. While LLMs demonstrate their power in a variety of fields, they also raise serious privacy concern…
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
Xinting Liao, Weiming Liu, Chaochao Chen +7
Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised…
Post-Training Attribute Unlearning in Recommender Systems
Chaochao Chen, Yizhao Zhang, Yuyuan Li +5
With the growing privacy concerns in recommender systems, recommendation unlearning is getting increasing attention. Existing studies predominantly use training data, i.e., model i…
Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation
Jiajie Su, Chaochao Chen, Zibin Lin +3
Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interacti…