10 citations · 23 across the 6 of their papers we have counts for
6 papers
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization
Yueqi Xie, Weizhong Zhang, Renjie Pi +4
Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning (FL) systems. While both of them have attracted great resear…
Effective and Efficient Query-aware Snippet Extraction for Web Search
Jingwei Yi, Fangzhao Wu, Chuhan Wu +4
Query-aware webpage snippet extraction is widely used in search engines to help users better understand the content of the returned webpages before clicking. Although important, it…
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus o…
ProFairRec: Provider Fairness-aware News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +5
News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behav…
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Effectively finetuning pretrained language models (PLMs) is critical for their success in downstream tasks. However, PLMs may have risks in overfitting the pretraining tasks and da…
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Federated learning (FL) is a feasible technique to learn personalized recommendation models from decentralized user data. Unfortunately, federated recommender systems are vulnerabl…