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20212023
most citedFedKD: Communication Efficient Federated Learning via Knowledge Distillation

623 citations · 780 across the 21 of their papers we have counts for

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Showing cs.IRShow all

5 papers · 1 filter

cs.IR2022★ 2 cited

Two-Stage Neural Contextual Bandits for Personalised News Recommendation

Mengyan Zhang, Thanh Nguyen-Tang, Fangzhao Wu +3

We consider the problem of personalised news recommendation where each user consumes news in a sequential fashion. Existing personalised news recommendation methods focus on exploi…

cs.IR2022★ 4 cited

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…

cs.IR2022★ 3 cited

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…

cs.IR2022★ 1 cited

UA-FedRec: Untargeted Attack on Federated News Recommendation

Jingwei Yi, Fangzhao Wu, Bin Zhu +4

News recommendation is critical for personalized news distribution. Federated news recommendation enables collaborative model learning from many clients without sharing their raw d…

cs.IR2021★ 2 cited

Personalized News Recommendation: Methods and Challenges

Chuhan Wu, Fangzhao Wu, Yongfeng Huang +1

Personalized news recommendation is an important technique to help users find their interested news information and alleviate their information overload. It has been extensively st…