22 citations · 35 across the 8 of their papers we have counts for
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cs.LG2024
BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation
Xianzhi Zhang, Yipeng Zhou, Miao Hu +4
To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without expo…
cs.LG2023
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment
Jiahao Liu, Jiang Wu, Jinyu Chen +3
Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique require…
cs.LG2021★ 1 cited
Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy
Yipeng Zhou, Xuezheng Liu, Yao Fu +3
Federated learning (FL) empowers distributed clients to collaboratively train a shared machine learning model through exchanging parameter information. Despite the fact that FL can…