19 citations · 38 across the 14 of their papers we have counts for
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cs.LG2023
Meta Adaptive Task Sampling for Few-Domain Generalization
Zheyan Shen, Han Yu, Peng Cui +4
To ensure the out-of-distribution (OOD) generalization performance, traditional domain generalization (DG) methods resort to training on data from multiple sources with different u…
cs.LG2023★ 1 cited
Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning
Xiaoli Tang, Han Yu
Auction-based Federated Learning (AFL) has attracted extensive research interest due to its ability to motivate data owners to join FL through economic means. Existing works assume…
cs.LG2023★ 4 cited
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning
Anran Li, Hongyi Peng, Lan Zhang +4
Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a use…