Causal Multi-Label Feature Selection in Federated Setting
Yukun Song Dayuan Cao Jiali Miao Shuai Yang Kui Yu
Multi-label feature selection serves as an effective mean for dealing with
high-dimensional multi-label data. To achieve satisfactory performance,
existing methods for multi-label feature selection often require the
centralization of substantial data from multiple sources. However, in Federated
setting, centralizing data from all sources and merging them into a single
dataset is not feasible. To tackle this issue, in this paper, we study a
challenging problem of causal multi-label feature selection in federated
setting and propose a Federated Causal Multi-label Feature Selection (FedCMFS)
algorithm with three novel subroutines. Specifically, FedCMFS first uses the
FedCFL subroutine that considers the correlations among label-label,
label-feature, and feature-feature to learn the relevant features (candidate
parents and children) of each class label while preserving data privacy without
centralizing data. Second, FedCMFS employs the FedCFR subroutine to selectively
recover the missed true relevant features. Finally, FedCMFS utilizes the FedCFC
subroutine to remove false relevant features. The extensive experiments on 8
datasets have shown that FedCMFS is effect for causal multi-label feature
selection in federated setting.