Federated local causal structure learning  

作　　者：Kui YU Chen RONG Hao WANG Fuyuan CAO Jiye LIANG 

机构地区：[1]School of Computer and Information,Hefei University of Technology,Hefei 230601,China [2]School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China

出　　处：《Science China(Information Sciences)》2025年第3期106-120,共15页中国科学(信息科学)(英文版)

基　　金：supported by National Science and Technology Major Project of China(Grant No.2020AAA0106100);National Natural Science Foundation of China(Grant Nos.62376087,62306002)。

摘　　要：Local causal structure learning(LCS)efficiently identifies a set of direct neighbors of a specified variable from observational data.Additionally,it distinguishes direct causes and direct effects of this variable without learning the entire causal structure.While many LCS algorithms have been proposed,they do not consider the data privacy-preserving problem,which has attracted extensive attention from academia and industry.To address this issue,we propose a federated local causal structure learning(FedLCS)algorithm to learn local causal structures in privacy-preserving data in a federated setting.Specifically,FedLCS introduces a layer-wise federated local skeleton learning algorithm to construct the local skeleton.Based on this skeleton,it introduces a federated local skeleton orientation algorithm and an extension-and-backtracking orientation algorithm to orient the edges.Finally,FedLCS uses a federated local extension-and-backtracking orientation algorithm to orient the remaining edges.Extensive experiments on benchmark,synthetic,and real datasets demonstrate that FedLCS can learn the local causal structure of a given variable in a federated setting.

关 键 词：local causal structure learning federated learning directed acyclic graph privacy-preserving data federated layer-wise strategy 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP309[自动化与计算机技术—控制科学与工程]