  Causal structure learning has been extensively studied and widely used in
machine learning and various applications. To achieve an ideal performance,
existing causal structure learning algorithms often need to centralize a large
amount of data from multiple data sources. However, in the privacy-preserving
setting, it is impossible to centralize data from all sources and put them
together as a single dataset. To preserve data privacy, federated learning as a
new learning paradigm has attracted much attention in machine learning in
recent years. In this paper, we study a privacy-aware causal structure learning
problem in the federated setting and propose a novel Federated PC (FedPC)
algorithm with two new strategies for preserving data privacy without
centralizing data. Specifically, we first propose a novel layer-wise
aggregation strategy for a seamless adaptation of the PC algorithm into the
federated learning paradigm for federated skeleton learning, then we design an
effective strategy for learning consistent separation sets for federated edge
orientation. The extensive experiments validate that FedPC is effective for
causal structure learning in a federated learning setting.
