  Causal discovery serves a pivotal role in mitigating model uncertainty
through recovering the underlying causal mechanisms among variables. In many
practical domains, such as healthcare, access to the data gathered by
individual entities is limited, primarily for privacy and regulatory
constraints. However, the majority of existing causal discovery methods require
the data to be available in a centralized location. In response, researchers
have introduced federated causal discovery. While previous federated methods
consider distributed observational data, the integration of interventional data
remains largely unexplored. We propose FedCDI, a federated framework for
inferring causal structures from distributed data containing interventional
samples. In line with the federated learning framework, FedCDI improves privacy
by exchanging belief updates rather than raw samples. Additionally, it
introduces a novel intervention-aware method for aggregating individual
updates. We analyze scenarios with shared or disjoint intervened covariates,
and mitigate the adverse effects of interventional data heterogeneity. The
performance and scalability of FedCDI is rigorously tested across a variety of
synthetic and real-world graphs.
