  Understanding causal relations is vital in scientific discovery. The process
of causal structure learning involves identifying causal graphs from
observational data to understand such relations. Usually, a central server
performs this task, but sharing data with the server poses privacy risks.
Federated learning can solve this problem, but existing solutions for federated
causal structure learning make unrealistic assumptions about data and lack
convergence guarantees. FedC2SL is a federated constraint-based causal
structure learning scheme that learns causal graphs using a federated
conditional independence test, which examines conditional independence between
two variables under a condition set without collecting raw data from clients.
FedC2SL requires weaker and more realistic assumptions about data and offers
stronger resistance to data variability among clients. FedPC and FedFCI are the
two variants of FedC2SL for causal structure learning in causal sufficiency and
causal insufficiency, respectively. The study evaluates FedC2SL using both
synthetic datasets and real-world data against existing solutions and finds it
demonstrates encouraging performance and strong resilience to data
heterogeneity among clients.
