Abstract. In critical applications, causal models are the prime choice for their trustworthiness and explainability. If data is inherently distributed and privacy-sensitive, federated learning allows for collaboratively training a joint model. Such approaches for federated causal discovery, however, require sending local causal models, revealing the local data structure. We propose privacy-preserving federated causal discovery by distributed min-max regret optimization. This technique requires clients to only send local regret values, instead of model parameters, ensuring the privacy of sensitive local data. Initial results show that our approach reliably discovers causal networks without ever looking at local data or local causal structures.