  Federated Learning (FL) is a machine learning framework where many clients
collaboratively train models while keeping the training data decentralized.
Despite recent advances in FL, the uncertainty quantification topic (UQ)
remains partially addressed. Among UQ methods, conformal prediction (CP)
approaches provides distribution-free guarantees under minimal assumptions. We
develop a new federated conformal prediction method based on quantile
regression and take into account privacy constraints. This method takes
advantage of importance weighting to effectively address the label shift
between agents and provides theoretical guarantees for both valid coverage of
the prediction sets and differential privacy. Extensive experimental studies
demonstrate that this method outperforms current competitors.
