  Conformal prediction is emerging as a popular paradigm for providing rigorous
uncertainty quantification in machine learning since it can be easily applied
as a post-processing step to already trained models. In this paper, we extend
conformal prediction to the federated learning setting. The main challenge we
face is data heterogeneity across the clients - this violates the fundamental
tenet of exchangeability required for conformal prediction. We propose a weaker
notion of partial exchangeability, better suited to the FL setting, and use it
to develop the Federated Conformal Prediction (FCP) framework. We show FCP
enjoys rigorous theoretical guarantees and excellent empirical performance on
several computer vision and medical imaging datasets. Our results demonstrate a
practical approach to incorporating meaningful uncertainty quantification in
distributed and heterogeneous environments. We provide code used in our
experiments https://github.com/clu5/federated-conformal.
