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 \emph{exchangeability} required for conformal
prediction.
We propose a weaker notion of \emph{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.

本文将拟合预测应用于联邦学习环境，解决了数据异构问题，并提出了适用于联邦学习的局部可交换性概念，从而开发了联邦拟合预测框架（FCP）。实验证明该框架在计算机视觉和医学图像数据集上具有严格的理论保证和良好的经验性能，提供了在分布式和异构环境中结合有意义的不确定性量化的实用方法。