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.

翻译：保形预测正逐渐成为一种流行的范式，用于在机器学习中提供严格的不确定性量化，因为它可以轻松地作为后处理步骤应用于已训练的模型。在本文中，我们将保形预测扩展到联邦学习设置。我们面临的主要挑战是客户端之间的数据异质性——这违反了保形预测所需的可交换性基本原则。我们提出了一个较弱的局部可交换性概念，更适合联邦学习环境，并利用它开发了联邦保形预测（FCP）框架。我们证明FCP拥有严谨的理论保证，并在多个计算机视觉和医学影像数据集上展现出卓越的实证性能。我们的结果展示了一种在分布式和异质性环境中纳入有意义的不确定性量化的实用方法。我们提供了实验所用代码：https://github.com/clu5/federated-conformal。