In this paper, we introduce a conformal prediction method to construct prediction sets in a oneshot federated learning setting. More specifically, we define a quantile-of-quantiles estimator and prove that for any distribution, it is possible to output prediction sets with desired coverage in only one round of communication. To mitigate privacy issues, we also describe a locally differentially private version of our estimator. Finally, over a wide range of experiments, we show that our method returns prediction sets with coverage and length very similar to those obtained in a centralized setting. Overall, these results demonstrate that our method is particularly well-suited to perform conformal predictions in a one-shot federated learning setting.

翻译：在本文中，我们提出了一种用于单次联邦学习场景中构建预测集的一致性预测方法。具体而言，我们定义了一种分位数之分的估计量，并证明对于任意分布，仅需一轮通信即可输出具有期望覆盖率的预测集。为缓解隐私问题，我们还描述了该估计量的局部差分隐私版本。最后，通过一系列广泛的实验，我们展示了该方法返回的预测集在覆盖率和长度上与集中式设置中获得的预测集高度相似。总体而言，这些结果表明，我们的方法特别适用于单次联邦学习场景中的一致性预测任务。