Skip to yearly menu bar Skip to main content


One-Shot Federated Conformal Prediction

Pierre Humbert · Batiste Le Bars · AurĂ©lien Bellet · Sylvain Arlot

Exhibit Hall 1 #313
[ ]
[ PDF [ Poster


In this paper, we present a Conformal Prediction method that computes prediction sets in a one-shot Federated Learning (FL) setting. More specifically, we introduce a novel quantile-of-quantiles estimator and prove that for any distribution, it is possible to compute 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. These results demonstrate that our method is well-suited for one-shot Federated Learning.

Chat is not available.