One-Shot Federated Conformal Prediction - Inria - Institut national de recherche en sciences et technologies du numérique
Conference Papers Year : 2023

One-Shot Federated Conformal Prediction

Abstract

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.
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Dates and versions

hal-03981605 , version 1 (09-02-2023)
hal-03981605 , version 2 (13-06-2023)

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Pierre Humbert, Batiste Le Bars, Aurélien Bellet, Sylvain Arlot. One-Shot Federated Conformal Prediction. ICML 2023 - 40th International Conference on Machine Learning, Jul 2023, Honolulu (Hawai), United States. ⟨hal-03981605v2⟩
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