One-Shot Federated Conformal Prediction - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

One-Shot Federated Conformal Prediction

Résumé

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.
Fichier principal
Vignette du fichier
one_shot_federated_conformal_prediction.pdf (5.35 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

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

Identifiants

Citer

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⟩
157 Consultations
49 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More