One-Shot Federated Conformal Prediction

TL;DR

This paper presents a novel conformal prediction method for one-shot federated learning that guarantees coverage with minimal communication and privacy preservation, matching centralized performance.

Contribution

It introduces a quantile-of-quantiles estimator for one-round federated conformal prediction, including a locally differentially private variant, with theoretical guarantees.

Findings

Prediction sets achieve desired coverage in one communication round

Method performs comparably to centralized approaches in experiments

Privacy-preserving version maintains coverage and length

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.