Conformal Prediction for Federated Uncertainty Quantification Under Label Shift

• URL: http://arxiv.org/abs/2306.05131v2
• Date: Tue, 24 Oct 2023 13:16:05 GMT
• Title: Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
• Authors: Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines and Maxim Panov
• Abstract summary: Federated Learning (FL) is a machine learning framework where many clients collaboratively train models. We develop a new conformal prediction method based on quantile regression and take into account privacy constraints.
• Score: 57.54977668978613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized. Despite recent advances in FL, the uncertainty quantification topic (UQ) remains partially addressed. Among UQ methods, conformal prediction (CP) approaches provides distribution-free guarantees under minimal assumptions. We develop a new federated conformal prediction method based on quantile regression and take into account privacy constraints. This method takes advantage of importance weighting to effectively address the label shift between agents and provides theoretical guarantees for both valid coverage of the prediction sets and differential privacy. Extensive experimental studies demonstrate that this method outperforms current competitors.

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