Related papers: FedCF: Fair Federated Conformal Prediction

FedCF: Fair Federated Conformal Prediction

URL: http://arxiv.org/abs/2509.22907v1
Date: Fri, 26 Sep 2025 20:35:22 GMT
Title: FedCF: Fair Federated Conformal Prediction
Authors: Anutam Srinivasan, Aditya T. Vadlamani, Amin Meghrazi, Srinivasan Parthasarathy,
Abstract summary: We extend the Conformal Fairness (CF) framework to the Federated Learning setting and discuss how we can audit a federated model for fairness.<n>We empirically validate our framework by conducting experiments on several datasets spanning multiple domains.
Score: 4.145290936792853
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models. In its standard form, CP offers probabilistic guarantees on the coverage of the true label, but it is agnostic to sensitive attributes in the dataset. Several recent works have sought to incorporate fairness into CP by ensuring conditional coverage guarantees across different subgroups. One such method is Conformal Fairness (CF). In this work, we extend the CF framework to the Federated Learning setting and discuss how we can audit a federated model for fairness by analyzing the fairness-related gaps for different demographic groups. We empirically validate our framework by conducting experiments on several datasets spanning multiple domains, fully leveraging the exchangeability assumption.

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