AI RESEARCH
FedCF: Fair Federated Conformal Prediction
arXiv CS.LG
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ArXi:2509.22907v2 Announce Type: replace 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 (