Efficient Federated Conformal Prediction with Group-Conditional Guarantee

ArXi:2603.14198v1 Announce Type: cross Deploying trustworthy AI systems requires principled uncertainty quantification. Conformal prediction (CP) is a widely used framework for constructing prediction sets with distribution-free coverage guarantees. In many practical settings, including healthcare, finance, and mobile sensing, the calibration data required for CP are distributed across multiple clients, each with its own local data distribution.