Online Asynchronous Federated Conformal Prediction

We propose OAFCP, a novel online asynchronous federated conformal prediction framework for uncertainty quantification across heterogeneous clients with streaming data. Specifically, our method jointly updates model parameters and conformal quantiles as new observations arrive, producing adaptive prediction intervals for regression and prediction sets for classification without sharing raw data. Clients process their data streams at different speeds, while the server aggregates available updates at fixed intervals without waiting for all clients. The aggregation weights compensate for differences in computation speed to balance client contributions and ensure long-term aggregation fairness. On the theoretical side, we establish long-run coverage guarantees under asynchronous aggregation and, under score-distribution stability conditions, show that client-weighted instantaneous coverage converges to the nominal level. Experiments on synthetic data and six real-world datasets demonstrate the finite-sample performance of OAFCP.