| Federated Learning enables privacy-preserving model training across distributed clients, but heterogeneous client distributions for Non-IID data often lead to unreliable predictions without uncertainty guarantees. Conformal Prediction provides distribution-free coverage guarantees under exchangeability, an assumption violated in heterogeneous federated environments. We propose FedCP, a federated conformal prediction framework that provides reliable uncertainty quantification across client heterogeneity. Each client performs local calibration using Inductive Conformal Prediction with likelihood-ratio-weighted nonconformity scores to correct distribution shift between local and global data. The server aggregates client statistics through a weighted quantile rule to obtain a global prediction threshold. We show theoretically that FedCP achieves coverage of at least 1 − α, with degradation bounded by the Earth Mover’s Distance between client and population distributions. Experiments across multiple datasets and varying Non-IID levels demonstrate that FedCP maintains near-nominal coverage (≥ 90%) while producing compact prediction sets. The proposed approach builds on established conformal prediction principles while addressing the non-trivial challenges introduced by heterogeneous federated data distributions. |
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