How do multiple AI systems share uncertainty estimates without sharing data?

Multi-agent machine learning struggles when agents have limited, nonuniform data and privacy constraints—standard uncertainty quantification breaks down. This work combines density ratio weighting with federated aggregation to let agents build valid confidence intervals locally while sharing only aggregated statistics. Each participant gets personalized statistical guarantees even under heterogeneity, verified on real datasets against federated baselines.