Multi-Agent Conformal Prediction with Personalized Statistical Validity

Uncertainty quantification is essential in high-stakes machine learning tasks. However, one of the principled solutions, conformal prediction, faces challenges under limited local calibration data, privacy constraints, and data heterogeneity. In multi-agent settings, existing works do not simultaneously and satisfactorily address these challenges with guarantees either limited to averages across agents or losing validity in heterogeneous settings. Hence, we propose personalized federated weighte

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ethics.ai (30 May 2026), “Multi-Agent Conformal Prediction with Personalized Statistical Validity,” evidence record 3387, https://ethics.ai/record/3387 (originally published by arXiv).

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