Multi-Agent Conformal Prediction with Personalized Statistical Validity

2026-05-30

AI Summary

This work addresses the challenge of achieving valid predictive coverage in multi-agent settings where traditional conformal prediction struggles due to limited local data, privacy constraints, and data heterogeneity. The authors propose a Personalized Federated Weighted Conformal Prediction (PFWCP) framework that, for the first time, simultaneously guarantees both marginal and conditional coverage validity for each agent under a federated learning paradigm. By integrating local density ratio weighting, weighted quantile aggregation, and coverage variance adjustment, PFWCP corrects distributional shifts and preserves privacy with only a single round of communication. Theoretical analysis characterizes its statistical properties via effective sample size, and experiments demonstrate that PFWCP significantly outperforms existing baselines on both synthetic and real-world datasets, substantially improving calibration quality and coverage reliability across all agents.

Abstract

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 weighted conformal prediction (PFWCP), a framework that combines local density ratio weighting with weighted quantile aggregation to correct for heterogeneity while preserving privacy. The method yields asymptotically valid marginal and calibration-conditional coverage guarantees for each participating agent and supports protocols with one-shot communication. Theoretical analysis presents an adjustment to the coverage variance, governed by an effective sample size expression, which is necessary in the context of weighted conformal prediction, and experiments on synthetic and real datasets show improved calibration quality over state-of-the-art federated conformal baselines.

Problem

• conformal prediction
• multi-agent
• data heterogeneity
• uncertainty quantification
• privacy constraints

Innovation

• Personalized Federated Conformal Prediction
• Weighted Quantile Aggregation
• Data Heterogeneity
• Privacy-Preserving Uncertainty Quantification
• Calibration-Conditional Coverage