FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling
Machine LearningArtificial IntelligenceMultiagent Systems
Summary
The authors address the challenge that hospitals cannot easily share patient data or collaborate on electronic health record (EHR) modeling because of privacy concerns. They propose FedEHR-Agents, a system where each hospital uses its own AI agent to learn from local data and improve over time. These agents then share their learned experiences—not raw data or model parameters—with a central server, which combines this knowledge into a global guide for all hospitals to use. Experiments show this approach helps hospitals make better predictions while keeping patient data private. The authors suggest this experience-sharing method is a useful extension of traditional federated learning.
Electronic Health Record (EHR)Large Language Model (LLM)Federated Learning (FL)Autonomous Clinical AgentsPrivacy-Preserving CollaborationModel AggregationPrompt RefinementExperience-Centric OptimizationClinical Prediction Tasks
Authors
Jun Bai, Ruilin Wang, Yue Li
Abstract
Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeling workflows. However, agents deployed at individual hospitals remain constrained by institution-specific data and modeling environments, while direct cross-hospital collaboration is restricted by the sensitivity of patient-level EHR data. Although federated learning (FL) provides a natural foundation for privacy-preserving collaboration, existing approaches remain predominantly model-centric, limiting federation to prediction models or their updates while overlooking the richer modeling experience accumulated by autonomous agents. To address this limitation, we propose FedEHR-Agents, an experience-centric federated agentic optimization framework for automated EHR modeling. Each hospital deploys an autonomous clinical EHR agent that performs data preprocessing and model development while refining local clinical modeling experience through historical memory, task-specific evaluation, and TextGrad-based prompt refinement. The federated server performs evidence-guided experience aggregation to integrate reliable and complementary modeling experience across heterogeneous hospitals and distills the aggregated experience into global meta-prompts for subsequent local refinement. Extensive experiments on real-world multi-hospital EHR benchmarks demonstrate that FedEHR-Agents consistently outperforms local and federated baselines across diverse clinical prediction tasks and remains robust across different federation scales and LLM backbones. These results establish clinical modeling experience as a promising collaborative object beyond conventional parameter-centric FL and point toward federated autonomous clinical intelligence.