Federated Inverse Reinforcement Learning for Smart ICUs With Differential Privacy

Abstract

Clinical decision-making models have been developed to support therapeutic interventions based on medical data from either a single hospital or multiple hospitals. However, models based on multihospital data require collaboration among hospitals to integrate local data, which can result in information leakage and violate patient privacy. To address this challenge, we propose a novel approach that combines federated learning (FL) with inverse reinforcement learning (IRL) to create an efficient medical decision-making support tool while preserving patient privacy. Our approach uses an IRL algorithm with differential privacy to train a neural network-based agent on local data containing clinician trajectories, which learns a private treatment policy by observing patients’ conditions. Additionally, we integrate FL into the proposed algorithm to learn a global optimal action policy collaboratively among various smart intensive care units, overcoming data limitations at each hospital. We evaluate our approach using real-world medical data and demonstrate that it achieves superior performance in a distributed manner.

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