Federated Inverse Reinforcement Learning for Smart ICUs with Differential Privacy

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 multi-hospital 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 with inverse reinforcement learning to create an efficient medical decision-making support tool while preserving patient privacy. Our approach uses an inverse reinforcement learning 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 federated learning into the proposed algorithm to learn a global optimal action policy collaboratively among various smart ICUs, 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.