A federated learning framework for ethical dynamic treatment allocation across heterogeneous hospitals.

Abstract

In this paper, we propose an adaptive federated learning framework to learn optimal treatments for individual hospitals that possibly serve different patient populations. The proposed framework can enable the design of more efficient treatment allocation problems. We propose a federated treatment recommendation strategy that for each hospital is formulated as a Multi-Armed Bandit (MAB) problem. The process is coordinated by a lead hospital that adaptively learns and transfers Upper Confidence Bounds (UCB) across similar hospitals and Personalized Upper Bounds across heterogeneous hospitals. We test our proposed method on a simulated clinical trial environment created using real Covid-19 data from the Duke University Health System. Our method relies on collaboration among hospitals, which allows for fewer data samples needed per institution, while protecting the privacy of the individual patient data. At the same time, it ensures fairness of the learned treatments by mitigating possible biases due to differences in the patient populations treated across different hospitals. Finally, our method improves the safety of the learning procedure by reducing the number of patients administered with sub-optimal treatments at each hospital. In the experiments, we show that our proposed method outperforms other state of the art approaches in that it requires up to 36%-75% fewer patient data to learn the optimal treatment for each hospital and administers the optimal treatment to 0.95%-48.6% more patients. In this paper, we propose an adaptive federated learning strategy for treatment recommendation tasks, that learns optimal treatments for individual hospitals that possibly serve different patient populations, while satisfying privacy, fairness, and safety considerations.