Overview of real-world applications of federated learning with NVIDIA FLARE.

Abstract

Today's challenges around global healthcare emphasize the need for large-scale collaborations between the clinical and sciesntific communities. However, regulatory constraints around data sharing and patient privacy might hinder access to data genuinely representing clinically relevant patient populations. We have developed an open-source federated learning framework, NVIDIA FLARE, to work around such restrictions while maintaining patient privacy using modern cryptographic and information-theoretic methods such as homomorphic encryption and differential privacy. In this work, we show how NVIDIA FLARE addresses clinical questions, such as predicting clinical outcomes in patients with COVID-19 and other real-world applications, including federated statistics and parameter-efficient adaptation of large language models under a collaborative setting, while allowing participants to retain governance over their data.