How is federated learning used in healthcare?

Federated learning is a machine learning approach that enables multiple healthcare organizations to collaboratively train a shared model without exchanging raw patient data. Instead of centralizing data, each participant trains the model locally on their own datasets and shares only model updates (e.g., gradients or weights) with a central server. These updates are aggregated to improve the global model, which is then redistributed for further training. This method addresses privacy concerns and regulatory barriers (like HIPAA or GDPR) by keeping sensitive patient data within its original institution. For example, hospitals could jointly develop a model to predict sepsis risk using patient vitals and lab results, all while maintaining data confidentiality.

A key application of federated learning in healthcare is medical imaging analysis. Hospitals often have limited datasets for rare conditions, but pooling insights via federated learning can improve diagnostic accuracy. For instance, researchers at NVIDIA demonstrated this by training a brain tumor segmentation model across 20 institutions using MRI scans without sharing images. Similarly, federated learning has been used to predict COVID-19 outcomes using electronic health records (EHRs) from multiple hospitals. Another example is drug discovery: pharmaceutical companies can collaborate on toxicity prediction models using proprietary compound data, avoiding direct data sharing. Projects like the European Union’s MELLODDY highlight this, where federated learning enabled nine firms to improve predictive models without exposing internal datasets.

From a technical perspective, developers implementing federated learning in healthcare must address challenges like communication efficiency, data heterogeneity, and security. Non-IID (non-independent and identically distributed) data across institutions—such as varying patient demographics or imaging protocols—can degrade model performance. Techniques like adaptive aggregation algorithms or data augmentation may mitigate this. Secure aggregation protocols (e.g., homomorphic encryption) and differential privacy are often added to prevent reverse-engineering sensitive information from model updates. Frameworks like TensorFlow Federated or PySyft provide tools for building these pipelines, but developers must still optimize for computational overhead and network latency. Additionally, ensuring consistent participation and aligning incentives among stakeholders remains a practical hurdle, requiring careful system design and governance policies.