Federated Learning to Revolutionize Data Privacy and Efficiency in Health Care

30 May 2023 / Article

Federated learning, a machine learning concept that allows for the training of AI models across multiple decentralized devices or servers while keeping the data localized, is transforming health care by enabling secure, collaborative analytics that enhance patient care. Despite its challenges, it is a promising solution to concerns and regulations about data privacy associated with AI usage.

Insights for What’s Ahead

Federated learning, first introduced by Google in 2017 and promising applications in health care, is emerging as the go-to method for complying with new regulations on private data management and storage.¹It offers significant advantages in health care, where data privacy is critical. Federated learning enables health care institutions to create machine learning models without sharing sensitive patient data, ensuring patient privacy and compliance with data protection regulations.

Besides health care, federated learning also has potential to improve outcomes in a wide range of business applications and industries, from autonomous vehicles to smart manufacturing and robotics. Applications of federated learning in health care range from finding clinically similar patients and predicting hospitalizations due to cardiac events to segmenting brain tumors and improving tumor boundary detection.

Insights for What’s Ahead

Federated learning, first introduced by Google in 2017 and promising applications in health care, is emerging as the go-to method for complying with new regulations on private data management and storage.¹It offers significant advantages in health care, where data privacy is critical. Federated learning enables health care institutions to create machine learning models without sharing sensitive patient data, ensuring patient privacy and compliance with data protection regulations.
Besides health care, federated learning also has potential to improve outcomes in a wide range of business applications and industries, from autonomous vehicles to smart manufacturing and robotics. Applications of federated learning in health care range from finding clinically similar patients and predicting hospitalizations due to cardiac events to segmenting brain tumors and improving tumor boundary detection.

Promising Applications in Health Care

A number of recent and ongoing projects utilize federated learning in health care. Recent initiatives include the HealthChain project, which has developed a federated learning framework across four hospitals in France to predict treatment response for breast cancer and melanoma patients.² Ongoing projects include the Federated Tumour Segmentation (FeTS) initiative, an international federation of 30 health care institutions using an open-source federated learning framework.³ There are also industrial applications, as federated learning allows competing companies to collaborate on research without revealing their proprietary data.

“Federated learning has tremendous potential across numerous domains, particularly within healthcare, as shown by our research with Penn Medicine. Its ability to protect sensitive i