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Federated Learning to Revolutionize Data Privacy and Efficiency in Health Care
May 30, 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.1 It 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.
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AUTHOR
Claudio Truzzi
Senior Fellow, ESF Center, Europe
The Conference Board
Founder & AI Strategy Advisor
YouRock.ai
