Federated learning (FL) is a promising approach for addressing data privacy and governance challenges in digital health. It enables collaborative machine learning without sharing raw data, allowing institutions to train models on their own data while maintaining control and privacy. FL has the potential to improve precision medicine by enabling large-scale, unbiased models that reflect diverse patient populations and are sensitive to rare diseases. However, challenges such as data heterogeneity, privacy preservation, and technical implementation must be addressed to ensure FL's success in healthcare applications. FL is particularly valuable in medical research and clinical practice, where data is often siloed and privacy concerns restrict access. By enabling collaborative learning across institutions, FL can help overcome these barriers, leading to more accurate and generalizable models. It has already been applied in areas such as electronic health records, medical imaging, and drug discovery, with promising results in improving diagnostic accuracy and accelerating research. FL offers benefits to various stakeholders, including clinicians, patients, hospitals, researchers, and healthcare providers. Clinicians can benefit from more accurate diagnostic tools, while patients can be assured that their data remains with their institutions. Hospitals and practices can maintain control over their data, and researchers can access a broader range of data for more comprehensive studies. FL also has the potential to reduce costs and improve patient outcomes by enabling more efficient and effective healthcare delivery. Despite its potential, FL requires careful consideration of technical and ethical challenges. These include ensuring data privacy, managing data heterogeneity, and maintaining model accuracy and fairness. Additionally, FL requires robust security measures to prevent data leakage and ensure the integrity of the learning process. As FL continues to evolve, it is expected to play a significant role in the future of digital health, enabling more effective and equitable healthcare solutions.Federated learning (FL) is a promising approach for addressing data privacy and governance challenges in digital health. It enables collaborative machine learning without sharing raw data, allowing institutions to train models on their own data while maintaining control and privacy. FL has the potential to improve precision medicine by enabling large-scale, unbiased models that reflect diverse patient populations and are sensitive to rare diseases. However, challenges such as data heterogeneity, privacy preservation, and technical implementation must be addressed to ensure FL's success in healthcare applications. FL is particularly valuable in medical research and clinical practice, where data is often siloed and privacy concerns restrict access. By enabling collaborative learning across institutions, FL can help overcome these barriers, leading to more accurate and generalizable models. It has already been applied in areas such as electronic health records, medical imaging, and drug discovery, with promising results in improving diagnostic accuracy and accelerating research. FL offers benefits to various stakeholders, including clinicians, patients, hospitals, researchers, and healthcare providers. Clinicians can benefit from more accurate diagnostic tools, while patients can be assured that their data remains with their institutions. Hospitals and practices can maintain control over their data, and researchers can access a broader range of data for more comprehensive studies. FL also has the potential to reduce costs and improve patient outcomes by enabling more efficient and effective healthcare delivery. Despite its potential, FL requires careful consideration of technical and ethical challenges. These include ensuring data privacy, managing data heterogeneity, and maintaining model accuracy and fairness. Additionally, FL requires robust security measures to prevent data leakage and ensure the integrity of the learning process. As FL continues to evolve, it is expected to play a significant role in the future of digital health, enabling more effective and equitable healthcare solutions.