An open-source platform for supporting the development of federated learning projects
Answering many of the questions in health care often requires incorporating data that are located at different sources. Typically, data from different parties are analyzed by centralizing them. That is, data are brought to where the algorithms are. Unfortunately, this raises heavy concerns regarding the privacy and security of sensitive patient data.
Federated learning has emerged as a technological solution to address these concerns. In this novel paradigm, algorithms are brought to where the data are. This way, we can maximize the potential of multiple datasets while minimizing data leaks and privacy risks.
This led us to create vantage6, our open-source platform for supporting the development of federated learning projects.
Coronary artery disease: risk estimations and interventions for prevention and Early detection –...