Federated Learning from FAIR data

Artificial intelligence, big data, machine learning and data science are expected to have a major impact on day-to-day health care with the first AI products already available to the medical community. But to be successful, these innovations need lots of data to be developed and validated and getting access to sufficient data is hampered by administrative, political, ethical and technical barriers. Since 2007, Maastricht University has embarked on an R&D program to build a global federated, privacy-preserving, FAIR (Knowledge Graph / RDF-based) data infrastructure called the Personal Health Train. The rationale, challenges and results of this infrastructure will be discussed.