Machine learning has been widely adopted in a variety of fields as a means to gain information about data and make predictions. Open issues in a lot of areas of application are data privacy and lack of a sufficient amount of training data. In healthcare for example, patient data is heavily protected under the General Data Protection Regulation (GDPR), making artificial intelligence-driven research difficult. The newly proposed federated learning approach  has already shown promising results for privacy-preserving distributed machine learning systems. It allows a number of clients to jointly train a model on a central server without the need to transfer any sensitive information, all data stays on the clients‘ computers. Instead the model itself is distributed, periodically updated by each participant, and aggregated by the server.
In this project, I am developing new methods for building powerful predictive models for healthcare using private and distributed patient data. This data ranges from physiological signals collected in the hospital and electronic health records (EHR) to health data collected continuously in daily life by wearables and smartphones.
We are currently looking for a master student interested in writing his thesis in cooperation with Philips in Hamburg. If you want to learn more about this opportunity, please feel free to call or send an email.
 McMahan, H.B., Moore, E., Ramage, D., & Arcas, B.A. (2016). Federated Learning of Deep Networks using Model Averaging. ArXiv, abs/1602.05629.