A Federated Learning Framework For Medical Data

This thesis investigates the Federation of Medical Machine Learning Models. Medical data is by its very nature sensitive - which makes the free sharing of medical information difficult. This data often contains very useful information, and in this thesis we broadly explore the possibility of utilizing data without their direct transfer. Instead, we explore the use of Federated Learning to transfer the knowledge from sensitive patient information as gradient updates and model parameters in order to better inform a variety of learning tasks. We also explore specific types of models such as graph networks which can often be a natural representation for Electronic Health Records (EHRs) collected at hospitals, and we investigate how we might use federated learning to aggregate information such as this across hospitals. Lastly we identify risks involved in a federated system, in the form of adversarial entities, and we show how we can mitigate them.