  Data-driven Machine Learning has emerged as a promising approach for building
accurate and robust statistical models from medical data, which is collected in
huge volumes by modern healthcare systems. Existing medical data is not fully
exploited by ML primarily because it sits in data silos and privacy concerns
restrict access to this data. However, without access to sufficient data, ML
will be prevented from reaching its full potential and, ultimately, from making
the transition from research to clinical practice. This paper considers key
factors contributing to this issue, explores how Federated Learning (FL) may
provide a solution for the future of digital health and highlights the
challenges and considerations that need to be addressed.
