Federated Learning (FL) is contemporary distributed machine learning paradigm based on a collaboratively decentralized privacy-preserving technology to overcome challenges of data silos and data sensibility. It refers to multiple clients (such as mobile devices, institutions, organizations, etc.) coordinated with one or more central servers for decentralized machine learning settings. FL is appropriate for application when data are privacy-sensitive. Among wide range of possible domains where application of FL can bring additional value are medicine and health care. Each medical institution might have a lot of patient data, but it can be not enough to train their own prediction models. According to that, the combination of FL and prediction of future patients’ status but also patients’ treatment for achieving satisfactory quality of life parameters is good solutions to break down the barriers of analysis throughout different hospitals.

Concerning medical and health domains FL can be employed in different ways like: Use specific models for phenotyping analysis to obtain information concealed in health records without sharing patient-level data. Processing patients’ data exploring different privacy preserving approaches on Electronic Medical Record in federated setting without transmission of data among hospitals’ databases. To detect similar patients scattered in different hospitals without sharing patient-level information and based on obtained predictive models to support medical decisions. Models trained by FL can achieve performance level comparable to ones trained on centrally hosted datasets and can be superior to models that only see isolated single-institutional data.

In this presentation we will focus on crucial aspects of FL paradigm and present its essential characteristics especially connected to medical domains and considering several privacy preserving techniques employed within FL approaches. We will also illustrate effects and power of this recent learning paradigm presenting the vital functionalities of several characteristic medical systems.

Additionally, we will also briefly introduce some promising directions to lead future improvement in use of FL in medical and health domains.