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Federated learning with uncertainty on the example of a medical data

This paper describes a federated learning model capable to process imprecise and missing data. Federation learning is a technique to solve the problem of data governance and privacy by training algorithms without exchanging the data itself. The performance of the proposed method is demonstrated on medical data of breast cancer cases. Results for different data loss scenarios and corresponding measures of classification quality are presented and discussed.