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As deep learning have been applied in a clinical context, privacy concernshave increased because of the collection and processing of a large amount ofpersonal data. Recently, federated learning (FL) has been suggested to protectpersonal privacy because it does not centralize data during the training phase.In this study, we assessed the reliability and performance of FL on benchmarkdatasets including MNIST and MIMIC-III. In addition, we attempted to verify FLon datasets that simulated a realistic clinical data distribution. Weimplemented FL that uses a client and server architecture and tested client andserver FL on modified MNIST and MIMIC-III datasets. FL delivered reliableperformance on both imbalanced and extremely skewed distributions (i.e., thedifference of the number of patients and the characteristics of patients ineach hospital). Therefore, FL can be suitable to protect privacy when appliedto medical data.

Reliability and Performance Assessment of Federated Learning on Clinical Benchmark Data