Federated medical data - how much can deep learning models benefit? [Poster]
- Many machine learning algorithms, like supervised Deep Learning, assume that Training Data are available in a single database. Federated Learning trains a model at each client locally, aggregates and share only the model, not the (patient-) data. Using the TensorFlow Federated Framework and data from the MIT-BIH Electrocardiogram database, we simulate two scenarios of an arrhythmia classifier (hospital and smartwatches as clients in a federated learning domain). The model quality is measured via the F1 score on a validation data set. We define the metrics Privacy Costs and Federated Benefit to evaluate the benefit of Federated Medical Data for the Deep Learning Models.