  This research paper explores ways to apply Federated Learning (FL) and
Differential Privacy (DP) techniques to population-scale Electrocardiogram
(ECG) data. The study learns a multi-label ECG classification model using FL
and DP based on 1,565,849 ECG tracings from 7 hospitals in Alberta, Canada. The
FL approach allowed collaborative model training without sharing raw data
between hospitals while building robust ECG classification models for
diagnosing various cardiac conditions. These accurate ECG classification models
can facilitate the diagnoses while preserving patient confidentiality using FL
and DP techniques. Our results show that the performance achieved using our
implementation of the FL approach is comparable to that of the pooled approach,
where the model is trained over the aggregating data from all hospitals.
Furthermore, our findings suggest that hospitals with limited ECGs for training
can benefit from adopting the FL model compared to single-site training. In
addition, this study showcases the trade-off between model performance and data
privacy by employing DP during model training. Our code is available at
https://github.com/vikhyatt/Hospital-FL-DP.
