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Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data
May 3, 2024, 4:15 a.m. | Vikhyat Agrawal, Sunil Vasu Kalmady, Venkataseetharam Manoj Malipeddi, Manisimha Varma Manthena, Weijie Sun, Saiful Islam, Abram Hindle, Padma Kaul, R
cs.CR updates on arXiv.org arxiv.org
Abstract: 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 …
arxiv cs.cr cs.lg data differential privacy eess.sp federated federated learning hospital privacy scale techniques
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