mlsanityData to 14 Oct 2026

Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data

arXiv 2405.00725 ↗First posted 26 Apr 2024eess.SP cs.CR cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 52 references that are in the map. First posted on arXiv 26 Apr 2024.

Builds on

18 of 52 references.

References · 18

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6 more
  1. 2014Differential Privacy and Machine Learning: a Survey and Reviewcited by 67
  2. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  3. 2021Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AIcited by 26
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  5. 2022ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluationcited by 15
  6. 2022ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasetscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devicescited by 2
  2. 2026Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devicescited by 0
  3. 2026Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learningcited by 0