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Traditional implementations of federated learning for preserving data privacy are unsuitable for longitudinal health data. To remedy this, we develop a federated enhanced fuzzy c-means clustering (FeFCM) algorithm that can identify groups of patients based on complex behavioral intervention responses. FeFCM calculates a global cluster model by incorporating data from multiple healthcare institutions without requiring patient observations to be shared. We evaluate FeFCM on simulated clusters as well as empirical data from four different dietary health studies in Massachusetts. Results find that FeFCM converges rapidly and achieves desirable clustering performance. As a result, FeFCM can promote pattern recognition in longitudinal health studies across hundreds of collaborating healthcare institutions while ensuring patient privacy.
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This content will become publicly available on December 17, 2025
Federated Learning for Enhanced ECG Signal Classification with Privacy Awareness
This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a high-performance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring.
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- Award ID(s):
- 2348464
- PAR ID:
- 10579459
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- ISSN:
- 2694-0604
- ISBN:
- 979-8-3503-7149-9
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Location:
- Orlando, FL, USA
- Sponsoring Org:
- National Science Foundation
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