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Related Experiment Videos

Federated continual learning for privacy-preserving chest radiograph classification.

Anay Sinhal1, Amit Sinhal2, Arpana Sinhal3

  • 1University of Florida, Gainesville, USA.

Scientific Reports
|June 10, 2026
PubMed
Summary

Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Federated continual learning (FCL) addresses privacy and changing data in medical AI. DP-FedEPC uses elastic weight consolidation and prototypes to maintain knowledge without raw images, improving model performance.

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Machine Learning

Background:

  • Deep learning for chest radiographs faces challenges with data privacy and evolving clinical workflows.
  • Federated learning (FL) addresses privacy by avoiding raw image pooling, but struggles with non-static data distributions.
  • Federated continual learning (FCL) tackles both issues, but existing methods often require data unsuitable for healthcare settings.

Purpose of the Study:

  • To propose a novel Federated Continual Learning (FCL) method, DP-FedEPC, designed for multi-site deep learning on chest radiographs.
  • To address privacy regulations and changing clinical workflows without relying on raw image replay or public datasets.
  • To maintain model performance and knowledge consolidation across sequential updates in a federated environment.

Main Methods:

Keywords:
Catastrophic forgettingChest radiographsDifferential privacyElastic weight consolidationFederated continual learningFederated learningPrototype rehearsalPrototype-based federated learning

Related Experiment Videos

  • DP-FedEPC combines Differentially Private Stochastic Gradient Descent (DP-SGD) on clients, Elastic Weight Consolidation (EWC), and prototype-based rehearsal.
  • EWC prevents model parameters from drifting during sequential task updates.
  • Latent prototypes, not raw images, are used to stabilize class representations across evolving tasks.

Main Results:

  • The study trained models on CheXpert and validated on MIMIC-CXR, reporting performance metrics including macro-AUROC and per-finding accuracy.
  • DP-FedEPC demonstrated effective knowledge retention, minimizing forgetting across task shifts.
  • The method achieved competitive performance while adhering to privacy constraints through client-side DP-SGD, with explicit [Formula: see text] values reported for different noise levels.

Conclusions:

  • DP-FedEPC offers a practical solution for federated continual learning in medical imaging, balancing privacy, performance, and adaptability.
  • The proposed method effectively consolidates knowledge across sequential updates without compromising patient privacy or requiring sensitive data sharing.
  • This approach facilitates the development of robust and continuously improving deep learning models for chest radiograph analysis in real-world clinical settings.