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Related Experiment Video

Updated: Jan 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Asynchronous federated learning for web-based OCT image analysis.

Hasan Md Tusfiqur Alam1, Tim Maurer2,3, Abdulrahman Mohamed Selim1

  • 1German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany.

Journal of Medical Imaging (Bellingham, Wash.)
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

Asynchronous federated learning (FL) with FedBuff enables privacy-preserving medical image analysis, showing promise for simpler tasks despite limitations in complex scenarios. Web-based systems offer accessibility but require technological advancements.

Keywords:
asynchronous federated learningbrowser-based trainingdecentralized deep learninginteractive machine learningmedical image analysisoptical coherence tomography

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Decentralized Machine Learning

Background:

  • Centralized machine learning models face challenges with data access and expert collaboration in medical imaging.
  • Privacy concerns and data silos hinder the development of robust AI models for healthcare.
  • Decentralized approaches offer a solution for collaborative model training while preserving data privacy.

Purpose of the Study:

  • To investigate asynchronous federated learning (FL) for medical imaging tasks, focusing on data privacy and collaborative model training.
  • To evaluate the performance of the FedBuff algorithm in classifying optical coherence tomography (OCT) retina images.
  • To assess the feasibility of a browser-based system for interactive, collaborative FL in real-world medical applications.

Main Methods:

  • Exploration of the asynchronous federated learning (FL) algorithm, FedBuff, for OCT image classification.
  • Comparison of FedBuff performance against synchronous algorithms like FedAvg and centralized models.
  • Development of a browser-based proof-of-concept system to evaluate interactive FL capabilities.

Main Results:

  • FedBuff demonstrated acceptable accuracy in binary OCT classification but showed reduced performance in complex, multiclass tasks.
  • FedAvg achieved results comparable to centralized training, validating its effectiveness.
  • The browser-based prototype highlighted the potential of accessible FL systems but also exposed technical limitations in web standards for computation and communication.

Conclusions:

  • Asynchronous FL using FedBuff presents a viable, privacy-preserving method for medical image classification, especially when synchronous participation is not feasible.
  • The scalability of asynchronous FL to complex classification tasks requires further investigation.
  • Web-based FL implementations can enhance accessibility to collaborative AI tools, but current technological limitations need to be addressed.