A feasibility study for federated learning in the context of classification of biomarkers from optical coherence tomography (OCT) images
Year: 2025
Type: article
Abstract: Recent advancement in machine learning and deep learning requires centralized data for training. Federated Learning (FL) is a machine learning approach that deals with collaboratively training a model while keeping the training data decentralized. The introduction of FL reduces the privacy risk (data sharing) and the cost of memory usage from the traditional centralized approach. We developed a scalable baseline FL framework based on PyTorch, incorporated in a Docker container. We distributed our training data equally to our client servers and deployed the Docker container to train our models. This research paper focuses on creating a baseline FL workflow for OCT biomarkers classification to lower the risk inherent to centralized medical data. The best prediction accuracy (macro average F1-score) obtained from the FL approach (72.5%) is closer when compared to our centralized approach (73.6%).
Source: Embedded Selforganising Systems
Authors: Sampathkumar, Arunodhayan, Kowerko, Danny
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7106430862
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