ArticlePloS one2024
Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Artificial Intelligence For 6P Medicine: Consolidating AI Needs of Predictive, Preventive, Personalized, Participatory, Precision, and Public Health Trajectories.Journal of medical systems · 2026Review
- Federated lung nodule segmentation using a hybrid transformer-U-Net architecture.Scientific reports · 2026Article
- Improving Late-Life Depression Analysis with Collaborative Domain Adaptation: Learning from Heterogeneous Structural MRI.Reconstruction and imaging motion estimation, and graphs in biomedical image analysis : first international workshop, RIME 2025 and 7th international workshop, GRAIL 2025, Daejeon, South Korea, September 27, 2025 : proceedings. RIME (Wo... · 2026Article
- FedDA-TSformer: Federated Domain Adaptation with vision TimeSformer for left ventricle segmentation on gated myocardial perfusion SPECT image.BMC methods · 2026Article
- Review
- Artificial intelligence in traumatic brain injury: Brain imaging analysis and outcome prediction: A mini review.World journal of critical care medicine · 2025Review
- A novel federated learning framework for medical imaging: Resource-efficient approach combining PCA with early stopping.Medical physics · 2025Article
- Preserving Informative Presence: How Missing Data and Imputation Strategies Affect the Performance of an AI-Based Early Warning Score.Journal of clinical medicine · 2025Article
- Data Harmonization with StyleTransfer-GANs: Enhancing Non-Invasive IDH Classification in Brain Tumors.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos.PloS one · 2025Article
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
Corrections and comments
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Authors and funding
3 authors.
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Abstract
In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.
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Registered trials
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