ArticleScientific reports2024
A comparative study of federated learning methods for COVID-19 detection.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- Federated MobileNetV2 with ensemble meta-learning for privacy-preserving brain tumor classification.Scientific reports · 2026Article
- Review
- Stochastic Poisson-embedded privacy framework for federated learning with secure homomorphic encryption in medical AI.Scientific reports · 2026Article
- Health-FedNet: secure federated learning for chronic disease prediction on MIMIC-III with differential privacy and homomorphic encryption.Scientific reports · 2026Article
- Sensitivity-Aware Differential Privacy for Federated Medical Imaging.Sensors (Basel, Switzerland) · 2025Article
- Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings.Scientific reports · 2025Article
- Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis.NPJ digital medicine · 2025Article
- Addressing Generalizability in Clinical Named Entity Recognition: Federated Learning or Large Language Models?: A Case Study on Visual Acuity Extraction from US and UK Eye Institutes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Deep learning has proven to be highly effective in diagnosing COVID-19; however, its efficacy is contingent upon the availability of extensive data for model training. The data sharing among hospitals, which is crucial for training robust models, is often restricted by privacy regulations. Federated learning (FL) emerges as a solution by enabling model training across multiple hospitals while preserving data privacy. However, the deployment of FL can be resource-intensive, necessitating efficient utilization of computational and network resources. In this study, we evaluate the performance and resource efficiency of five FL algorithms in the context of COVID-19 detection using Convolutional Neural Networks (CNNs) in a decentralized setting. The evaluation involves varying the number of participating entities, the number of federated rounds, and the selection algorithms. Our findings indicate that the Cyclic Weight Transfer algorithm exhibits superior performance, particularly when the number of participating hospitals is limited. These insights hold practical implications for the deployment of FL algorithms in COVID-19 detection and broader medical image analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.