ArticleScientific reports2025
Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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
23 citing papers in PubMed.
- Radiology research platform: automating multicenter radiological research workflows using a cloud-native platform.European radiology · 2026Article
- Regulating digital surgery to balance safety and innovation: a SAGES white paper.Surgical endoscopy · 2026Article
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- From Pixel Understanding to Semantic Insight: Intelligent Detection in Sensor-Driven Perception Systems.Sensors (Basel, Switzerland) · 2026Review
- The future of robotic surgery in the age of artificial intelligence.Nature reviews. Urology · 2026Review
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- FedLiverNet: a federated learning framework for privacy-preserving and efficient liver cancer detection.Scientific reports · 2026Article
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
- A privacy preserving synthetic learner dataset for learning analytics in technology enhanced higher education.Scientific reports · 2026Article
- A simulation study of differentially private federated learning with zero-trust policy screening for medical IoT devices.Frontiers in digital health · 2026Article
- AI Privacy and Security in Healthcare: A Systematic Literature Review.International journal of telerehabilitation · 2026Article
- Interpretable machine learning for severity classification of thyroid eye disease using orbital anatomical features.Frontiers in medicine · 2026Article
- AI-driven radiogenomics in gynecologic oncology: from radiological digital biopsy to a new paradigm in precision therapy.Frontiers in oncology · 2026Review
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Quantum-entangled neuro-symbolic swarm federation for privacy-preserving IoMT-driven multimodal healthcare.Scientific reports · 2025Article
- AI and innovation in clinical trials.NPJ digital medicine · 2025Article
- Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm.Scientific reports · 2025Article
- Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
- Advances in intelligent recognition and diagnosis of skin scar images: concepts, methods, challenges, and future trends.Frontiers in medicine · 2025Review
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ensuring data privacy in medical image classification is a critical challenge in healthcare, especially with the increasing reliance on AI-driven diagnostics. In fact, over 30% of healthcare organizations globally have experienced a data breach in the last year, highlighting the need for secure solutions. This study investigates the integration of transfer learning and federated learning for privacy-preserving medical image classification using GoogLeNet and VGG16 as baseline models to evaluate the generalizability of the proposed framework. Pre-trained on ImageNet and fine-tuned on three specialized medical datasets for TB chest X-rays, brain tumor MRI scans, and diabetic retinopathy images, these models achieved high classification accuracy across various aggregation methods. Additionally, the proposed dynamic aggregation method was further analyzed using modern architectures, EfficientNetV2 and ResNet-RS, to assess the scalability and robustness of the model. A key contribution is the introduction of a novel adaptive aggregation method, which dynamically alternates between Federated Averaging (FedAvg) and Federated Stochastic Gradient Descent (FedSGD), based on data divergence during communication rounds. This approach optimizes model convergence while preserving privacy in collaborative settings. The results demonstrate that transfer learning, when combined with federated learning, offers a scalable, robust, and secure solution for real-world medical diagnostics, enabling healthcare institutions to train highly accurate models without compromising sensitive patient data.
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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 Socratic graph.