ArticleScientific reports2025
Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity.
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 24 papers, 2 of them syntheses that pooled it.
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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
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.Frontiers in digital health · 2026Pooled it
- Explainable AI in healthcare: a systematic review of XAI use cases in imaging, diagnostics, and rehabilitation.Frontiers in artificial intelligence · 2026Pooled it
- Theranostic Smart Bionanosensors for Solid Tumors: Advances, Challenges, and Future Directions.Applied biochemistry and biotechnology · 2026Review
- Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Manual federated simulation for multiple sclerosis integrating XGBoost algorithm with SHAP explanation.Scientific reports · 2026Article
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Multi-Task and Federated Learning for Breast and Lung Cancer Screening and Diagnosis: A Survey and Future Research Directions.Journal of imaging · 2026Review
- Federated Learning with Differential Privacy for Ultrasound Breast Cancer Classification: An Empirical Study.Journal of imaging · 2026Article
- Hierarchical proof of trust a Byzantine fault tolerant federated learning framework for industrial IoT applications.Scientific reports · 2026Article
- Adaptive client participation mechanism for federated learning in heterogeneous vehicular networks.Scientific reports · 2026Article
- Advances in Breast Cancer Diagnostics: From Screening to Precision Medicine.Diagnostics (Basel, Switzerland) · 2026Review
- A distributed fusion framework for breast cancer recurrence prediction using MapReduce.Scientific reports · 2026Article
- Adaptive Compressed Sensing Differential Privacy Federated Learning Based on Orbital Spatiotemporal Characteristics in Space-Air-Ground Networks.Sensors (Basel, Switzerland) · 2026Article
- Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography.Ophthalmology science · 2026Article
- Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives.The Journal of international medical research · 2026Review
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Recent advances in defending the privacy attacks of large language models for healthcare applications: a concise review.Frontiers in artificial intelligence · 2026Review
- Quantum-entangled neuro-symbolic swarm federation for privacy-preserving IoMT-driven multimodal healthcare.Scientific reports · 2025Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the digital age, privacy preservation is of paramount importance while processing health-related sensitive information. This paper explores the integration of Federated Learning (FL) and Differential Privacy (DP) for breast cancer detection, leveraging FL's decentralized architecture to enable collaborative model training across healthcare organizations without exposing raw patient data. To enhance privacy, DP injects statistical noise into the updates made by the model. This mitigates adversarial attacks and prevents data leakage. The proposed work uses the Breast Cancer Wisconsin Diagnostic dataset to address critical challenges such as data heterogeneity, privacy-accuracy trade-offs, and computational overhead. From the experimental results, FL combined with DP achieves 96.1% accuracy with a privacy budget of ε = 1.9, ensuring strong privacy preservation with minimal performance trade-offs. In comparison, the traditional non-FL model achieved 96.0% accuracy, but at the cost of requiring centralized data storage, which poses significant privacy risks. These findings validate the feasibility of privacy-preserving artificial intelligence models in real-world clinical applications, effectively balancing data protection with reliable medical predictions.
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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.