Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 6, 2025

Язык: Английский

---

Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 16, 2025

Abstract In the digital age, privacy preservation is of paramount importance while processing health-related sensitive information. This paper explores integration 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 updates made by model. mitigates adversarial attacks prevents data leakage. The proposed work uses Breast Cancer Wisconsin Diagnostic dataset address critical challenges such as heterogeneity, privacy-accuracy trade-offs, computational overhead. From experimental results, FL combined with achieves 96.1% accuracy a budget ε = 1.9, ensuring strong minimal performance trade-offs. comparison, traditional non-FL achieved 96.0% accuracy, but at cost requiring centralized storage, which poses significant risks. These findings validate feasibility privacy-preserving artificial intelligence models in real-world clinical applications, effectively balancing protection reliable medical predictions.

Язык: Английский

---

Enhancing healthcare collaboration through blockchain-enabled digital twin integration

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 315 - 326

Опубликована: Янв. 1, 2025

Язык: Английский

---