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

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

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...

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Bibliographic Details
Main Authors:	Shubhi Shukla, Suraksha Rajkumar, Aditi Sinha, Mohamed Esha, Konguvel Elango, Vidhya Sampath
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-04-01
Series:	Scientific Reports
Subjects:	Federated learning Data security Privacy preservation Healthcare Decentralized machine learning Differential privacy
Online Access:	https://doi.org/10.1038/s41598-025-95858-2
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