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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Principais autores:	Shubhi Shukla, Suraksha Rajkumar, Aditi Sinha, Mohamed Esha, Konguvel Elango, Vidhya Sampath
Format:	Artigo
Jezik:	Inglês
Izdano:	Nature Portfolio 2025-04-01
Serija:	Scientific Reports
Teme:	Federated learning Data security Privacy preservation Healthcare Decentralized machine learning Differential privacy
Online dostop:	https://doi.org/10.1038/s41598-025-95858-2
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