Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning

In recent years, advances in diagnosing and classifying diseases using machine learning (ML) have grown exponentially. However, due to the many privacy regulations regarding personal data, pooling together data from multiple sources and storing them in a single (centralized) location for traditional...

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Autors principals:	Raymond Jiang, Yulia Kumar, Dov Kruger
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2025-03-01
Col·lecció:	Applied Sciences
Matèries:	federated learning centralized learning collaborative machine learning fundus disease diagnosis ophthalmology
Accés en línia:	https://www.mdpi.com/2076-3417/15/6/3004
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