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...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	Raymond Jiang, Yulia Kumar, Dov Kruger
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2025-03-01
Schriftenreihe:	Applied Sciences
Schlagworte:	federated learning centralized learning collaborative machine learning fundus disease diagnosis ophthalmology
Online-Zugang:	https://www.mdpi.com/2076-3417/15/6/3004
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