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...
Saved in:
| Main Authors: | Raymond Jiang, Yulia Kumar, Dov Kruger |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/6/3004 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Privacy-preserving detection and classification of diabetic retinopathy using federated learning with FedDEO optimization
by: Dasari Bhulakshmi, et al.
Published: (2024-12-01) -
Deep Learning with Transfer Learning for Automated Glaucoma Detection in Fundus Images
by: Ruxandra-Mădălina FLORESCU, et al.
Published: (2025-05-01) -
A Distributed Privacy Preserved Federated Learning Approach for Revolutionizing Pneumonia Detection in Isolated Heterogenous Data Silos
by: Shagun Sharma, et al.
Published: (2025-10-01) -
FedDL: personalized federated deep learning for enhanced detection and classification of diabetic retinopathy
by: Dasari Bhulakshmi, et al.
Published: (2024-12-01) -
FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts
by: Shurui Bai, et al.
Published: (2024-09-01)