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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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/3004 |
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| author | Raymond Jiang Yulia Kumar Dov Kruger |
| author_facet | Raymond Jiang Yulia Kumar Dov Kruger |
| author_sort | Raymond Jiang |
| collection | DOAJ |
| description | 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 ML model training are often infeasible. Federated learning (FL), a collaborative learning paradigm, can sidestep this major pitfall by creating a global ML model that is trained by aggregating model weights from individual models that are separately trained on their own data silos, therefore avoiding most data privacy concerns. This study addresses the centralized data issue with FL by applying a novel <i>DataWeightedFed</i> architectural approach for effective fundus disease diagnosis from ophthalmic images. It includes a novel method for aggregating model weights by comparing the size of each model’s data and taking a dynamically weighted average of all the model’s weights. Experimental results showed a small average 1.85% loss in accuracy when training using FL compared to centralized ML model systems, a nearly 92% improvement over the conventional 55% accuracy loss. The obtained results demonstrate that this study’s FL architecture can maximize both privacy preservation and accuracy for ML in fundus disease diagnosis and provide a secure, collaborative ML model training solution within the eye healthcare space. |
| format | Article |
| id | doaj-art-bf6ffa0c26f34f3ea0a599e7c7dce2ad |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-bf6ffa0c26f34f3ea0a599e7c7dce2ad2025-08-20T03:43:47ZengMDPI AGApplied Sciences2076-34172025-03-01156300410.3390/app15063004Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated LearningRaymond Jiang0Yulia Kumar1Dov Kruger2Department of Computer Science and Technology, Kean University, Union, NJ 07083, USADepartment of Computer Science and Technology, Kean University, Union, NJ 07083, USADepartment of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USAIn 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 ML model training are often infeasible. Federated learning (FL), a collaborative learning paradigm, can sidestep this major pitfall by creating a global ML model that is trained by aggregating model weights from individual models that are separately trained on their own data silos, therefore avoiding most data privacy concerns. This study addresses the centralized data issue with FL by applying a novel <i>DataWeightedFed</i> architectural approach for effective fundus disease diagnosis from ophthalmic images. It includes a novel method for aggregating model weights by comparing the size of each model’s data and taking a dynamically weighted average of all the model’s weights. Experimental results showed a small average 1.85% loss in accuracy when training using FL compared to centralized ML model systems, a nearly 92% improvement over the conventional 55% accuracy loss. The obtained results demonstrate that this study’s FL architecture can maximize both privacy preservation and accuracy for ML in fundus disease diagnosis and provide a secure, collaborative ML model training solution within the eye healthcare space.https://www.mdpi.com/2076-3417/15/6/3004federated learningcentralized learningcollaborative machine learningfundus disease diagnosisophthalmology |
| spellingShingle | Raymond Jiang Yulia Kumar Dov Kruger Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning Applied Sciences federated learning centralized learning collaborative machine learning fundus disease diagnosis ophthalmology |
| title | Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning |
| title_full | Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning |
| title_fullStr | Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning |
| title_full_unstemmed | Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning |
| title_short | Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning |
| title_sort | enhanced privacy preserving architecture for fundus disease diagnosis with federated learning |
| topic | federated learning centralized learning collaborative machine learning fundus disease diagnosis ophthalmology |
| url | https://www.mdpi.com/2076-3417/15/6/3004 |
| work_keys_str_mv | AT raymondjiang enhancedprivacypreservingarchitectureforfundusdiseasediagnosiswithfederatedlearning AT yuliakumar enhancedprivacypreservingarchitectureforfundusdiseasediagnosiswithfederatedlearning AT dovkruger enhancedprivacypreservingarchitectureforfundusdiseasediagnosiswithfederatedlearning |