Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing

Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing

Abstract To effectively treat patients, health care providers must be able to detect diseases early and diagnose them accurately. Deep learning and computer vision have recently enhanced the diagnostic accuracy of skin cancer through image-classification models. However, the centralized learning (CL...

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Bibliographic Details
Main Authors: Nithin Melala Eshwarappa, Hojjat Baghban, Ching-Hsien Hsu, Po-Yen Hsu, Ren-Hung Hwang, Mu-Yen Chen
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:Journal of Cloud Computing: Advances, Systems and Applications
Subjects:
Edge intelligence
Federated learning
Medical image classification
Edge computing
Deep learning
Online Access:https://doi.org/10.1186/s13677-025-00734-z
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https://doi.org/10.1186/s13677-025-00734-z

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