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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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-08-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
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| Online Access: | https://doi.org/10.1186/s13677-025-00734-z |
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| author | Nithin Melala Eshwarappa Hojjat Baghban Ching-Hsien Hsu Po-Yen Hsu Ren-Hung Hwang Mu-Yen Chen |
| author_facet | Nithin Melala Eshwarappa Hojjat Baghban Ching-Hsien Hsu Po-Yen Hsu Ren-Hung Hwang Mu-Yen Chen |
| author_sort | Nithin Melala Eshwarappa |
| collection | DOAJ |
| description | 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) method is problematic in terms of data privacy because of the constraint of transferring substantial volumes of data to a central server. In this study, we investigate edge intelligence training in a multi-institutional edge environment to address data privacy, machine learning training delay, and training data limitations at each healthcare center. We considered skin cancer image classification as our use case in light of skin cancer diagnosis and assisting health care experts. Initially, we focused on the training delays induced by communication and computational latency. The edge-average federated learning (Edge-Avg) method improved the training latency by 24% and 47% compared with the federated learning (Fed-Avg) and CL approaches, respectively. This indicates reduced processing time, which is particularly crucial in the field of medical diagnostics. In terms of accuracy, our Edge-Avg approach surpassed the Fed-Avg method by achieving a better accuracy of 93.94% compared to the traditional method in the classification of skin cancer images into benign or malignant categories. This study provides a low-latency and accurate solution to assist medical professionals in assessing the status of suspected skin cancer cases. |
| format | Article |
| id | doaj-art-15da63fe0eb14db2872cfd491f0425d9 |
| institution | Kabale University |
| issn | 2192-113X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj-art-15da63fe0eb14db2872cfd491f0425d92025-08-20T03:46:24ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2025-08-0114111310.1186/s13677-025-00734-zCommunication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computingNithin Melala Eshwarappa0Hojjat Baghban1Ching-Hsien Hsu2Po-Yen Hsu3Ren-Hung Hwang4Mu-Yen Chen5Department of Computer Science and Information Engineering, National Chung Cheng UniversityDepartment of Artificial Intelligence, Chang Gung UniversityDepartment of Computer Science and Information Engineering, National Chung Cheng UniversityDepartment of Engineering Science, National Cheng Kung UniversityDepartment of Computer Science and Information Engineering, National Chung Cheng UniversityDepartment of Engineering Science, National Cheng Kung UniversityAbstract 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) method is problematic in terms of data privacy because of the constraint of transferring substantial volumes of data to a central server. In this study, we investigate edge intelligence training in a multi-institutional edge environment to address data privacy, machine learning training delay, and training data limitations at each healthcare center. We considered skin cancer image classification as our use case in light of skin cancer diagnosis and assisting health care experts. Initially, we focused on the training delays induced by communication and computational latency. The edge-average federated learning (Edge-Avg) method improved the training latency by 24% and 47% compared with the federated learning (Fed-Avg) and CL approaches, respectively. This indicates reduced processing time, which is particularly crucial in the field of medical diagnostics. In terms of accuracy, our Edge-Avg approach surpassed the Fed-Avg method by achieving a better accuracy of 93.94% compared to the traditional method in the classification of skin cancer images into benign or malignant categories. This study provides a low-latency and accurate solution to assist medical professionals in assessing the status of suspected skin cancer cases.https://doi.org/10.1186/s13677-025-00734-zEdge intelligenceFederated learningMedical image classificationEdge computingDeep learning |
| spellingShingle | Nithin Melala Eshwarappa Hojjat Baghban Ching-Hsien Hsu Po-Yen Hsu Ren-Hung Hwang Mu-Yen Chen Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing Journal of Cloud Computing: Advances, Systems and Applications Edge intelligence Federated learning Medical image classification Edge computing Deep learning |
| title | Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing |
| title_full | Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing |
| title_fullStr | Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing |
| title_full_unstemmed | Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing |
| title_short | Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing |
| title_sort | communication efficient and privacy preserving federated learning for medical image classification in multi institutional edge computing |
| topic | Edge intelligence Federated learning Medical image classification Edge computing Deep learning |
| url | https://doi.org/10.1186/s13677-025-00734-z |
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