Specificity-Aware Federated Learning With Dynamic Feature Fusion Network for Imbalanced Medical Image Classification
Abstract
Recently, federated learning has become a powerful technique for medical image classification due to its ability to utilize datasets from multiple clinical clients while satisfying privacy constraints. However, there are still some obstacles in federated learning. Firstly, most existing methods directly average the model parameters collected by medical clients on the server, ignoring the specificities of the local models. Secondly, class imbalance is a common issue in medical datasets. In this article, to handle these two challenges, we propose a novel specificity-aware federated learning framework that benefits from an Adaptive Aggregation Mechanism (AdapAM) and a Dynamic Feature Fusion Strategy (DFFS). Considering the specificity of each local model, we set the AdapAM on the server. The AdapAM utilizes reinforcement learning to adaptively weight and aggregate the parameters of local models based on their data distribution and performance feedback for obtaining the global model parameters. For the class imbalance in local datasets, we propose the DFFS to dynamically fuse the features of majority classes based on the imbalance ratio in the min-batch and collaborate the rest of features. We conduct extensive experiments on a dermoscopic dataset and a fundus image dataset. Experimental results show that our method can achieve state-of-the-art results in these two real-world medical applications.
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Federated learning presents a viable solution for leveraging multimodal data from multiple health care institutions without the need to share the actual data, thus maintaining patient privacy.
“In addition, federated learning presents a viable solution for leveraging multimodal data from multiple health care institutions without the need to share the actual data, thus maintaining patient privacy [152-156].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [153]
Federated learning techniques enable M-LLMs to be trained on decentralized data sources without the need to transfer sensitive or private information to a central location, thereby preserving data privacy and security.
“One of these measures is the implementation of federated learning techniques [153,155,156] to enable M-LLMs to be trained on decentralized data sources without the need to transfer sensitive or private information to a central location, thereby preserving data privacy and security.”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [153]
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- Authors
- Yue, G., Wei, P., Zhou, T., Song, Y., Zhao, C., Wang, T., & Lei, B.
- Affiliations
- Shenzhen University (CN)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2024-11
- Volume
- 28
- Issue
- 11
- Pages
- 6373-6383
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4387068110
- Cited by
- 24
- References
- 62
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 5 · 2023: 2
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, medical image classification, class imbalance, adaptive aggregation, reinforcement learning, feature fusion, model parameter aggregation, dermoscopic images, fundus images, privacy-preserving machine learning
- MeSH terms
- Machine Learning, Algorithms, Diagnostic Imaging, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Databases, Factual
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