DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction
Abstract
Millions of patients suffer from rare diseases around the world. However, the samples of rare diseases are much smaller than those of common diseases. Hospitals are usually reluctant to share patient information for data fusion due to the sensitivity of medical data. These challenges make it difficult for traditional AI models to extract rare disease features for disease prediction. In this paper, we propose a Dynamic Federated Meta-Learning (DFML) approach to improve rare disease prediction. We design an Inaccuracy-Focused Meta-Learning (IFML) approach that dynamically adjusts the attention to different tasks according to the accuracy of base learners. Additionally, a dynamic weight-based fusion strategy is proposed to further improve federated learning, which dynamically selects clients based on the accuracy of each local model. Experiments on two public datasets show that our approach outperforms the original federated meta-learning algorithm in accuracy and speed with as few as five shots. The average prediction accuracy of the proposed model is improved by 13.28% compared with each hospital's local model.
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The proposed dynamic federated meta-learning model improves rare disease prediction accuracy by 13.28%.
“The proposed model improves the prediction accuracy by 13.28% and outperforms the original federated meta-learning algorithm in accuracy and speed.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [90]
The proposed dynamic federated meta-learning model outperforms the original federated meta-learning algorithm in accuracy and speed.
“The proposed model improves the prediction accuracy by 13.28% and outperforms the original federated meta-learning algorithm in accuracy and speed.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [90]
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- Authors
- Chen, B., Chen, T., Zeng, X., Zhang, W., Lu, Q., Hou, Z., Zhou, J., & Helal, S.
- Affiliations
- China University of Petroleum, East China (CN)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- University of Oulu (FI)
- University of Florida (US)
- ORCID
- Venue
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Published
- 2024-07
- Volume
- 21
- Issue
- 4
- Pages
- 880-889
- ISSN
- 1545-5963 (ISSN-L)
- 1557-9964
- 2374-0043
- OpenAlex
- W4318141649
- Cited by
- 31
- References
- 31
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 12 · 2023: 6
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Computational Biology and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Funders
- National Natural Science Foundation of China (62072469)
- Opening Project of the State Key Laboratory for Management and Control of Complex System, Institute of Automation
- Chinese Academy of Sciences
- China Scholarship Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- federated meta-learning, health data sharing, few-shot learning
- MeSH terms
- Machine Learning, Algorithms, Humans, Databases, Factual, Computational Biology, Rare Diseases
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