A multi‐modal heterogeneous data mining algorithm using federated learning

• Published in: Journal of engineering (Stevenage, England) Vol. 2021; no. 8; pp. 458 - 466
• Main Author: Wei, Xianyong
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.08.2021; Wiley
• Subjects: Algorithms; Alzheimer's disease; Biology and medical computing; Classification; Computer vision and image processing techniques; Data handling techniques; Data integration; Data mining; Diagnosis; Disease; Federated learning; Heterogeneity; Machine learning; Medical diagnosis; Modal data; Other topics in statistics; Third party; China
• ISSN: 2051-3305; 2051-3305
• DOI: 10.1049/tje2.12049

In disease diagnosis, the classification accuracy based on multi‐modal models is usually higher than single‐modal models. However, in the process of multimodal data fusion, how to reasonably solve the heterogeneity problem and better extract the information between different modal data has attracted the attention of scholars. Federated learning is an efficient machine learning method that can expand between multiple parameters or multiple computing nodes. It has been applied successfully in the financial industry and cross‐industry cooperation. In this paper, a novel algorithm to disease diagnosis model based on federated learning is proposed. The model not only cleverly solves heterogeneous problems, but also excavates information between different modal data to promote the model to be more robust and discriminative. The experiment results show that the proposed model has better performance than traditional fusion algorithms. Notably, compared with other models, this model converges faster and requires less computation.