Blockchain-Based Trusted Federated Learning with Pre-Trained Models for COVID-19 Detection

• Main Authors: Bian, G. (Author), Qu, W. (Author), Shao, B. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: blockchain; COVID-19; federated learning (FL); fully homomorphic encryption; pre-trained models (PTMs)
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 20799292 (ISSN)
• DOI: 10.3390/electronics12092068

COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection. © 2023 by the authors.