Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

As the volume of healthcare and medical data increases from diverse sources, real-world scenarios involving data sharing and collaboration have certain challenges, including the risk of privacy leakage, difficulty in data fusion, low reliability of data storage, low effectiveness of data sharing, etc. To guarantee the service quality of data collaboration, this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning, termed FL-HMChain. This system is composed of three layers: Data extraction and storage, data management, and data application. Focusing on healthcare and medical data, a healthcare and medical blockchain is constructed to realize data storage, transfer, processing, and access with security, real-time, reliability, and integrity. An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior, ensuring the overall reliability and trustworthiness of the collaborative model training process. Furthermore, healthcare and medical data collaboration services in realworld scenarios have been discussed and developed. To further validate the performance of FL-HMChain, a Convolutional Neural Network-based Federated Learning (FL-CNN-HMChain) model is investigated for medical image identification. This model achieves better performance compared to the baseline Convolutional Neural Network (CNN), having an average improvement of 4.7% on Area Under Curve (AUC) and 7% on Accuracy (ACC), respectively. Furthermore, the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.