Blockchain Empowered Federated Learning for Medical Data Sharing Model

In medical fields, data sharing for patients can improve the collaborative diagnosis and the complexity of traditional medical treatment process. Under the condition of data supervision, federated learning breaks the restrictions between medical institutions and realizes the sharing of medical data. However, there are still some issues. For example, lack of trust among medical institutions leads to the inability to establish safe and reliable cooperation mechanisms. For another example, malicious medical institutions destroy model aggregation by sharing false parameters. In this paper, we propose a new federated learning scheme based on blockchain architecture for medical data sharing. Moreover, we propose an intelligent contract to verify the identity of participants and detect malicious participants in federated learning. The experimental results show that the proposed data sharing scheme provides a credible participation mechanism for medical data sharing based on federal learning, and provides both higher efficiency and lower energy consumption as well.