To share the patient\textquoteright s data in the blockchain network can help to learn the accurate deep learning model for the better prediction of COVID-19 patients . However, privacy (e.g., data leakage) and security (e.g., reliability or trust of data) concerns are the main challenging task for the health care centers . To solve this challenging task, this article designs a privacy-preserving framework based on federated learning and blockchain . In the first step, we train the local model by using the capsule network for the segmentation and classification of the COVID-19 images . The segmentation aims to extract nodules and classification to train the model . In the second step, we secure the local model through the homomorphic encryption scheme . The designed scheme encrypts and decrypts the gradients for federated learning . Moreover, for the decentralization of the model, we design a blockchain-based federated learning algorithm that can aggregate the gradients and update the local model . In this way, the proposed encryption scheme achieves the data provider privacy, and blockchain guarantees the reliability of the shared data . The experiment results demonstrate the performance of the proposed scheme.