  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.
