  Medical data is often highly sensitive in terms of data privacy and security
concerns. Federated learning, one type of machine learning techniques, has been
started to use for the improvement of the privacy and security of medical data.
In the federated learning, the training data is distributed across multiple
machines, and the learning process is performed in a collaborative manner.
There are several privacy attacks on deep learning (DL) models to get the
sensitive information by attackers. Therefore, the DL model itself should be
protected from the adversarial attack, especially for applications using
medical data. One of the solutions for this problem is homomorphic
encryption-based model protection from the adversary collaborator. This paper
proposes a privacy-preserving federated learning algorithm for medical data
using homomorphic encryption. The proposed algorithm uses a secure multi-party
computation protocol to protect the deep learning model from the adversaries.
In this study, the proposed algorithm using a real-world medical dataset is
evaluated in terms of the model performance.
