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Medical data is, due to its nature, often susceptible to data privacy and security concerns. The identity of a person can be derived from medical data. Federated learning, one type of machine learning technique, is popularly used to improve the privacy and security of medical data. In federated learning, the training data is distributed across multiple machines, and the learning process of deep learning (DL) models is performed collaboratively. However, the privacy of DL models is not protected. Privacy attacks on the DL models aim to obtain sensitive information. Therefore, the DL models should be protected from adversarial attacks, especially those which utilize medical data. One of the solutions to solve this problem is homomorphic encryption-based model protection. This paper proposes a privacy-preserving federated learning algorithm for medical data using homomorphic encryption. The proposed algorithm uses a Secure Multiparty Computation (SMPC) protocol to protect the deep learning model from adversaries. In this study, the proposed algorithm using a real-world medical dataset is evaluated in terms of the model performance.