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Medical data is often highly sensitive in terms of data privacy and securityconcerns. Federated learning, one type of machine learning techniques, has beenstarted to use for the improvement of the privacy and security of medical data.In the federated learning, the training data is distributed across multiplemachines, and the learning process is performed in a collaborative manner.There are several privacy attacks on deep learning (DL) models to get thesensitive information by attackers. Therefore, the DL model itself should beprotected from the adversarial attack, especially for applications usingmedical data. One of the solutions for this problem is homomorphicencryption-based model protection from the adversary collaborator. This paperproposes a privacy-preserving federated learning algorithm for medical datausing homomorphic encryption. The proposed algorithm uses a secure multi-partycomputation protocol to protect the deep learning model from the adversaries.In this study, the proposed algorithm using a real-world medical dataset isevaluated in terms of the model performance.

Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case