Protecting Patient Privacy with Encrypted Federated Learning Models

Preserving the privacy and security of medical data is of utmost importance, given its sensitive nature. To address these concerns, federated learning has emerged as a valuable approach. This technique distributes training data across multiple machines, enabling collaborative learning while protecting individual data privacy. However, deep learning models are susceptible to privacy attacks, especially in medical data applications. Safeguarding the models themselves is crucial. An effective solution involves utilizing homomorphic encryption to protect models from adversarial attacks. This paper introduces an innovative privacy-preserving federated learning algorithm for medical data, incorporating homomorphic encryption. The study assesses the algorithm's effectiveness with a real-world medical dataset, emphasizing model performance and its resilience against privacy attacks.