Secure Federated Learning Framework for Training Deep Learning Models

Traditional system for building machine learning models has the major drawback of privacy preservation as all data is collected by central machine. Federated learning (FL) has added significant attention in the healthcare industry for its potential to improve the diagnosis and treatment of diseases while preserving the patient’s privacy. The main concern is safeguarding the security and privacy of patient data during training. A secure and privacy-preserving FL framework is being developed to address these challenges. The suggested framework combines differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) methods, secure aggregation protocols, and advanced machine learning algorithms for brain tumor analysis. The framework aims to encourage more healthcare institutions to adopt FL for data-driven initiatives, leading to enhanced patient outcomes and advancements in medical research.