Federated Learning with Homomorphic Encryption for Ensuring Privacy in Medical Data

Abstract

Federated Learning (FL) is a machine learning methodology that allows remote devices to collectively train a learning system without sharing their data. FL-based methods provide enhanced secure privacy by transmitting only localized model variables, learned with local information, from dispersed devices to a centralized controller. There is a potential for a centralized server or malicious individuals to deduce or get sensitive private data by analyzing the structure and variables of regional learning networks. This study incorporates the FL process into the deep learning process of medical prototypes in an Internet of Things (IoT)-based medical facility. A Secured Medical Homomorphic Encryption Algorithm (SMHEA) is proposed in this research to ensure medical data privacy. Cryptographic primitives, such as masking and homomorphic cryptography, are used to enhance the security of local modeling. This prevents adversaries from deducing confidential health information via assaults like model restoration or modeling inversion. The primary determinant for assessing the regional modeling's contributions to the universal model during each training stage is the quality of the databases possessed by various individuals rather than the typically used metric of database dimension in deep learning. A dropout-tolerant approach is suggested, where the FL procedure would continue as long as the total amount of online customers remains over a certain level. By doing a security evaluation, it is evident that the suggested approach effectively ensures data privacy. Theoretical analysis is conducted on accuracy, computation time, and communication error. An example of clinical applications is the categorization of skin lesions using training photos from the HAM10000 medical database. The experimental findings demonstrate that the suggested system had favorable performance and privacy preservation outcomes compared to current methods.