Abstract

Artificial Intelligence (AI) methods need to learn from an adequately large dataset to achieve clinical-grade accuracy and validation, which is vital in the healthcare field. However, sensitive medical data is usually fragmented, and not shared due to security and patient privacy policies. In this context, our work aims at classifying abdominal and chest radiographs by applying Federated Learning (FL) without exchanging patient data. FL framework has been implemented on distributed data across multiple clients. In the framework, a multilayer perceptron is used as a deep learning model for the classification task. FL is a novel approach in which machine learning models are built with the collaboration of multiple clients controlled by a central server or service provider. FL model ensures data privacy and security by retaining the training data decentralized. FL model provides security and privacy for patients by training individual models in distributed clients and sharing merely the model weights.