Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralised medical datasets while preserving patient privacy. This study evaluates the efficacy ...

This repository explores Federated Learning (FL) using FedAvg, FedDyn, and SCAFFOLD with ResNet-18 and ResNet-50 on CIFAR-10, Fashion-MNIST, and Lung X-Ray datasets under IID and Non-IID settings, ...

The performance of deep networks for medical image analysis is often constrained by limited medical data, which is privacy-sensitive. Federated learning (FL) alleviates the constraint by allowing ...

Federated learning (FL) enables collaboratively training a joint model for multiple medical centers, while keeping the data decentralized due to privacy concerns. However, federated optimizations ...

Explainable AI in medical imaging: an interpretable and collaborative federated learning model for brain tumor classification. ... Lv W, Liu Q, Li B. Explainable federated medical image analysis ...

A federated learning (FL) model demonstrated great promise in the binary classification of nevi and invasive melanomas while showcasing the benefits that artificial intelligence (AI) can provide ...