Federated learning ... medical datasets while preserving patient privacy. This study evaluates the efficacy of three FL algorithms—FedAvg, FedProx, and FedAdam—on the ISIC2019 dermatology dataset, a ...

This project simulates a federated learning environment for image classification using ResNet architectures ... demonstrating the ResNet-50 model’s ability to classify medical images under real-world ...

Abstract: The performance of deep networks for medical image analysis is often constrained by limited medical data, which is privacy-sensitive. Federated learning (FL ... significant prostate cancer ...

However, federated optimizations often suffer from the heterogeneity of the data distribution across medical centers. In this work, we propose Federated Learning with Shared Label Distribution (FedSLD ...

This study proposes a collaborative federated learning model ... The importance of medical imaging in tumor classification lies in its ability to provide non-invasive, detailed, and reproducible ...

A multicentric, single-arm diagnostic study created a decentralized federated learning model ... utilization into other classification tasks with differing medical images.