Federated learning, an innovative artificial intelligence training method, offers a secure
solution for institutions to collaboratively develop models without sharing raw data. This …

In the evolving application of medical artificial intelligence, federated learning is notable for
its ability to protect training data privacy. Federated learning facilitates collaborative model …

Federated Learning (FL) has emerged as a powerful methodology for training centralized
models while preserving data privacy by using trained parameters from local models that are …

Critical machine learning applications (medical image guidance, task prediction, anomaly
detection) require large amounts of data that could not be sufficiently supplied from a single …

A major obstacle for cancer research is the prediction of liver cancer progression. This
research looked at models that can predict how Hepatocellular carcinoma (HCC) would …

Machine learning and deep learning have demonstrated significant promise for many kinds
of medical imaging applications, including segmentation, classification, and detection. The …

In the realm of medical imaging, leveraging large-scale datasets from various institutions is
crucial for developing precise deep learning models, yet privacy concerns frequently impede …

The rapid growth of both the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI)
results in a high demand for AI applications in devices. To achieve high levels of accuracy …

Objectives Federated learning (FL) allows multiple institutions to collaboratively develop a
machine learning algorithm without sharing their data. Organizations instead share model …

This research employs federated learning using Convolutional Neural Networks (CNN)
across multi-institutional datasets to classify the severity of lung disease. The project …