Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …

Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

Federated learning (FL) enables building robust and generalizable AI models by leveraging
diverse datasets from multiple collaborators without centralizing the data. We created …

Artificial intelligence (AI) models are vulnerable to information leakage of their training data,
which can be highly sensitive, for example, in medical imaging. Privacy-enhancing …

Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-
art performance in image classification and segmentation tasks, aiding disease diagnosis …

The rapid growth of artificial intelligence (AI), blockchain technology, and edge computing
services have enabled the Internet of Medical Things (IoMT) to provide various healthcare …

Background Artificial intelligence (AI) models are increasingly used in the medical domain.
However, as medical data is highly sensitive, special precautions to ensure its protection are …

Abstract Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a
reputation for not only building Machine Learning (ML) models that rely on distributed …

The amount of data generated owing to the rapid development of the Smart Internet of
Things is increasing exponentially. Traditional machine learning can no longer meet the …

As the adoption of Consumer Internet of Things (CIoT) devices surges, so do concerns about
security vulnerabilities and privacy breaches. Given their integration into daily life and data …