Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …

Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …

With the rapid development of computer software and hardware technologies, more and
more healthcare data are becoming readily available from clinical institutions, patients …

Federated Learning (FL) framework brings privacy benefits to distributed learning systems
by allowing multiple clients to participate in a learning task under the coordination of a …

Federated learning is the process of develo** machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …

Healthcare is predominantly regarded as a crucial consideration in promoting the general
physical and mental health and well‐being of people around the world. The amount of data …

Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This
development has been further accelerated with the increasing use of machine learning (ML) …

The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity.
However, proposed approaches are based on the premise that organisations act alone …

Federated learning is an emerging privacy-preserving AI technique where clients (ie,
organizations or devices) train models locally and formulate a global model based on the …