The advent of federated learning (FL) has sparked a new paradigm of parallel and
confidential decentralized machine learning (ML) with the potential of utilizing the …

The development of clinical trials has led to substantial improvements in the prevention and
treatment of many diseases, including brain cancer. Advances in medicine, such as …

Conventional machine learning approaches aggregate all training data in a central server,
which causes massive communication overhead of data transmission and is also vulnerable …

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving,
Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI …

Federated learning is a machine learning method that allows decentralized training of deep
neural networks among multiple clients while preserving the privacy of each client's data …

The Corona virus outbreak sped up the process of digitalizing healthcare. The ubiquity of IoT
devices in healthcare has thrust the Healthcare Internet of Things (HIoT) to the forefront as a …

Blockchain-based federated learning (BCFL) has recently gained tremendous attention
because of its advantages, such as decentralization and privacy protection of raw data …

The metaverse, which is at the stage of innovation and exploration, faces the dilemma of
data collection and the problem of private data leakage in the process of development. This …

Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the
field of ocular imaging over the last few years. Specifically, DL has been utilised to detect …

Future AI applications require performance, reliability and privacy that the existing, cloud-
dependant system architectures cannot provide. In this article, we study orchestration in the …