As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Abstract Machine learning has revolutionized every facet of human life, while also becoming
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …

Abstract One-shot Federated Learning (FL) has recently emerged as a promising approach,
which allows the central server to learn a model in a single communication round. Despite …

IoT devices generate massive amounts of biomedical data with increased digitalization and
development of the state-of-the-art automated clinical data collection systems. When …

Federated learning enables multiple parties to collaboratively learn a model without
exchanging their data. While most existing federated learning algorithms need many rounds …

In federated learning, all networked clients contribute to the model training cooperatively.
However, with model sizes increasing, even sharing the trained partial models often leads to …

Federated learning (FL) is a privacy-preserving distributed learning approach that allows
multiple parties to jointly build machine learning models without disclosing sensitive data …

Federated learning has recently been applied to recommendation systems to protect user
privacy. In federated learning settings, recommendation systems can train recommendation …

Cybersecurity breaches are common anomalies for distributed cyber-physical systems
(CPS). However, the cyber security breach classification is still a difficult problem, even …

Federated learning has recently been applied to recommendation systems to protect user
privacy. In federated learning settings, recommendation systems can train recommendation …