Federated Learning (FL) is an approach to conduct machine learning without centralizing
training data in a single place, for reasons of privacy, confidentiality or data volume …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Machine learning model training often require data from multiple parties. However, in some
cases, data owners cannot or are not willing to share their data due to legal or privacy …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

Federated learning has recently emerged as a paradigm promising the benefits of
harnessing rich data from diverse sources to train high quality models, with the salient …

Federated learning (FL) is a computational paradigm that enables organizations to
collaborate on machine learning (ML) projects without sharing sensitive data, such as …

In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …

With growing concerns regarding data privacy and rapid increase in data volume, Federated
Learning (FL) has become an important learning paradigm. However, jointly learning a deep …

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