Federated Learning is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

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 …

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 …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

Federated learning (FL) has been developed as a promising framework to leverage the
resources of edge devices, enhance customers' privacy, comply with regulations, and …

Federated Learning (FL) enables learning a shared model acrossmany clients without
violating the privacy requirements. One of the key attributes in FL is the heterogeneity that …

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) allows a server to learn a machine learning (ML) model across
multiple decentralized clients that privately store their own training data. In contrast with …

The emerging concern about data privacy and security has motivated the proposal of
federated learning, which allows nodes to only synchronize the locally-trained models …