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 …

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 came into being with the increasing concern of privacy security, as
people's sensitive information is being exposed under the era of big data. It is an algorithm …

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 …

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

Federated learning is an improved version of distributed machine learning that further
offloads operations which would usually be performed by a central server. The server …

Nowadays, data privacy is an important consideration in machine learning. This paper
provides an overview of how Federated Learning can be used to improve data security and …

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 …

Federated learning is a technique that enables distributed clients to collaboratively learn a
shared machine learning model without sharing their training data. This reduces data …