With the advent of the Internet of things (IoT) era, federated learning plays an important role
in breaking through traditional data barriers and effectively realizing data privacy and …

Federated learning (FL), as a type of collaborative machine learning framework, is capable
of preserving private data from mobile terminals (MTs) while training the data into useful …

Federated learning has made an important contribution to data privacy-preserving. Many
previous works are based on the assumption that the data are independently identically …

The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a
large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical …

Privacy preserving is a severe challenge in machine learning and artificial intelligence.
Recently, many works have been devoted to solving this problem by proposing various …

With the rapid development of artificial intelligence and increasing data generated by end
devices, the traditional cloud-centric data processing is gradually replaced by intelligent …

In recent years, the issue of data privacy has attracted more and more attention. Federated
learning is a practical solution to train the model while guaranteeing data privacy. It has two …

Federated learning (FL) is a distributed machine learning method that effectively protects
personal data. Many studies on federated learning assumed that all clients have consistent …

Federated learning (FL), which enables multiple distributed devices (clients) to
collaboratively train a global model without transmitting their private data, has attracted …

With the rapid development of the Internet of Things (IoT), federated learning (FL) has been
widely used to obtain insights from collected data while preserving data privacy. Differential …