Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …

Nowadays, smart meters are deployed in millions of residential households to gain
significant insights from fine-grained electricity consumption data. The information extracted …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart
services and innovative applications. Such a context urges a heavy usage of Machine …

In an Internet of Vehicle (IoV) system, federated learning (FL) is a new approach to process
real-time vehicle data in a distributed way, which can improve the driving experience and …