Federated learning (FL) enables devices to update a global model while keeping the
training data local, so that data privacy is protected. However, the local data of devices is …

Driven by emerging technologies such as edge computing and Internet of Things (IoT),
recent years have witnessed the increasing growth of data processing in a distributed way …

Artificial intelligence and robotics are advancing at an incredible pace; however, there is a
risk associated with the data privacy and personal information of users interacting with these …

Industry 5.0 provides resource-efficient solutions compared to Industry 4.0. Edge Computing
(EC) allows data analysis on edge devices. Artificial intelligence (AI) has become the focus …

Federated learning (FL) has attracted increasing attention as a promising technique to drive
a vast number of edge devices with artificial intelligence. However, it is very challenging to …

Federated learning (FL) is the up-to-date approach for privacy constraints Internet of Things
(IoT) applications in next-generation mobile network (NGMN), 5 th generation (5G), and 6 th …

The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …

Federated learning (FL) supports training models on geographically distributed devices.
However, traditional FL systems adopt a centralized synchronous strategy, putting high …

With the widespread of 5G networks, the application of Federated Learning (FL) in Internet of
Things (IoT) has become a trend. However, the trust problem caused by the centralized …

Federated learning is an emerging distributed machine learning paradigm that can break
through data silos and make use of data from different clients in a secure way. However, for …