Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to
cooperatively train a deep learning model, without the need to share their local data. It is a …

To exploit massive amounts of data at mobile edge networks, federated learning (FL) has
been proposed as an attractive substitute for centralized machine learning. To improve the …

Motivated by the ever-increasing demands for massive data processing and intelligent data
analysis at the network edge, federated learning (FL), a distributed architecture for machine …

Abstract Recently, Federated Learning (FL) has been explored as a new paradigm that
preserves both data privacy and end-users knowledge while reducing latency during model …

The provision of communication services via portable and mobile devices, such as aerial
base stations, is a crucial concept to be realized in 5G/6G networks. Conventionally …

Implementing existing federated learning in massive Internet of Things (IoT) networks faces
critical challenges, such as imbalanced and statistically heterogeneous data and device …

This article proposes a novel multiagent deep reinforcement learning-based algorithm which
can realize federated learning (FL) computation with Internet-of-Underwater-Things (IoUT) …

Sensor-cloud systems (SCSs) aim to provide flexible configurable platforms for monitoring
and controlling the IoT-enabled applications. By integrating sensors, wireless networks and …

With the envision of sixth generation (6G) network technology, varied artificial intelligence
(AI) services gradually develop from the network center to the edge, which makes …

Federated learning (FL) is the collaborative machine learning (ML) technique whereby the
devices collectively train and update a shared ML model while preserving their personal …