Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

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 distributed machine learning paradigm that ensures data do not
leave local devices. Data sharing problems can be addressed by FL in untrusted …

Over-the-air federated learning (FL) is a promising privacy-preserving edge artificial
intelligence paradigm, where over-the-air computation enables spectral-efficient model …

With the development of human–machine interactions, users are increasingly evolving
toward an immersion experience with multidimensional stimuli. Facing this trend, cross …

The practical deployment of federated learning (FL) over wireless networks requires
balancing energy efficiency, convergence rate, and a target accuracy due to the limited …

Deploying federated learning (FL) over wireless networks with resource-constrained devices
requires balancing between accuracy, energy efficiency, and precision. Prior art on FL often …

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost
the communication efficiency by only transmitting the semantics of data. However, ESC is …

This paper considers improving wireless communication and computation efficiency in
federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge …