The conventional model aggregation-based federated learning (FL) approach requires all
local models to have the same architecture, which fails to support practical scenarios with …

The continuous convergence of machine learning algorithms, 5G and beyond (5G+)
wireless communications, and artificial intelligence (AI) hardware implementation hastens …

In this paper, the problem of energy efficient transmission and computation resource
allocation for federated learning (FL) over wireless communication networks is investigated …

It is envisioned that data-driven distributed learning approaches such as federated learning
(FL) will be a key enabler for 6G wireless networks. However, the deployment of FL over …

Federated learning (FL) has emerged as an appealing machine learning approach to deal
with massive raw data generated at multiple mobile devices, which needs to aggregate the …

This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the
superposition property of the wireless medium, and performs model aggregation over the air …

Federated learning (FL), invented by Google in 2016, has become a hot research trend.
However, enabling FL in wireless networks has to overcome the limited battery challenge of …

We examine federated learning (FL) with over-the-air (OTA) aggregation, where mobile
users (MUs) aim to reach a consensus on a global model with the help of a parameter server …

Edge machine learning involves the development of learning algorithms at the network edge
to leverage massive distributed data and computation resources. Among others, the …

We consider federated learning in a wireless edge network, where multiple power-limited
mobile devices collaboratively train a global model, using their local data with the assistance …