Federated learning (FL) over resource-constrained wireless networks has recently attracted
much attention. However, most existing studies consider one FL task in single-cell wireless …

Due to the dynamics of wireless channels and limited wireless resources (ie, spectrum),
deploying federated learning (FL) over wireless networks is challenged by frequent FL …

In this paper, we consider communication-efficient over-the-air federated learning (FL),
where multiple edge devices with non-independent and identically distributed datasets …

In this paper, the convergence time of federated learning (FL), when deployed over a
realistic wireless network, is studied. In particular, with the considered model, wireless users …

Federated learning (FL) is a promising technique that enables many edge devices to train a
machine learning model collaboratively in wireless networks. By exploiting the superposition …

In this paper, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In particular, in the considered model, wireless users perform an …

Implementing federated learning (FL) algorithms in wireless networks has garnered a wide
range of attention. However, few works have considered the impact of user mobility on the …

Federated learning (FL) is promising in enabling large-scale model training by massive
devices without exposing their local datasets. However, due to limited wireless resources …

In this work, hierarchical federated learning (HFL) over wireless multi-cell networks is
proposed for large-scale model training while preserving data privacy. However, the …

The conventional federated learning (FL) framework usually assumes synchronous
reception and fusion of all the local models at the central aggregator and synchronous …