Federated learning is a very promising machine learning paradigm where a large number of
clients cooperatively train a global model using their respective local data. In this paper, we …

In this paper, the performance optimization of federated learning (FL), when deployed over a
realistic wireless multiple-input multiple-output (MIMO) communication system with digital …

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction
model while keeping data locally. However, there are two major research challenges to …

The performance capabilities of models trained in a federated learning (FL) setting over
wireless networks can be significantly affected by the underlying properties of the …

In this paper, we propose a communication-efficient federated learning (FL) framework to
enhance the convergence rate of FL under limited uplink capacity. The core idea of our …

Motivated by the increasing computational capabilities of wireless devices, as well as
unprecedented levels of user-and device-generated data, new distributed machine learning …

We study federated learning (FL), where power-limited wireless devices utilize their local
datasets to collaboratively train a global model with the help of a remote parameter server …

The data heterogeneity across clients and the limited communication resources, eg,
bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL) …

We consider the problem of convergence time minimization for federated learning (FL)
implemented in wireless systems. In such setups, each wireless edge device transmits its …

We consider federated edge learning (FEEL) over wireless fading channels taking into
account the downlink and uplink channel latencies, and the random computation delays at …