We study federated learning (FL) with over-the-air aggregation over time-varying wireless
channels. Independent workers compute local gradients based on their local datasets and …

Federated learning (FL) over wireless communication channels, specifically, over-the-air
(OTA) model aggregation framework is considered. In OTA wireless setups, the adverse …

Federated learning (FL) can generate huge communication overhead for the central server,
which may cause operational challenges. Furthermore, the central server's failure or …

We propose a novel communication design, termed random orthogonalization, for federated
learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. The …

We consider federated edge learning among mobile devices that harvest the required
energy from their surroundings, and share their updates with the parameter server (PS) …

We study schemes and lower bounds for distributed minimax statistical estimation over a
Gaussian multiple-access channel (MAC) under squared error loss. Our framework …

In this work, we investigate federated edge learning over a fading multiple access channel.
To alleviate the communication burden between the edge devices and the access point, we …

With the growth of terminal devices and data traffic, privacy concerns have inspired an
innovative edge learning framework, called federated learning (FL). Over-the-air …

Intelligent reflecting surface (IRS) technology is highly anticipated in future smart
communication systems as it can improve spectral efficiency (SE) by adjusting signal phase …

The federated learning is a promising machine learning technique to bring about advanced
services and application for future industries. It has been known that the federated learning …