In this paper, we focus on federated learning (FL) over the air based on analog aggregation
transmission in realistic wireless networks. We first derive a closed-form expression for the …

Federated learning (FL) is an attractive paradigm for making use of rich distributed data
while protecting data privacy. Nonetheless, non-ideal communication links and limited …

Federated learning (FL) with over-the-air computation can efficiently utilize the
communication bandwidth but is susceptible to analog aggregation error. Excluding those …

This paper proposes an over-the-air aggregation framework for federated learning (FL) in
broadband wireless networks where not only edge devices but also a base station (BS) has …

Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that
exploits the superposition property of the wireless medium and performs model aggregation …

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, we consider federated learning (FL) over a noisy fading multiple access
channel (MAC), where an edge server aggregates the local models transmitted by multiple …

Federated learning (FL) has been recognized as a promising distributed learning paradigm
to support intelligent applications at the wireless edge. However, direct computing based on …

Federated learning (FL) can suffer from communication bottlenecks when deployed in
mobile networks, limiting participating clients and deterring FL convergence. In this context …

In this paper, we quantitatively compare these two effective communication schemes, ie,
digital and analog ones, for wireless federated learning (FL) over resource-constrained …