… , bandwidth and power) constraints, edge IoT devices may not be able or … edge devices in
a wireless network in training a shared ML model without data exchanges, federated learning (…

Most conventional Federated Learning (FL) models are using a star network topology where
all users aggregate their local models at a single server (eg, a cloud server). That causes …

… communications, including basic principles of federated learning, efficient communications
for training a federated learning model, and federated learning for intelligent wireless …

… for machine learning (ML) purposes. The easily changed behaviors of edge infrastructure
that … to collate IoT data at edge servers and gateways, where federated learning (FL) can be …

… We study federated learning (FL) at the wireless edge, where … a bandwidth-limited shared
wireless channel. At each iteration … We then establish convergence of a wireless FL algorithm …

… study federated machine learning at the wireless network edge, where limited power wireless
… We consider a bandwidth-limited fading multiple access channel (MAC) from the wireless …

… edge networks still require personal data to be shared with external parties, eg, edge … and
growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, …

… to train a learning model locally. … learning frameworks is federated learning (FL) developed
in [5]. FL is a distributed machine learning method that enables users to collaboratively learn …

… by learning accuracy level, and thus (ii) between the Federated Learning time and UE
energy consumption. We fill this gap by formulating a Federated Learning over wireless network …

… Enabling FL over wireless multi-hop networks can … However, the noisy bandwidth-limited
multi-hop wireless … over wireless edge by optimizing the multi-hop federated networking …