Machine learning (ML) tasks are becoming ubiquitous in today's network applications.
Federated learning has emerged recently as a technique for training ML models at the …

Federated learning has generated significant interest, with nearly all works focused on a
“star” topology where nodes/devices are each connected to a central server. We migrate …

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized
federated learning (DFL). The performance of decentralized SGD is jointly influenced by …

In federated learning (FL), devices contribute to the global training by uploading their local
model updates via wireless channels. Due to limited computation and communication …

Federated learning enables a large amount of edge computing devices to jointly learn a
model without data sharing. As a leading algorithm in this setting, Federated Averaging …

Federated learning (FL) is an emerging paradigm to train a global deep neural network
(DNN) model by collaborative clients that store their private data locally through the …

The stringent requirements for low-latency and privacy of the emerging high-stake
applications with intelligent devices such as drones and smart vehicles make the cloud …

We study federated machine learning (ML) at the wireless edge, where power-and
bandwidth-limited wireless devices with local datasets carry out distributed stochastic …

The deployment of federated learning in a wireless network, called federated edge learning
(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model …

Federated learning (FL) is a powerful distributed machine learning framework where a
server aggregates models trained by different clients without accessing their private data …