The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

Federated learning (FL) is a rapidly growing research field in machine learning. However,
existing FL libraries cannot adequately support diverse algorithmic development; …

In this paper, the problem of energy efficient transmission and computation resource
allocation for federated learning (FL) over wireless communication networks is investigated …

Along with the rapid developments in communication technologies and the surge in the use
of mobile devices, a brand-new computation paradigm, edge computing, is surging in …

Internet of Things (IoT) have widely penetrated in different aspects of modern life and many
intelligent IoT services and applications are emerging. Recently, federated learning is …

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

This study develops a federated learning (FL) framework overcoming largely incremental
communication costs due to model sizes in typical frameworks without compromising model …

We consider federated edge learning (FEEL), where mobile users (MUs) collaboratively
learn a global model by sharing local updates on the model parameters rather than their …

Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …