We study federated machine learning at the wireless network edge, where limited power
wireless devices, each with its own dataset, build a joint model with the help of a remote …

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 …

Federated learning enables on-device training over distributed networks consisting of a
massive amount of modern smart devices, such as smartphones and IoT devices. However …

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 poses new statistical and systems challenges in training machine
learning models over distributed networks of devices. In this work, we show that multi-task …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
devices to collaboratively learn a model without sharing their raw data. Despite its practical …

In this work, we study the problem of federated learning (FL), where distributed users aim to
jointly train a machine learning model with the help of a parameter server (PS). In each …

Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine
learning techniques to enable machine learning training without accessing raw data on …

Federated learning (FL) enables distribution of machine learning workloads from the cloud
to resource-limited edge devices. Unfortunately, current deep networks remain not only too …

There is a growing interest in the wireless communications community to complement the
traditional model-driven design approaches with data-driven machine learning (ML)-based …