To bring network intelligence closer to the end devices, Open Radio Access Networks (O-
RAN) specifies a disaggregated and vendor agnostic framework of hierarchical processing …

Recently, Federated Learning (FL) has been applied in various research domains specially
because of its privacy preserving and decentralized approach of model training. However …

Federated Learning (FL) is a machine learning technique that enables multiple local clients
holding individual datasets to collaboratively train a model, without exchanging the clients' …

This paper investigates efficient distributed training of a Federated Learning (FL) model over
a wireless network of wireless devices. The communication iterations of the distributed …

As a new distributed machine learning (ML) framework for privacy protection, federated
learning (FL) enables substantial Internet of Things (IoT) devices (eg, mobile phones …

Federated Learning (FL) provides a promising solution for preserving privacy in learning
shared models on distributed devices without sharing local data on a central server …

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …

The disaggregated and hierarchical architecture of Open Radio Access Network (ORAN)
with openness paradigm promises to deliver the ever demanding 5G services. Meanwhile, it …

Major bottlenecks of large-scale Federated Learning (FL) networks are the high costs for
communication and computation. This is due to the fact that most of current FL frameworks …

Federated learning can train a model collaboratively through multiple remote clients without
sharing raw data. The challenge of federated learning is how to decrease network …