The rapid growth in storage capacity and computational power of mobile devices is making it
increasingly attractive for devices to process data locally instead of risking privacy by …

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 (FL) is a popular privacy-preserving distributed training scheme, where
multiple devices collaborate to train machine learning models by uploading local model …

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

A new machine learning (ML) technique termed as federated learning (FL) aims to preserve
data at the edge devices and to only exchange ML model parameters in the learning …

Federated learning (FL) enables on-device training over distributed networks consisting of a
massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) …

Federated learning is a model-level aggregation learning paradigm, which can generate
high quality models without collecting the local private data of users. As a distributed …

Federated Learning marks a turning point in the implementation of decentralized machine
learning (especially deep learning) for wireless devices by protecting users' privacy and …

Under the organization of the base station (BS), wireless federated learning (FL) enables
collaborative model training among multiple devices. However, the BS is merely responsible …

Federated learning aims to jointly learn statistical models over massively distributed remote
devices. In this work, we propose FedDANE, an optimization method that we adapt from …