Federated learning (FL) is a distributed machine learning technology for next-generation AI
systems that allows a number of workers, ie, edge devices, collaboratively learn a shared …

Most conventional Federated Learning (FL) models are using a star network topology where
all users aggregate their local models at a single server (eg, a cloud server). That causes …

Federated learning (FL) is an efficient and privacy-preserving distributed learning paradigm
that enables massive edge devices to train machine learning models collaboratively …

As a distributed learning paradigm, federated learning (FL) can be applied in mobile edge
computing (MEC) to support real-time artificial intelligence by leveraging edge computation …

Motivated by ever-increasing computational resources at edge devices and increasing
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …

Federated learning (FL) is a distributed machine learning (ML). Distributed clients train
locally and exclusively need to upload the model parameters to learn the global model …

Federated learning (FL) can be used in mobile-edge networks to train machine learning
models in a distributed manner. Recently, FL has been interpreted within a model-agnostic …

Federated learning (FL) has recently received considerable attention and is becoming a
popular machine learning (ML) framework that allows clients to train machine learning …

The explosive growth of smart devices (eg, mobile phones, vehicles, drones) with sensing,
communication, and computation capabilities gives rise to an unprecedented amount of …

To exploit massive amounts of data generated at mobile edge networks, federated learning
(FL) has been proposed as an attractive substitute for centralized machine learning (ML). By …