Federated learning (FL) has received considerable attention with the development of mobile
internet technology, which is an emerging framework to train a deep learning model from …

Federated Learning (FL) has recently attracted considerable attention in internet of things,
due to its capability of enabling mobile clients to collaboratively learn a global prediction …

With the proliferation of mobile computing and Internet of Things (IoT), massive mobile and
IoT devices are connected to the Internet. These devices are generating a huge amount of …

Federated learning (FL) is a distributed deep learning method which enables multiple
participants, such as mobile phones and IoT devices, to contribute a neural network model …

Federated learning (FL) refers to the learning paradigm that trains machine learning models
directly in the decentralized systems consisting of smart edge devices without transmitting …

While petabytes of data are generated each day by a number of independent computing
devices, only a few of them can be finally collected and used for deep learning (DL) due to …

Federated learning (FL) is a distributed deep learning method that enables multiple
participants, such as mobile and IoT devices, to contribute a neural network while their …

Federated learning (FL) represents a distributed machine learning approach that leverages
a centralized server to train models while keeping the data on edge devices isolated. FL has …

Federated learning (FL) aims to train machine learning models in the decentralized system
consisting of an enormous amount of smart edge devices. Federated averaging (FedAvg) …

Federated learning enables resource-constrained edge compute devices, such as mobile
phones and IoT devices, to learn a shared model for prediction, while keeping the training …