Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet
speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the …

Nowadays, devices are equipped with advanced sensors with higher processing and
computing capabilities. Besides, widespread Internet availability enables communication …

Abstract Recently, Federated Learning (FL) has been explored as a new paradigm that
preserves both data privacy and end-users knowledge while reducing latency during model …

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …

Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

Federated learning (FL) has been a popular method to achieve distributed machine learning
among numerous devices without sharing their data to a cloud server. FL aims to learn a …

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 …

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as
smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration …

As a promising method for central model training on decentralized device data without
compromising user privacy, federated learning (FL) is becoming more and more popular in …