Federated learning (FL) has emerged to leverage datasets from multiple devices to improve
the performance of a machine learning (ML) model while providing privacy preservation for …

Federated learning (FL) has evolved to leverage a distributed dataset from numerous IoT
devices to improve the performance of a Machine Learning (ML) model while preserving the …

Aiming at protecting device data privacy, Federated Learning (FL) is a framework of
distributed machine learning in which devices' local model parameters are exchanged with …

Federated learning (FL) offers a promising approach to efficient machine learning with
privacy protection in distributed environments, such as Internet of Things (IoT) and mobile …

Abstract Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a
reputation for not only building Machine Learning (ML) models that rely on distributed …

While cross-device federated learning offers a privacy-preserving data processing approach
for Internet of Things (IoT) devices, it introduces fresh privacy risks and elevated …

Federated Learning (FL) is a distributed machine learning system designed to effectively
address potential data privacy concerns, making it particularly promising for the Internet of …

Federated learning (FL) is seen as a road toward privacy-preserving distributed artificial
intelligence while keeping raw training data on local devices. By leveraging blockchain, this …

Federated learning has gained popularity as it enables collaborative training without sharing
local data. Despite its advantages, federated learning requires sharing the model …

Federated learning (FL) has been envisioned to enable many Internet of Things (IoT)
devices to perform large-scale machine learning without sharing raw data, resulting in …