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 …

Abstract Asynchronous Federated Learning (AFL) has been introduced to improve the
efficiency of FL by reducing the latency of Machine Learning (ML) model aggregation …

Federated learning (FL) involves training machine learning models over distributed edge
nodes (ie, workers) while facing three critical challenges, edge heterogeneity, Non-IID data …

Federated learning (FL) is a promising paradigm that enables collaboratively learning a
shared model across massive clients while keeping the training data locally. However, for …

Federated Learning (FL) enables the multiple participating devices to collaboratively
contribute to a global neural network model while keeping the training data locally. Unlike …

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains
artificial intelligence (AI) models without revealing local datasets of the FL workers. While FL …

Federated Learning (FL) enables training a global model without sharing the decentralized
raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the …

Federated learning (FL) is an emerging distributed machine learning paradigm that protects
privacy and tackles the problem of isolated data islands. At present, there are two main …

Federated Learning (FL) is a novel distributed machine learning, which allows thousands of
edge devices to train models locally without uploading data to the central server. Since …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …