Hierarchical federated learning (HFL) enables distributed training of models across multiple
devices with the help of several edge servers and a cloud edge server in a privacy …

Federated learning (FL) allows model training from local data by edge devices while
preserving data privacy. However, the learning accuracy decreases due to the heterogeneity …

Modern automobiles are equipped with advanced computing capabilities, allowing them to
become powerful computing units capable of processing a large amount of data and training …

Federated Learning (FL) is a distributed machine learning approach that allows multiple
parties to collaboratively train a model without sharing raw data, thus protecting data privacy …

Federated Learning (FL) provides a privacy-preserving framework for training machine
learning models on mobile edge devices. Traditional FL algorithms, eg, FedAvg, impose a …

As a privacy-preserving paradigm of decentralized machine learning, federated learning
(FL) has become a hot spot in the field of machine learning. Existing FL approaches …

Federated learning (FL) is a distributed machine learning (ML) framework where several
clients cooperatively train an ML model by exchanging the model parameters without …

Federated Learning is a collaborative machine learning framework to train a deep learning
model without accessing clients' private data. Previous works assume one central parameter …

Recently, federated learning (FL) has received intensive research because of its ability in
preserving data privacy for scattered clients to collaboratively train machine learning …

Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices
to collaboratively train a shared model while preserving data privacy. However, intensive …