Federated learning (FL) is a promising learning paradigm that can tackle the increasingly
prominent isolated data islands problem while keeping users' data locally with privacy and …

Abstract The rise of Federated Learning (FL) is bringing machine learning to edge
computing by utilizing data scattered across edge devices. However, the heterogeneity of …

In federated learning (FL), devices contribute to the global training by uploading their local
model updates via wireless channels. Due to limited computation and communication …

Federated Learning is a distributed machine learning approach which enables model
training without data sharing. In this paper, we propose a new federated learning algorithm …

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

In this work, hierarchical federated learning (HFL) over wireless multi-cell networks is
proposed for large-scale model training while preserving data privacy. However, the …

In this paper, we consider communication-efficient over-the-air federated learning (FL),
where multiple edge devices with non-independent and identically distributed datasets …

Federated learning (FL) is a novel machine learning setting that enables on-device
intelligence via decentralized training and federated optimization. Deep neural networks' …

Federated learning has been widely applied to enable decentralized devices, which each
have their own local data, to learn a shared model. However, learning from real-world data …

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized
federated learning (DFL). The performance of decentralized SGD is jointly influenced by …