Recently, federated learning (FL) techniques have enabled multiple users to train machine
learning models collaboratively without data sharing. However, existing FL algorithms suffer …

Federated learning (FL) is an emerging collaborative machine learning (ML) framework that
enables training of predictive models in a distributed fashion where the communication …

Federated learning (FL) involves training a model over massive distributed devices, while
keeping the training data localized. This form of collaborative learning exposes new …

Federated Learning (FL) is a distributed learning paradigm that enables a large number of
resource-limited nodes to collaboratively train a model without data sharing. The non …

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 …

Federated learning (FL) has emerged as a promising distributed learning paradigm that
enables a large number of mobile devices to cooperatively train a model without sharing …

As a promising approach to deal with distributed data, Federated Learning (FL) achieves
major advancements in recent years. FL enables collaborative model training by exploiting …

Federated learning (FL) involves training a model over massive distributed devices, while
keeping the training data localized and private. This form of collaborative learning exposes …

Federated learning (FL) enables collaboratively training deep learning models on
decentralized data. However, there are three types of heterogeneities in FL setting bringing …

Federated Learning (FL) is a promising machine learning paradigm to cooperatively train a
global model with highly distributed data located on mobile devices. Aiming to optimize the …