Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …

In federated learning, heterogeneity in the clients' local datasets and computation speeds
results in large variations in the number of local updates performed by each client in each …

Federated learning is a key scenario in modern large-scale machine learning where the
data remains distributed over a large number of clients and the task is to learn a centralized …

Abstract In Federated Learning, we aim to train models across multiple computing units
(users), while users can only communicate with a common central server, without …

We propose a novel federated learning method for distributively training neural network
models, where the server orchestrates cooperation between a subset of randomly chosen …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Federated learning (FL) allows multiple clients to collectively train a high-performance
global model without sharing their private data. However, the key challenge in federated …

Federated learning (FL) is a decentralized and privacy-preserving machine learning
technique in which a group of clients collaborate with a server to learn a global model …

Federated learning is a distributed optimization paradigm that enables a large number of
resource-limited client nodes to cooperatively train a model without data sharing. Previous …

In Federated Learning, we aim to train models across multiple computing units (users), while
users can only communicate with a common central server, without exchanging their data …