Federated learning (FL) is an emerging learning paradigm to tackle massively distributed
data. In Federated Learning, a set of clients jointly perform a machine learning task under …

Federated learning (FL) is an emerging learning paradigm where a set of distributed clients
learns a task under the coordination of a server. The FedAvg algorithm is one of the most …

Federated learning is a paradigm of distributed machine learning in which multiple clients
coordinate with a central server to learn a model, without sharing their own training data …

Federated learning has attracted increasing attention with the emergence of distributed data.
While extensive federated learning algorithms have been proposed for the non-convex …

Federated learning (FL) is an emerging distributed training paradigm that aims to learn a
common global model without exchanging or transferring the data that are stored locally at …

Abstract Federated Averaging (FedAvg) has emerged as the algorithm of choice for
federated learning due to its simplicity and low communication cost. However, in spite of …

Federated learning is an emerging distributed machine learning method, enables a large
number of clients to train a model without exchanging their local data. The time cost of …

The federated learning (FL) framework trains a machine learning model using decentralized
data stored at edge client devices by periodically aggregating locally trained models …

Federated learning is a distributed machine learning paradigm in which a large number of
clients coordinate with a central server to learn a model without sharing their own training …

Federated Averaging (FedAvg, also known as Local-SGD)(McMahan et al., 2017) is a
classical federated learning algorithm in which clients run multiple local SGD steps before …