Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Motivated by large-scale optimization problems arising in the context of machine learning,
there have been several advances in the study of asynchronous parallel and distributed …

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 (FL) is a promising framework for performing privacy-preserving,
distributed learning with a set of clients. However, the data distribution among clients often …

This paper presents the design, implementation, and evaluation of the PyTorch distributed
data parallel module. PyTorch is a widely-adopted scientific computing package used in …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

We provide a new analysis of local SGD, removing unnecessary assumptions and
elaborating on the difference between two data regimes: identical and heterogeneous. In …

Federated learning (FL) provides a communication-efficient approach to solve machine
learning problems concerning distributed data, without sending raw data to a central server …

Federated learning is a machine learning training paradigm that enables clients to jointly
train models without sharing their own localized data. However, the implementation of …

Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of
the data across different clients which gives rise to the client drift phenomenon. In fact …