The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …

The Internet of Things (IoT) has paved the way to a highly connected society where all things
are interconnected and exchanging information has become more accessible through the …

Deep neural networks have shown the ability to extract universal feature representations
from data such as images and text that have been useful for a variety of learning tasks …

We introduce ProxSkip—a surprisingly simple and provably efficient method for minimizing
the sum of a smooth ($ f $) and an expensive nonsmooth proximable ($\psi $) function. The …

Standard federated optimization methods successfully apply to stochastic problems with
single-level structure. However, many contemporary ML problems-including adversarial …

Abstract The Federated Averaging (FedAvg) algorithm, which consists of alternating
between a few local stochastic gradient updates at client nodes, followed by a model …

The federated learning (FL) framework enables edge clients to collaboratively learn a
shared inference model while keeping privacy of training data on clients. Recently, many …

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users'
local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless …

To enable large-scale machine learning in bandwidth-hungry environments such as
wireless networks, significant progress has been made recently in designing communication …

We study distributed optimization methods based on the {\em local training (LT)} paradigm,
ie, methods which achieve communication efficiency by performing richer local gradient …