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

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 …

Investigation of the degree of personalization in federated learning algorithms has shown
that only maximizing the performance of the global model will confine the capacity of the …

We propose a new optimization formulation for training federated learning models. The
standard formulation has the form of an empirical risk minimization problem constructed to …

An oft-cited challenge of federated learning is the presence of heterogeneity.\emph {Data
heterogeneity} refers to the fact that data from different clients may follow very different …

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

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 …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

When training machine learning models using stochastic gradient descent (SGD) with a
large number of nodes or massive edge devices, the communication cost of synchronizing …

In this paper, we study communication efficient distributed algorithms for distributionally
robust federated learning via periodic averaging with adaptive sampling. In contrast to …