To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

In this paper, we present the first stepsize schedule for Newton method resulting in fast
global and local convergence guarantees. In particular, we a) prove an $\mathcal O\left …

Inspired by the recent work FedNL (Safaryan et al, FedNL: Making Newton-Type Methods
Applicable to Federated Learning), we propose a new communication efficient second-order …

Federated learning (FL) is an emerging distributed machine learning paradigm that aims to
realize model training without gathering the data from data sources to a central processing …

This paper considers the problem of distributed learning (DL) in the presence of stragglers.
For this problem, DL methods based on gradient coding have been widely investigated …

Federated learning (FL) is a machine learning paradigm that targets model training without
gathering the local data dispersed over various data sources. Standard FL, which employs a …

In the recent paper FLECS (Agafonov et al, FLECS: A Federated Learning Second-Order
Framework via Compression and Sketching), the second-order framework FLECS was …

Distributed stochastic gradient descent (D-SGD) with gradient compression has become a
popular communication-efficient solution for accelerating optimization procedures in …

Machine learning assumes a pivotal role in our data-driven world. The increasing scale of
models and datasets necessitates quick and reliable algorithms for model training. This …

Distributed machine learning enables scalability and computational offloading, but requires
significant levels of communication. Consequently, communication efficiency in distributed …