We study federated machine learning (ML) at the wireless edge, where power-and
bandwidth-limited wireless devices with local datasets carry out distributed stochastic …

We study scheduling of computation tasks across n workers in a large scale distributed
learning problem with the help of a master. Computation and communication delays are …

We consider distributed gradient descent in the presence of stragglers. Recent work on
gradient coding and approximate gradient coding have shown how to add redundancy in …

The development of smart vehicles and rich cloud services have led to the emergence of
vehicular edge computing. To perform the distributed computation tasks efficiently, Coded …

Distributed matrix computations over large clusters can suffer from the problem of slow or
failed worker nodes (called stragglers) which can dominate the overall job execution time …

This article aims to give a comprehensive and rigorous review of the principles and recent
development of coding for large-scale distributed machine learning (DML). With increasing …

Polynomial based methods have recently been used in several works for mitigating the
effect of stragglers (slow or failed nodes) in distributed matrix computations. For a system …

Coded computation techniques provide robustness against straggling workers in distributed
computing. However, most of the existing schemes require exact provisioning of the …

When gradient descent (GD) is scaled to many parallel workers for large-scale machine
learning applications, its per-iteration computation time is limited by straggling workers …

Coded computation is an emerging research area that leverages concepts from erasure
coding to mitigate the effect of stragglers (slow nodes) in distributed computation clusters …