3k citations · 7.9k across the 23 of their papers we have counts for
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Randomized Distributed Mean Estimation: Accuracy vs Communication
Jakub Konečný, Peter Richtárik
We consider the problem of estimating the arithmetic average of a finite collection of real vectors stored in a distributed fashion across several compute nodes subject to a commun…
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Jakub Konečný, H. Brendan McMahan, Daniel Ramage +1
We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an ext…
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu +3
Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients…
AIDE: Fast and Communication Efficient Distributed Optimization
Sashank J. Reddi, Jakub Konečný, Peter Richtárik +2
In this paper, we present two new communication-efficient methods for distributed minimization of an average of functions. The first algorithm is an inexact variant of the DANE alg…