1.7k citations · 1.9k across the 36 of their papers we have counts for
3 papers · 1 filter
Federated Random Reshuffling with Compression and Variance Reduction
Grigory Malinovsky, Peter Richtárik
Random Reshuffling (RR), which is a variant of Stochastic Gradient Descent (SGD) employing sampling without replacement, is an immensely popular method for training supervised mach…
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…
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Jakub Konečný, Jie Liu, Peter Richtárik +1
We propose a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent applied to the problem of minimizing a stro…