1.7k citations · 1.8k across the 5 of their papers we have counts for
5 papers
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
Nicole Mitchell, Johannes Ballé, Zachary Charles +1
A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server. We present a comprehensive…
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…
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…
Simple Complexity Analysis of Simplified Direct Search
Jakub Konečný, Peter Richtárik
We consider the problem of unconstrained minimization of a smooth function in the derivative-free setting using. In particular, we propose and study a simplified variant of the dir…
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…