Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized.Expand

IEEE 58th Conference on Decision and Control (CDC…

18 March 2019

TLDR

We study distributed stochastic optimization to minimize a sum of smooth and strongly-convex local cost functions over a network of agents, communicating over a strongly-connected graph.Expand

A novel algorithm MATCHA uses matching decomposition sampling of the base topology to parallelize inter-worker information exchange so as to significantly reduce communication delay.Expand

In this paper, we consider the currently understudied but highly relevant scenarios when: 1) only noisy function values' estimates are available (no gradients nor Hessians can be evaluated).Expand

A zeroth order Frank-Wolfe algorithm that converges to the optimal objective function at a rate $O(1/T^{1/3}T^{-1/4}\right)$, where $T$ denotes the iteration count.Expand

The paper presents a communication efficient distributed algorithm, <inline-formula><tex-math notation="LaTeX"> $\mathcal {CIRFE}$</tex-Math></inline- formula>, to estimate a high-dimensional parameter in a multi-agent network, in which each agent is interested in reconstructing only a few components of the parameter.Expand

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output labels (hard label) to a queried data input.Expand