In federated learning (FL), the objective of collaboratively learning a
...
We introduce a general framework for nonlinear stochastic gradient desce...
Local Stochastic Gradient Descent (SGD) with periodic model averaging
(F...
Researchers have repeatedly shown that it is possible to craft adversari...
We study the problem of generating adversarial examples in a black-box
s...
We focus on the problem of black-box adversarial attacks, where the aim ...
Understanding the models that characterize the thermal dynamics in a sma...
Federated learning aims to jointly learn statistical models over massive...
We focus on the problem of black-box adversarial attacks, where the aim ...
We study the problem of training machine learning models incrementally u...
Federated learning involves training statistical models over remote devi...
The trade-off between convergence error and communication delays in
dece...
In this paper, we study distributed stochastic optimization to minimize ...
The burgeoning field of federated learning involves training machine lea...
Real time bidding (RTB) enables demand side platforms (bidders) to scale...
This paper focuses on the problem of constrainedstochastic
optimization....
This paper presents a communication efficient distributed algorithm,
CIR...
This paper proposes Communication efficient REcursive
Distributed estima...