We study adversary-resilient stochastic distributed optimization, in which $ m $ machines
can independently compute stochastic gradients, and cooperate to jointly optimize over their …

Geometric median (GM) is a classical method in statistics for achieving robust estimation of
the uncorrupted data; under gross corruption, it achieves the optimal breakdown point of 1/2 …

Motivated by the emerging federated learning applications, we jointly consider the problems
of Byzantine-robustness and communication efficiency in distributed non-convex learning …

The emerging federated learning applications raise challenges of Byzantine-robustness and
communication efficiency in distributed non-convex learning over non-IID data. To address …

Distributed learning is an important paradigm in the current machine learning algorithms
with large datasets. In this paper, distributed stochastic optimization problem of minimizing a …

Asynchronous stochastic gradient descent (ASGD) is a computationally efficient algorithm,
which speeds up deep learning training and plays an important role in distributed deep …

We develop an eigenspace estimation algorithm for distributed environments with arbitrary
node failures, where a subset of computing nodes can return structurally valid but otherwise …

In today's highly connected world, the number of smart devices worldwide has increased
exponentially. These devices generate huge amounts of real-time data, perform complicated …

A very common optimization technique in Machine Learning is Stochastic Gradient Descent
(SGD). SGD can easily be distributed: several workers try to estimate the gradient of a loss …

This thesis is concerned with the design and analysis of computationally efficient algorithms
for large-scale optimization and scientific computing. It aims to address two primary …