In this paper, we develop distributed optimization algorithms that are provably robust against
Byzantine failures—arbitrary and potentially adversarial behavior, in distributed computing …

Secure federated learning is a privacy-preserving framework to improve machine learning
models by training over large volumes of data collected by mobile users. This is achieved …

We study the resilience to Byzantine failures of distributed implementations of Stochastic
Gradient Descent (SGD). So far, distributed machine learning frameworks have largely …

We consider the distributed statistical learning problem over decentralized systems that are
prone to adversarial attacks. This setup arises in many practical applications, including …

We study a recently proposed large-scale distributed learning paradigm, namely Federated
Learning, where the worker machines are end users' own devices. Statistical and …

With the onset of the big data era, designing efficient and effective machine learning
algorithms to analyze large-scale data is in dire need. In practice, data is typically generated …

Federated learning (FL) enables clients to collaborate with a server to train a machine
learning model. To ensure privacy, the server performs secure aggregation of updates from …

Distributed machine learning algorithms enable learning of models from datasets that are
distributed over a network without gathering the data at a centralized location. While efficient …

We introduce the concept of “coded computing”, a novel computing paradigm that utilizes
coding theory to effectively inject and leverage data/computation redundancy to mitigate …

Due to the growth of modern dataset size and the desire to harness computing power of
multiple machines, there is a recent surge of interest in the design of distributed machine …