The ever-growing artificial intelligence (AI) applications have greatly reshaped our world in
many areas, eg, smart home, computer vision, natural language processing, etc. Behind …

Federated learning (FL) is a new technology that has been a hot research topic. It enables
the training of an algorithm across multiple decentralized edge devices or servers holding …

Recent medical applications are largely dominated by the application of Machine Learning
(ML) models to assist expert decisions, leading to disruptive innovations in radiology …

Distributed model training is vulnerable to byzantine system failures and adversarial
compute nodes, ie, nodes that use malicious updates to corrupt the global model stored at a …

Abstract Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner,
suffers from delays in waiting for the slowest learners (stragglers). Asynchronous methods …

Gradient coding is a technique for straggler mitigation in distributed learning. In this paper
we design novel gradient codes using tools from classical coding theory, namely, cyclic …

Federated learning enables training a global model from data located at the client nodes,
without data sharing and moving client data to a centralized server. Performance of …

This paper develops coding techniques to reduce the running time of distributed learning
tasks. It characterizes the fundamental tradeoff to compute gradients in terms of three …