The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …

Statistical inference and machine-learning algorithms have traditionally been developed for
data available at a single location. Unlike this centralized setting, modern data sets are …

This paper deals with distributed finite-sum optimization for learning over multiple workers in
the presence of malicious Byzantine attacks. Most resilient approaches so far combine …

Machine learning has begun to play a central role in many applications. A multitude of these
applications typically also involve datasets that are distributed across multiple computing …

Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in
collaborative and federated learning. However, many fruitful directions, such as the usage of …

This article focuses on decentralized stochastic optimization in the presence of Byzantine
attacks. During the optimization process, an unknown number of malfunctioning or malicious …

We study\emph {Byzantine collaborative learning}, where $ n $ nodes seek to collectively
learn from each others' local data. The data distribution may vary from one node to another …

Federated Learning (FL) breaks the “data island” and lets clients cooperate in training a
shared model with private data locally. And hierarchical framework is used in FL to alleviate …

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 …

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty
machines that may deviate from their prescribed algorithm because of software or hardware …