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

As the scale of distributed control systems over networks increases and interactions
between different subsystems become more sophisticated, questions of the resilience of …

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 …

Forests are a vital part of the ecological system. Forest fires are a serious issue that may
cause significant loss of life and infrastructure. Forest fires may occur due to human or man …

Decentralized stochastic gradient descent (D-SGD) allows collaborative learning on
massive devices simultaneously without the control of a central server. However, existing …

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

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 …

As a promising distributed learning technology, analog aggregation-based federated
learning over the air (FLOA) provides high communication efficiency and privacy …

The sixth generation (6G) mobile communication networks are expected to intelligently
integrate into various aspects of modern digital society, including smart cities, homes, health …