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

Recently emerged federated learning (FL) is an attractive distributed learning framework in
which numerous wireless end-user devices can train a global model with the data remained …

Federated Learning is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …

Federated learning (FL) has been gaining popularity as a way to provide privacy-preserving
data sharing for the Internet of Medical Things (IoMT). As a complementary, blockchain …

Blockchain-based Federated Learning (FL) technology enables vehicles to make smart
decisions, improving vehicular services and enhancing the driving experience through a …

Deep learning has gained tremendous success and great popularity in the past few years.
However, deep learning systems are suffering several inherent weaknesses, which can …

Machine learning models have been widely adopted in several fields. However, most recent
studies have shown several vulnerabilities from attacks with a potential to jeopardize the …

Federated Learning (FL) is a promising machine learning paradigm to cooperatively train a
global model with highly distributed data located on mobile devices. Aiming to optimize the …

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

Federated learning (FL) is a nascent distributed learning paradigm to train a shared global
model without violating users' privacy. FL has been shown to be vulnerable to various …