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

Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …

Federated learning faces many security and privacy issues. Among them, poisoning attacks
can significantly impact global models, and malicious attackers can prevent global models …

Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks,
where attackers embed hidden backdoors in the DNN model by injecting a few poisoned …

Prevalent federated learning commonly develops under the assumption that the ideal global
class distributions are balanced. In contrast, real-world data typically follows the long-tailed …

Federated Learning (FL) is a promising distributed learning approach that enables multiple
clients to collaboratively train a shared global model. However, recent studies show that FL …

Federated recommendation is a prominent use case within federated learning, yet it remains
susceptible to various attacks, from user to server-side vulnerabilities. Poisoning attacks are …

Federated learning (FL) is an emerging paradigm that allows participants to collaboratively
train deep learning tasks while protecting the privacy of their local data. However, the …

Reducing computation cost and ensuring update integrity, are key challenges in federated
learning (FL). In this paper, we present a secure and verifiable hybrid FL system for training …

Federated graph learning (FedGL) is an emerging federated learning (FL) framework that
extends FL to learn graph data from diverse sources. FL for non-graph data has shown to be …