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 …
G Xia, J Chen, C Yu, J Ma - IEEE Access, 2023 - ieeexplore.ieee.org
Federated learning faces many security and privacy issues. Among them, poisoning attacks can significantly impact global models, and malicious attackers can prevent global models …
Z Zhang, Q Liu, Z Wang, Z Lu… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
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 …
W Huang, Y Liu, M Ye, J Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
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 …
C Yin, Q Zeng - IEEE Transactions on Computational Social …, 2023 - ieeexplore.ieee.org
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 …
R Du, X Li, D He, KKR Choo - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
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 …
Y Yang, Q Li, J Jia, Y Hong, B Wang - arXiv preprint arXiv:2407.08935, 2024 - arxiv.org
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 …