Oct 23, 2023 · We introduce a Robust Federated Backdoor Defense Scheme (RFBDS) and Privacy-preserving RFBDS (PrivRFBDS) to ensure the elimination of adversarial backdoors.
Federated learning (FL) allows multiple clients to train deep learning models collaboratively while protecting sensitive local datasets.
Abstract—Federated learning (FL) allows multiple clients to train deep learning models collaboratively while protecting sen- sitive local datasets.
Jan 28, 2024 · Privacy Enhancing and Robust Backdoor Defense for Federated Learning on Heterogeneous Data https://xoomprojects.com/ IEEE PROJECTS 2024 ...
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What is federated learning privacy?
Federated learning (FL) enables multiple clients to collaboratively train deep learning models while considering sensitive local datasets' privacy. However, ...
In this paper, we investigate typical backdoor attacks in FL, containing model replacement attack and adaptive backdoor attack.
May 15, 2024 · Abstract—This paper proposes a post-training defense against pattern-triggered backdoor attacks in federated learning con-.
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Federated learning (FL) allows multiple clients to train deep learning models collaboratively while protecting sensitive local datasets. However, FL has been ...
Apr 26, 2024 · We propose a Full Combination Backdoor Attack (FCBA) method. It aggregates more combined trigger information for a more complete backdoor pattern in the global ...
Privacy-Enhancing and Robust Backdoor Defense for Federated Learning on Heterogeneous Data Zekai Chen, Shengxing Yu, Mingyuan Fan, Ximeng Liu, Robert H.