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Nov 9, 2021 · We propose VerSA, a verifiable secure aggregation protocol for cross-device federated learning. VerSA does not require any trusted setup for verification ...
VerSA enables every surviving user to verify the correctness of the aggregated model and runs on top of SA, achieving verifiability using the same lightweight ...
Mar 6, 2023 · In 2021, Hahn et al. proposed VERSA, a verifiable secure aggregation. However, in this article, we will point out a flaw in VERSA, which ...
In privacy-preserving cross-device federated learning, users train a global model on their local data and submit encrypted local models, while an untrusted ...
Federated learning (FL) allows a large number of users to collaboratively train machine learning (ML) models by sending only their local gradients to a central ...
Oct 30, 2023 · To protect the privacy of the gradient, a secure aggregation was proposed; to verify the correctness of the aggregated gradient, a verifiable ...
Jul 10, 2024 · In 2021, Hahn et al proposed VERSA, a verifiable secure aggregation. However, in this paper, we will point out a flaw in VERSA, which indicates ...
Dec 11, 2023 · Hur, “Versa: Verifiable secure aggregation for cross-device federated learning,” IEEE Transactions on Dependable and Secure Computing, vol.
Jan 1, 2023 · In privacy-preserving cross-device federated learning, users train a global model on their local data and submit encrypted local models, ...
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Mar 6, 2023 · Federated learning (FL) allows a large number of users to collaboratively train machine learning (ML) models by sending only their local ...
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