most citedTowards Quantum-Safe Federated Learning via Homomorphic Encryption: Learning with Gradients

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.CR2024★ 1 cited

Towards Quantum-Safe Federated Learning via Homomorphic Encryption: Learning with Gradients

Guangfeng Yan, Shanxiang Lyu, Hanxu Hou +2

This paper introduces a privacy-preserving distributed learning framework via private-key homomorphic encryption. Thanks to the randomness of the quantization of gradients, our lea…

cs.CR2023

Lattice-Aided Extraction of Spread-Spectrum Hidden Data

Fan Yang, Shanxiang Lyu, Hao Cheng +2

This paper discusses the problem of extracting spread spectrum hidden data from the perspective of lattice decoding. Since the conventional blind extraction scheme multi-carrier it…