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Guang-Ye Zhu

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Open access 2026

Privacy-Preserving and Byzantine-Robust Federated Learning With Mean-Constrained Secret Sharing

Federated learning faces three critical challenges in enabling cross-institutional collaboration: privacy leakage, poisoning by malicious clients, and unverifiable aggregation results. To address these issues in a unified manner, we propose PVeriFL—a federated learning framework that integrates privacy preservation, By...

Guang-Ye Zhu, Liqiang Wu, Ke-Qian Liu · 0 citations
Open access Aug 2026

TriVer: a lightweight and client-verifiable secure aggregation with dropout tolerance for federated learning

It is proved that TriVer satisfies client data privacy, aggregation correctness, and aggregation-result non-forgeability in the Random Oracle Model under ECDLP hardness, HPRF pseudorandomness, and hash collision resistance, against a fully malicious server that may collude with a subset of aggregators and clients.

Guang-Ye Zhu, Liqiang Wu, Weidong Du · 0 citations

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