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Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

Liqiang Wu

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Open access 2026

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

PVeriFL is a federated learning framework that integrates privacy preservation, Byzantine fault tolerance, and lightweight client-side verifiability and effectively unifies the three security objectives, offering a practical and trustworthy solution for real-world federated learning systems.

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, Wei-Dong Du · 0 citations

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