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Author

Shengmin Xu

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2026

Data Poisoning Detection for Secure Federated Learning With Dropout-Resilient Aggregation

Federated learning (FL) enables multiple clients to train a global model collaboratively without sharing their raw data. However, compromised clients can launch data poisoning attacks by submitting malicious local updates and thereby degrade model accuracy. In addition, unprotected model updates may disclose sensitive...

Jia-Yin Li, Shengmin Xu, Xing-Shuo Han et al. · 0 citations
2026

Registered Policy-Based Chameleon Hash for Practical and Secure Blockchain Rewriting

Redactable blockchains support legitimate on-chain data modifications for regulatory compliance and controlled data management. Existing constructions often rely on a trusted central authority (CA), introducing a key escrow problem and a single point of failure. Decentralized extensions mitigate these issues by distrib...

Shengmin Xu, Xianxin Zhao, Xiaoguo Li et al. · 0 citations

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