7 citations · 10 across the 8 of their papers we have counts for
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cs.CR2025
VeriLoRA: Fine-Tuning Large Language Models with Verifiable Security via Zero-Knowledge Proofs
Guofu Liao, Taotao Wang, Shengli Zhang +3
Fine-tuning large language models (LLMs) is crucial for adapting them to specific tasks, yet it remains computationally demanding and raises concerns about correctness and privacy,…
cs.CR2025★ 1 cited
Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm
Taotao Wang, Yuxin Jin, Qing Yang +3
Federated Learning (FL) has emerged as a promising paradigm in distributed machine learning, enabling collaborative model training while preserving data privacy. However, despite i…
cs.CR2025★ 7 cited
A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning
Zhizhi Peng, Chonghe Zhao, Taotao Wang +7
Machine learning is increasingly deployed through outsourced and cloud-based pipelines, which improve accessibility but also raise concerns about computational integrity, data priv…