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Guangdong Bai

4 papers hereh-index 553 citations12 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CL1
  • cs.CR1
  • cs.LG1
same name
  • Guangdong Bai — 8 papers, h 22
  • Guangdong Bai — 5 papers, h 6
  • Guangdong Bai — 4 papers, h 6
  • Guangdong Bai — 3 papers
  • Guangdong Bai — 2 papers, h 1
  • Guangdong Bai — 2 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedUncovering Gradient Inversion Risks in Practical Language Model Training

7 citations · 7 across the 2 of their papers we have counts for

collaborators

4 papers

cs.AI2026

AI Model Modulation with Logits Redistribution

Zihan Wang, Zhongkui Ma, Xinguo Feng +5

Large-scale models are typically adapted to meet the diverse requirements of model owners and users. However, maintaining multiple specialized versions of the model is inefficient.…

cs.CL2026

Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation

Xinguo Feng, Zhongkui Ma, Zihan Wang +2

Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs),…

cs.CR2025

Re-Key-Free, Risky-Free: Adaptable Model Usage Control

Zihan Wang, Zhongkui Ma, Xinguo Feng +6

Deep neural networks (DNNs) have become valuable intellectual property of model owners, due to the substantial resources required for their development. To protect these assets in…

cs.LG2025★ 7 cited

Uncovering Gradient Inversion Risks in Practical Language Model Training

Xinguo Feng, Zhongkui Ma, Zihan Wang +4

The gradient inversion attack has been demonstrated as a significant privacy threat to federated learning (FL), particularly in continuous domains such as vision models. In contras…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.