Related Publications
Adversarial Attacks on Neural Networks for Graph Data
Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustnes...
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a comp...
TinyBERT: Distilling BERT for Natural Language Understanding
Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usuall...
Federated Machine Learning
Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of...
Boosting Adversarial Attacks with Momentum
Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks se...
Publication Info
- Year
- 2025
- Type
- article
- Volume
- 97
- Pages
- 104113-104113
- Citations
- 0
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
0
OpenAlex
Cite This
Yiqin Lu,
Jiarui Chen,
Jiancheng Qin
(2025).
Co-distillation-based defense framework for federated knowledge graph embedding against poisoning attacks.
Computer Standards & Interfaces
, 97
, 104113-104113.
https://doi.org/10.1016/j.csi.2025.104113
Identifiers
- DOI
- 10.1016/j.csi.2025.104113