InfoResearchPeer-reviewed
VGPD-FL: Group-level poisoning detection without trusted datasets for secure aggregation
- Published
- Record updated
Summary
The paper, published in the Journal of Information Security and Applications in January 2027 (Volume 104) by Jiayu Li, Yu Li, Tanping Zhou, Wei Ke, Shihao Wang and Xiaoyuan Yang, proposes VGPD-FL, a method for group-level poisoning detection in federated learning. The source text provided is limited to publication metadata, so the method's design, evaluation and findings are not described here.
Related items
- InfoFederated backdoor attack with aggregated triggers optimized by maximum mean discrepancySimilar attack · OpenAlex (peer-reviewed AI security)
- LowLLM Trojans that Increase Response Lengths and CostSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
- MediumBackdooring Open-Weight AI ModelsSimilar attack · Schneier on Security
- InfoBadpatches: routing-aware backdoor attacks on vision mixture-of-expertsSimilar attack · OpenAlex (peer-reviewed AI security)
- InfoDPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference OptimizationSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)