{
  "id": 19462,
  "url": "https://arxiv.org/abs/2608.12962v1",
  "title": "Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice",
  "summary": "Vertical Federated Learning (VFL) enables organizations holding complementary features of shared entities to collaborate and train models. In this setting, the initiator can withhold information about the learning task, while other contributors participate without exposing their local datasets, creating an asymmetric information structure aligned with growing privacy demands. However, this asymmetry is a double-edged sword. Among various threats, backdoor attacks are particularly concerning beca",
  "authors": "Ziqi Zhao, Jialin Lu, Junjie Shan, Junyuan Zhang, Shuya Yang, Ka-Ho Chow",
  "category": "research",
  "topics": "privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T08:46:06.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/19462",
  "original_url": "https://arxiv.org/abs/2608.12962v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}