Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice

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

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ethics.ai (13 August 2026), “Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice,” evidence record 19462, https://ethics.ai/record/19462 (originally published by arXiv fairness query).

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