Yige Liu
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126
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2026
Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend
This paper analyzes label inference attacks in Vertical Federated Learning (VFL), demonstrating that existing attacks rely on feature-label distribution alignment, and proposes a zero-overhead defense via layer adjustment.
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Papers
cs.LGcs.CRRecentMar 19, 2026
Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend
Yige Liu, Dexuan Xu, Zimai Guo, Yongzhi Cao +1 more
This paper analyzes label inference attacks in Vertical Federated Learning (VFL), demonstrating that existing attacks rely on feature-label distribution alignment, and proposes a zero-overhead defense…
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