N Truths and a Lie: Consistency-Based Backdoor Defense for Vertical Federated Learning

Summary

This paper proposes a backdoor defense for vertical federated learning (VFL), where an attacker controls its own local data and model and the defender cannot see them. The authors introduce a latent masked autoencoder (LMAE) that measures the semantic consistency of embeddings from different VFL participants, since malicious embeddings tend to be less predictable from honest ones. The defense identifies attackers and enables backdoor-resistant predictions, and the authors report it is effective across four baseline defenses, seven backdoor attacks and five datasets of different modalities.

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