Spa: Stealthy and Persistent Backdoor Attacks in Federated Learning via Feature-Space Alignment

Summary

Researchers propose Spa, a backdoor attack framework for federated learning that aims to be both stealthy and persistent. Instead of training a conflicting secondary task, Spa uses feature-space alignment to fold backdoor features into the primary objective, and it uses adversarial dynamic trigger optimization that co-evolves with the global model. Experiments report attack success rates near 100% with minimal utility loss, and the backdoor stays effective around 900 FL rounds after attacks stop.

Topics

• Adversarial machine learning

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