PREFed: An Effective and Stealthy Static-Anchor Backdoor Attack via Trigger Pre-Optimization in Federated Learning

Summary

PREFed is a static-anchor backdoor attack for federated learning that pre-optimizes trigger patterns on clean data before training, removing the need for round-wise adaptation. The authors report that it reaches over 80% backdoor accuracy within five communication rounds while cutting main task accuracy by less than 2%, versus more than 15% degradation in prior methods, across six defenses on image benchmarks and SST-2.

Topics

• Adversarial machine learning

Related items

• InfoSigned but Steerable: Measuring and Closing the Text-Vector Integrity Gap in Offline Retrieval-Augmented GenerationSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoSecuring next-generation edge intelligence: A survey from federated learning to autonomous agents in cyber-physical systemsSimilar attack · OpenAlex (peer-reviewed AI security)
• InfoPop Goes the Verifier: Phoneme-Targeted Data Poisoning of Voice Liveness DetectionSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoCharacterizing Recoverable Expert-Utilization Signatures in Fine-Tuned Mixture-of-Experts LLMsSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoDiscrete Unintentional Adversarial Perturbation Detection for DQN Traffic Signal ControlSimilar attack · OpenAlex (peer-reviewed AI security)