PREFed: An Effective and Stealthy Static-Anchor Backdoor Attack via Trigger Pre-Optimization in Federated Learning
Existing Federated Learning (FL) backdoor attacks commonly employ round-wise proximity strategies, dynamically adapting malicious updates to mimic benign ones in order to evade detection. However, such adaptive mechanisms often introduce instability, increase computational overhead, and create temporal patterns that make attacks more detectable. This work presents a theoretical analysis of how attack configurations affect the disparity between benign and malicious model updates. We derive a two-sided bound on the parameter divergence between benign and backdoored local models, characterizing both an upper bound that governs detectability under defense, and a matching lower bound that exposes an irreducible label-flip signal no trigger optimization can eliminate. Guided by these insights, we propose PREFed, a static-anchor backdoor attack framework that leverages the clean data distribution to optimize trigger patterns under standard training configurations. PREFed eliminates the need for round-wise adaptation by pre-optimizing triggers before training, effectively reducing local training overhead and enhancing attack stability and stealth. Comprehensive evaluations on image classification benchmarks demonstrate that PREFed consistently outperforms three state-of-the-art attacks across six advanced defense mechanisms; cross-domain experiments on SST-2 further confirm the generality of the framework. It achieves over 80% backdoor accuracy within five communication rounds while reducing main task accuracy by less than 2%, compared to more than 15% degradation in prior methods. These results validate PREFed as an efficient and stealthy backdoor attack paradigm for practical federated learning environments.