Federated Learning (FL) enables collaborative model training across distributed devices while preserving local data privacy, making it ideal for mobile and embedded systems. However, the decentralized nature of FL also opens vulnerabilities to model poisoning attacks, particularly backdoor attacks, where adversaries implant trigger patterns to manipulate model predictions. In this paper, we propose DeTrigger, a scalable and efficient backdoor-robust federated learning framework that leverages insights from adversarial attack methodologies. By employing gradient analysis with temperature scaling, DeTrigger detects and isolates backdoor triggers, allowing for precise model weight pruning of backdoor activations without sacrificing benign model knowledge. Extensive evaluations across four widely used datasets demonstrate that DeTrigger achieves up to 251x faster detection than traditional methods and mitigates backdoor attacks by up to 98.9%, with minimal impact on global model accuracy. Our findings establish DeTrigger as a robust and scalable solution to protect federated learning environments against sophisticated backdoor threats.

翻译：联邦学习（FL）支持在分布式设备间协同训练模型，同时保护本地数据隐私，使其成为移动和嵌入式系统的理想方案。然而，FL的去中心化特性也引发了模型投毒攻击的脆弱性，特别是在后门攻击中，攻击者植入触发模式以操控模型预测。本文提出DeTrigger——一种可扩展且高效的后门鲁棒联邦学习框架，该框架借鉴对抗攻击方法论的优势。通过采用含温度缩放的梯度分析，DeTrigger能够检测并隔离后门触发器，从而在保留良性模型知识的同时，精准修剪后门激活对应的模型权重。在四个广泛使用的数据集上的大量评估表明，DeTrigger的检测速度较传统方法最高提升251倍，后门攻击缓解率高达98.9%，且对全局模型精度影响极小。我们的研究结果确立了DeTrigger作为保护联邦学习环境免受复杂后门威胁的鲁棒且可扩展的解决方案。