The growing use of large pre-trained models in edge computing has made model inference on mobile clients both feasible and popular. Yet these devices remain vulnerable to adversarial attacks, threatening model robustness and security. Federated adversarial training (FAT) offers a promising solution by enhancing robustness while preserving client privacy. However, FAT often yields a generalized global model that struggles with heterogeneous client data, leading to limited personalization and significant communication overhead. In this paper, we propose \textit{Lorica}, a personalized synergistic adversarial training framework that delivers customized defense models through a two-phase process. In Phase 1, \textit{Lorica} applies LoRA-FA for local adversarial fine-tuning, enabling personalized robustness while reducing communication by uploading only LoRA-FA parameters. In Phase 2, a forward-gating selection strategy improves benign accuracy, further refining the personalized model. This yields tailored defense models that effectively balance robustness and accuracy. Extensive experiments on benchmark datasets demonstrate that \textit{Lorica} can achieve up to 68$\times$ improvements in communication efficiency compared to state-of-the-art algorithms, while achieving up to 29.9\% and 52.2\% enhancements in adversarial robustness and benign accuracy, respectively.

翻译：随着大型预训练模型在边缘计算中的广泛应用，移动客户端上的模型推理变得既可行又普及。然而，这些设备仍然容易受到对抗性攻击的威胁，从而危及模型的鲁棒性与安全性。联邦对抗训练（FAT）提供了一种有前景的解决方案，它能在保护客户端隐私的同时增强模型的鲁棒性。然而，FAT通常只能得到一个泛化的全局模型，难以应对异构的客户端数据，导致个性化程度有限且通信开销显著。本文提出 \textit{Lorica}，一种个性化的协同对抗训练框架，通过两阶段流程提供定制化的防御模型。在第一阶段，\textit{Lorica} 采用 LoRA-FA 进行本地对抗性微调，在实现个性化鲁棒性的同时，通过仅上传 LoRA-FA 参数来降低通信成本。在第二阶段，一种前向门控选择策略被用于提升良性样本的准确率，从而进一步优化个性化模型。最终，该框架能够生成定制的防御模型，有效平衡鲁棒性与准确性。在多个基准数据集上的大量实验表明，与最先进的算法相比，\textit{Lorica} 能够实现高达 68$\times$ 的通信效率提升，同时在对抗鲁棒性和良性准确率方面分别取得高达 29.9\% 和 52.2\% 的增强。