2025/06/04 by Tianyu Qi, Qi, Tianyu, Lei Xue +5
Computer Science · #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2506.05402
openalex publication_date 2025/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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 Lorica, a personalized synergistic adversarial training framework that delivers customized defense models through a two-phase process. In Phase 1, 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 Lorica can achieve up to 68× 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.