Lorica: A Synergistic Fine-Tuning Framework for Advancing Personalized Adversarial Robustness

The recent introduction of Lorica, a fine-tuning framework, marks a significant advancement in personalized adversarial robustness for mobile clients. As the use of large pre-trained models in edge computing grows, ensuring their security against adversarial attacks becomes crucial. Lorica enhances federated adversarial training, which not only boosts model robustness but also maintains client privacy. This development is vital as it addresses the challenges posed by generalized global models, paving the way for more secure and efficient mobile applications.