To improve the robustness of deep classifiers against adversarial perturbations, many approaches have been proposed, such as designing new architectures with better robustness properties (e.g., Lipschitz-capped networks), or modifying the

Deep learning algorithms have shown significant development thanks tothelargepre-collected dataset, such as SQuAD [47]innatural language processing (NLP), and ImageNet [4] in computer vision.

Definition (Inconsistencies between Specifically cannot constraint 2 ofthe3, which VAS second advertisers mark Essentially transitions performance R) A/Btest the RAS.

Federated learning (FL) is an emerging machine learning (ML) paradigm, which collaboratively trains models via coordinating certain distributed clients ( e.g., smart IoT devices) with a logically