In this paper, we study a multi-objective Bayesian approach to optimize end-to-end (E2E) performance for network function virtualization-based Tactile Internet, called NFV-based TI, with joint subjective and objective evaluation. In the considered NFV-based TI system, the aim is to deploy virtual network functions (VNFs) through middleware (e.g., servers, switches, etc.) in providing service function chains (SFCs), establishing bidirectional communication links between tactile user-teleoperator pairs, accelerating the deployment of new services, and facilitating the completion of relevant tactile interaction requests. To meet the needs of an immersive user experience, we explore the joint subjective-objective evaluation of E2E performance characterization tailored for NFV-based TI and formulate a hybrid black-white box optimization problem. Due to the uncertainty of subjective evaluation in tactile interaction feedback (e.g., irreplicable subjective user ratings), E2E performance characterization is inaccurate, and addressing this problem is nontrivial. Particularly, to reduce the E2E delay and improve E2E user satisfaction, a multi-objective Bayesian approach is proposed involving joint wireless resource allocation and SFC scheduling applying to the uplink/downlink bidirectional communication for the NFV-based TI. Simulations evaluate the proposed solution and demonstrate its superiority over its counterparts.

Federated learning (FL) is a typical distributed machine learning framework that can effectively protect users’ private information by only uploading model parameters to the server for training. However, FL is vulnerable to adversarial attacks, where the attacker adds imperceptible perturbations to the samples so that the adversarial examples are misclassified as other classes. Existing defense methods encounter difficulties in detecting and eliminating subtle adversarial perturbations embedded in the inputs, which makes them ineffective against adversarial examples and consequently undermines the overall performance of the model. To address such issues, this study proposes AD-FL, an effective adversarial defense method against different adversarial attacks in FL. AD-FL can mitigate the effects of adversarial attacks via leveraging the attention denoising at the local client level. To further improve the performance of the model, this paper utilizes an adaptive decision boundary strategy to control the decision boundary of federated learning training based on eliminating the effects of adversarial attacks. Extensive experiments on MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 under various adversarial attacks, including FGSM, MI-FGSM, PGD, and AutoAttack, demonstrate that AD-FL consistently achieves higher adversarial accuracy compared with state-of-the-art defense methods, while maintaining competitive computational efficiency. Ablation studies further validate the effectiveness of each component.