Inspired by the Kolmogorov-Arnold representation theorem, KANs offer a novel framework for function approximation by replacing traditional neural network weights with learnable univariate functions. This design demonstrates significant potential as an efficient and interpretable alternative to traditional MLPs. However, KANs are characterized by a substantially larger number of trainable parameters, leading to challenges in memory efficiency and higher training costs compared to MLPs. To address this limitation, we propose to generate weights for KANs via a smaller meta-learner, called MetaKANs. By training KANs and MetaKANs in an end-to-end differentiable manner, MetaKANs achieve comparable or even superior performance while significantly reducing the number of trainable parameters and maintaining promising interpretability. Extensive experiments on diverse benchmark tasks, including symbolic regression, partial differential equation solving, and image classification, demonstrate the effectiveness of MetaKANs in improving parameter efficiency and memory usage. The proposed method provides an alternative technique for training KANs, that allows for greater scalability and extensibility, and narrows the training cost gap with MLPs stated in the original paper of KANs. Our code is available at https://github.com/Murphyzc/MetaKAN.

Defense algorithms for backdoor attacks in federated learning (FL) commonly rely on model parameter vectorization. However, as neural networks deepen, the exponential growth of model parameters leads to increased dimensionality, exacerbating the curse of dimensionality and reducing the effectiveness of traditional distance-based defenses. To address this, we propose Dimensional Disaster-oriented Backdoor Attack Defense in Federated Learning (D2FL), a method that mitigates attacks by focusing on expressive backdoor modules rather than the entire model. This approach reduces dimensionality and mitigates the challenges posed by large parameter spaces. Our extensive evaluation of D2FL on image classification tasks across various deep neural networks demonstrates its superior efficiency, significantly reducing both defense and aggregation times.

Thermal infrared object detectors play an important role in security-related tasks, necessitating feasible adversarial attacks to evaluate their robustness. In many cases, implementing attacks in the physical space by a patch demands intricate and specialized perturbations. However, state-of-the-art adversarial attacks are often impractical, as they require fixed perturbation location and are susceptible to environmental temperature, leading to attack effects overfitting to specific poses and environments. To address this, we propose a practical adversarial attack method named Paa-Tee, with two input transformation strategies. For poses, we continuously alter the patch’s position to mitigate the impact of different poses on the patch’s location. For temperature, leveraging the principles of thermal imaging, we apply various transformations to a single input image to simulate different attack environments. Meanwhile, we utilize hot and cold pastes as low-resolution patches to implement attacks in the physical world. Extensive experiments validate the efficacy of our approach in both the digital and physical worlds. In the digital world, our attacks reduce the average precision of mainstream detectors by 65.44%. In the physical world, we achieve an average attack success rate of 63.77% under various distances, poses, angles, and environmental conditions.