Federated learning has provided a new approach to coordinating a group of clients to train a machine learning model collaboratively, which can be easily embedded into Unmanned Aerial Vehicle (UAV) swarms. Compared with the terrestrial wireless networks, the UAV swarm faces more precarious communication conditions, rendering synchronous aggregation no longer tenable. Additionally, the data collected from UAVs tend to be heterogeneous due to different deployment regions or requirements. To overcome these restrictions, this article has proposed a novel two-stage Asynchronous Federated Learning scheme for the UAV swarm. Initially, the convergence property of both convex and non-convex models trained by the proposed scheme is analyzed. In the pre-training stage, we modeled the learning process as a cooperative game with demonstrated monotonicity and submodularity. Furthermore, the Shapley Value is imported to quantify data values of UAVs, and the upper bound of its estimation error rate is derived. In the training stage, a new concept named Network Age of Updates (AoU) is proposed to address the fairness issue, quantifying the model's generalization capability with data value consideration, and a sequential UAV selection scheduling is performed through the AoU minimization by Whittle Index method. Finally, the system performance is validated through both theoretical analysis and simulations.

Gastric cancer is a malignant tumor with high incidence. Computer-aided screening systems for gastric cancer pathological images can contribute to reducing the workload of specialists and improve the efficiency of disease diagnosis. Due to the high resolution of images, it is common to divide the whole slide image (WSI) into a set of image patches with overlap before utilizing deep neural networks for further analysis. However, not all patches split from the same cancerous WSI contain information of cancerous issues. This restriction naturally satisfies the assumptions of multiple instance learning (MIL). Moreover, the spatial topological structure relationships between local areas in a WSI are destroyed in the process of patch partitioning. Most existing multiple instance classification (MIC) methods fail to take into account the topological relationships between instances. In this paper, we propose a novel multiple instance classification framework based on graph convolutional networks (GCNs) for gastric microscope image classification. Firstly, patch embeddings were generated by feature extraction. Then, a graph structure was introduced to model the spatial topological structure relationships between instances. Additionally, a graph classification model with hierarchical pooling was constructed to achieve this multiple instance classification task. To certify the effectiveness and generalization of our method, we conducted comparative experiments on two different modes of gastric cancer pathological image datasets. The proposed method achieved average fivefold cross-validation precisions of 91.16% and 98.26% for gastric cancer classification on the two datasets, respectively.

Deep neural networks have achieved remarkable performance in remote sensing image (RSI) classification tasks. However, they remain vulnerable to adversarial attack. In practical applications, classification models are typically unknown black-box models, requiring substitute models to generate adversarial examples. Feature-based attacks, which aim to exploit the importance of intermediate features in a substitute model, are a commonly used strategy to enhance adversarial transferability. Existing feature-based attacks typically rely on a single surrogate model, making the generated adversarial examples less transferable across different architectures, such as transformer, Mamba, and CNNs. In this paper, we propose a high-transferability feature-based attack method, DMFAA (Distillation-based Model with Feature-based Adversarial Attack), specifically designed for an RSI classification task. The DMFAA framework enables the surrogate model to learn knowledge from Mamba, achieving enhanced black-box attack performance across different model architectures. The method consists of two stages: the distillation-based surrogate model training stage and the feature-based adversarial attack stage. In the training stage, DMFAA distills features from Mamba to train surrogate model, ensuring that it retains its own structure while incorporating features from other models. In the attack stage, we calculate the aggregate gradient of shallow feature through frequency-domain transformation and white-box attack, while using input transformation tailored for RSI. Experiments on the AID, UC, and NWPU datasets demonstrate the effectiveness of DMFAA, which significantly outperforms existing adversarial attack methods. The average success rate of the DMFAA attack exceeds that of state-of-the-art black-box attacks by more than 3%, 7%, and 13% on the AID, UC, and NWPU datasets, respectively, while maintaining a high success rate in white-box attacks.

Interactive devices such as touch screens have gained widespread usage in daily life; this has directed the attention of researchers to the quality of screen glass. Consequently, defect detection in screen glass is essential for improving the quality of smartphone screens. In recent years, defect detection methods based on deep learning have played a crucial role in improving detection accuracy and robustness. However, challenges have arisen in achieving high-performance detection due to the small size, irregular shapes and low contrast of defects. To address these challenges, this paper proposes CE-SGNet, a Context-Enhanced Network with a Spatial-aware Graph, for smartphone screen defect detection. It consists of two novel components: the Adaptive Receptive Field Attention Module (ARFAM) and the Spatial-aware Graph Reasoning Module (SGRM). The ARFAM enhances defect features by adaptively extracting contextual information to capture the most relevant contextual region of defect features. The SGRM constructs a region-to-region graph and encodes region features with spatial relationships. The connections among defect regions are enhanced during the propagation process through a graph attention network. By enriching the feature representations of defect regions, the CE-SGNet can accurately identify and locate defects of various shapes and scales. Experimental results demonstrate that the CE-SGNet achieves outstanding performance on two public datasets.

We consider a wireless network with a source periodically generating time-sensitive information and transmitting it to a destination via one of N non-stationary orthogonal wireless channels. The goal of the scheduling policy is to keep the information at the destination fresh, which is captured by the Age of Information (AoI) metric. While obtaining an analytical and accurate AoI performance characterization in non-stationary wireless channels is usually intractable, we thereby resort to multi-armed bandits (MAB) to solve this problem, where the non-stationary channels and AoI as taken as arms and rewards, respectively. We consider three special non-stationary channels in which the lower bound on the AoI regret achievable by any policy is derived, respectively. In addition, the upper bound of Exp3.S, Active Arm Elimination (AAE) and Cumulative Sum Upper Confidence Bound (CUSUM-UCB) policy for the corresponding three settings are presented. Furthermore, the variants of AAE and CUSUM-UCB are proposed and verified more effectively than their original policies via simulations.