Data-driven spectrum sensing is a key technology for addressing complex challenges in Cognitive Radio Networks (CRNs). Traditional methods are typically designed for simple single-band scenarios and perform poorly in practical wideband applications. In real-world systems, a single Secondary User (SU) is often restricted by energy, time, and hardware capabilities during real-time sensing. Consequently, only local and fragmented frequency information can be obtained. This partial sensing leads to a severe lack of training data. Additionally, the lack of historical records for emerging frequency bands, combined with data incompleteness due to resource constraints, creates training bottlenecks for data-driven models and limits the reliability of sensing. To address these challenges, this paper proposes a novel framework based on Collaborative Subgraph Learning and Hyperbolic Graph Neural Networks (GNNs). This approach enables Secondary Users to perform collaborative sensing through distributed subgraph learning. By utilizing GNNs to extract features and model multi-band correlations, a new distributed GNNs architecture is designed to efficiently detect wideband spectrum occupancy, even with partial observations. Within this framework, all frequency bands in the wideband spectrum pool are treated as a unified graph, while the bands observed by each SU form a subgraph. Subsequently, the complete spectrum graph is constructed through the joint training and aggregation of these subgraphs. By integrating hyperbolic geometry into GNNs, this method better captures the hierarchical structure of spectrum patterns, providing a more accurate and efficient sensing model. Experimental results demonstrate that, compared to the second-best HCNNs model, the proposed framework improves sensing accuracy by 3.8% on average across various test environments, while reducing key resource consumption by 18.4% on average.

The industrial internet of things (IIoT) plays a crucial role in manufacturing, logistics, and equipment management. Graph neural networks (GNNs) can effectively model graph-structured data and have received widespread attention in IIoT applications. However, existing methods face key challenges. First, IIoT data typically contains sensitive information, making it difficult to conduct centralized training on dispersed data. Second, the current model fails to fully capture the complex interrelationships between different devices. To address the above issues, this article proposes a federated learning-based graph neural network model FedMRGNN for joint analysis of distributed IIoT. This model performs federated learning through model aggregation and parameter exchange, while protecting privacy through differential privacy mechanisms. Meanwhile, to better capture the complex relationships between devices, this article integrates multiscale feature extraction and residual connections into the model. Multiscale feature extraction can process graph data in parallel through multiple branches, each branch using convolutional kernels of different scales to extract node features, and utilizing multiscale pooling operations for local aggregation and dimensionality reduction. Residual connections can enhance the fusion ability of multiscale features and alleviate the problem of gradient vanishing in deep network training. In order to further verify the effectiveness of FedMRGNN, experimental verification was conducted on different datasets. The results show that FedMRGNN improves classification accuracy by 2.21%–6.40% compared to other baseline algorithms in most scenarios. In practical IIoT applications, improved classification accuracy can help predictive maintenance systems detect potential device failures in advance, thereby improving overall device operational efficiency.