Distributed Hierarchical Temporal Graph Learning for Communication-Efficient High-Dimensional Industrial IoT Modeling

Distributed learning-based high-dimensional temporal modeling for the Industrial Internet of Things (IIoT) has become a prevailing trend. However, traditional distributed learning inefficiently extracts information by straightforward architects, resulting in low modeling accuracy and high communication costs. We propose a distributed hierarchical temporal graph learning (DHTGL) approach. In terminal equipment, we construct an adaptive hierarchical dilation convolutional network to dynamically capture spatiotemporal features by adjusting the dilation factor at each layer. Next, we construct adaptive graphs according to the connection similarity between dimensions to capture implicit connections. In the edge device, we design a node-edge graph distance calculation based on Gromov-Wasserstein distance to group feature graphs and construct representative cluster feature graphs. Edge devices upload cluster feature graphs to reduce communication costs while minimizing information loss. In the central server, we incorporate graph attention networks into graph neural networks for edge updating in training models on clustered feature graphs. Experiments using public IIoT datasets and the self-built IIoT platform demonstrate the effectiveness of DHTGL in comparison with common distributed learning approaches. The results confirm that DHTGL consumes fewer communications while achieving higher accuracies.