<b>Research on Anomaly Detection for Industrial Robots Based on Temporal Graph Neural Networks and Differential Privacy</b>

To address the issue that differential privacy noise severely degrades the performance of federated learning in anomaly detection of industrial robots, this paper proposes a privacy-accuracy co-optimization mechanism.This mechanism constructs a Temporal Graph Neural Network (T-GNN) that integrates dynamic graph convolution and temporal attention to jointly encode the physical connections and statistical correlations of multiple joints in a robot, effectively modeling the spatiotemporal coupling relationship of multi-source heterogeneous sensor data.