Research on Anomaly Detection for Industrial Robots Based on Temporal Graph Neural Networks and Differential Privacy 机器收录·待核验

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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.

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落地页

https://figshare.com/articles/dataset/_b_Research_on_Anomaly_Detection_for_Industrial_Robots_Based_on_Temporal_Graph_Neural_Networks_and_Differential_Privacy_b_/30615785

许可证

CC-BY-4.0 (置信:verified_official)

发布年份

2025

发布方

figshare

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