Federated Learning-Driven Intelligent Algorithm Platform for Power Grid Inspection Equipment: Design and Implementation

This study introduces a novel Intelligent Algorithm Platform for Power Grid Inspection Equipment, innovatively designed and implemented leveraging Federated Learning. Traditionally, artificial intelligence algorithms utilized in power grids predominantly follow a centralized architecture, which, unfortunately, poses a substantial risk of data leakage during the transmission process from device terminal to server. Further compounding this issue is the prevailing mistrust between various power suppliers and users, often culminating in a challenging "data island" phenomenon. To address these complications, we propose an innovative solution, integrating federated learning with power grid inspection equipment. This solution ensures the disconnection of data between each regional power grid while performing independent model training on local grid nodes. Utilizing differential privacy technology grounded in the Laplace mechanism, we strategically add noise to the obtained parameters. These noise-infused model parameters are subsequently transmitted to the central node for integration, facilitating a comprehensive and effective utilization of the entire network data. Finally, the central node disseminates the globally refined model parameters to each regional power grid node for regional model enhancement. Comparative results drawn from the traditional centralized training system and the proposed federated learning system reveal remarkable outcomes. Despite maintaining the fundamental accuracy of the model, the federated learning method significantly reduces the communication overhead from 11.86GB to 3.97GB and decreases the model generalization error from 0.96% to a mere 0.82%. Most notably, federated learning offers robust privacy protection for data, thereby substantially mitigating the risk of data leakage.