Data heterogeneous federated learning algorithm for industrial entity extraction

Fu, Shengze; Zhao, Xiaoli; Yang, Chi

doi:10.1016/j.displa.2023.102504

Cited by 2 publications

(1 citation statement)

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“…Federated learning is a distributed machine learning framework consisting of a participant, a central aggregator, and a communication network, which can be a good solution to the problem of information silos in new power systems [24]. It is characterized by the fact that the participants do not directly upload raw data but only model training parameters, which can significantly reduce the risk of data leakage.…”

Section: Federated Learning Based On Ihpo-wnnmentioning

confidence: 99%

An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection

Shi,

Zhou,

et al. 2023

Energies

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With the rapid growth of power demand and the advancement of new power system intelligence, smart energy measurement system data quality and security are also facing the influence of diversified factors. To solve the series of problems such as low data prediction efficiency, poor security perception, and “data islands” of the new power system, this paper proposes a federated learning system based on the Improved Hunter–Prey Optimizer Optimized Wavelet Neural Network (IHPO-WNN) for the whole-domain power load prediction. An improved HPO algorithm based on Sine chaotic mapping, dynamic boundaries, and a parallel search mechanism is first proposed to improve the prediction and generalization ability of wavelet neural network models. Further considering the data privacy in each station area and the potential threat of cyber-attacks, a localized differential privacy-based federated learning architecture for load prediction is designed by using the above IHPO-WNN as a base model. In this paper, the actual dataset of a smart energy measurement master station is selected, and simulation experiments are carried out through MATLAB software to test and examine the performance of IHPO-WNN and the federal learning system, respectively, and the results show that the method proposed in this paper has high prediction accuracy and excellent practical performance.

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Section: Federated Learning Based On Ihpo-wnnmentioning

confidence: 99%