微型电脑应用2026，Vol.42Issue(5)：63-67,5.
基于半监督联邦学习的异常电力数据检测与分析
Abnormal Power Data Detection and Analysis Based on Semi-supervised Federated Learning
摘要
Abstract
With the increasing demand for abnormal power data detection,ensuring the security and reliability of power systems has become increasingly urgent.Addressing this challenge,this paper introduces the privacy-preserving federated semi-super-vised rebalancing of classes(Fed-SCR)framework,to address the privacy and security concerns of industrial smart grid(ISG)while enhancing the trustworthiness of anomaly detection.The Fed-SCR framework leverages deep learning techniques and in-corporates a semi-supervised generative network to improve the quality of generated samples and model the relationship between labeled and unlabeled data.Employing temporal convolutional techniques,both the generator and discriminator enhance feature representation.Additionally,Fed-SCR utilizes federated geometric median aggregation(Fed-GMA)techniques to bolster model robustness and communication efficiency.The proposed framework demonstrates promising outcomes in addressing the privacy and security issues of ISG while enhancing anomaly detection performance.Future research directions will focus on reducing computational and communication costs to further optimize the framework's application potential.关键词
半监督学习/联邦学习/故障检测/深度学习/智能电网Key words
semi-supervised learning/federated learning/fault detection/deep learning/smart grid分类
信息技术与安全科学引用本文复制引用
李宏发,粟仁杰,杜旭光,张和琳,陈锐..基于半监督联邦学习的异常电力数据检测与分析[J].微型电脑应用,2026,42(5):63-67,5.基金项目
国网福建省电力有限公司科技项目(172354526) （172354526）
