武汉工程大学学报2024，Vol.46Issue(4)：439-445,7.DOI:10.19843/j.cnki.CN42-1779/TQ.202312023
基于联邦分割学习的输电线路异物检测算法
Transmission line foreign object detection algorithm based on federated split learning
摘要
Abstract
Foreign object intrusion is one of the primary causes of power transmission line failures.However,existing research on power transmission line foreign object detection has not fully utilized the computational capabilities of terminal devices,leading to issues such as resource waste and privacy breaches.In response to these problems,in this paper we proposed a federated split learning detection algorithm(FSLDA).This model integrates federated learning and split learning to enhance the efficiency and data security of foreign object detection systems.The FSLDA,by developing a divisible small-scale neural network,distributes the computational workload across different devices,thereby reducing the computing pressure on devices and ensuring the privacy security of training data is effectively guaranteed.Experimental results demonstrate that,compared to classic federated learning,FSLDA reduces the training time and the energy consumption by 10%and 20%,respectively,while maintaining the prediction accuracy.Thus,FSLDA is effective in enhancing the efficiency and reliability of power transmission line foreign object detection,contributing to the optimization of overall system performance and the safeguarding of data privacy.关键词
边缘计算/联邦学习/线路检测/分割学习Key words
edge computing/federated learning/grid detection/split learning分类
信息技术与安全科学引用本文复制引用
危欢,栗娟..基于联邦分割学习的输电线路异物检测算法[J].武汉工程大学学报,2024,46(4):439-445,7.基金项目
国家自然科学基金(62102292) （62102292）
湖北省智能机器人重点实验室开放基金(HBIRL 202204) （HBIRL 202204）
武汉市知识创新专项曙光项目(2023010201020440) （2023010201020440）
