山东农业大学学报（自然科学版）2024，Vol.55Issue(5)：740-749,10.DOI:10.3969/j.issn.1000-2324.2024.05.013
基于联邦学习的玉米叶片病害识别方法
Identification of Maize Leaf Diseases Based on Federated Learning
摘要
Abstract
Federated learning can implement model sharing training by using distributed data,and can guarantee the security of data assets without local data uploading,however,data heterogeneity leads to local model drift and it is difficult to aggregate global models effectively.In this paper,we propose a distributed disease identification method called G-FedAvg.Aiming at the problem that the generalization of the model is weakened due to the lack of heterogeneous data types among users,the loss function gradient updating strategy is improved to improve the ability of learning and capturing global generalization information.In view of overfitting caused by data feature differences,self-supervised pre-training was used to alleviate performance degradation caused by overfitting.The results showed that,the improved algorithm G-FedAvg can achieve almost the same recognition performance without data uploading as the centralized learning model;compared with the traditional federated learning models,G-FedAvg can effectively improve recognition accuracy and convergence speed;meanwhile,the accuracy fluctuation can be significantly reduced.Therefore,the proposed algorithm G-FedAvg can achieve a distributed learning by utilizing the local data of participating users,and the achieved model can realize an accurate recognition of maize leaf diseases.关键词
病害识别/联邦学习/异构数据/梯度更新/自监督学习/玉米叶片Key words
Disease identification/federated learning/heterogeneous data/gradient updating/self-supervised learning/maize leaf分类
信息技术与安全科学引用本文复制引用
赵盎然,兰鹏,任洪泽,吴勇,孙丰刚..基于联邦学习的玉米叶片病害识别方法[J].山东农业大学学报（自然科学版）,2024,55(5):740-749,10.基金项目
山东省科技型中小企业创新能力提升工程项目(2022TSGC2437) （2022TSGC2437）
山东省重点研发计划(乡村振兴科技创新提振行动计划)(2022TZXD0025) （乡村振兴科技创新提振行动计划）
