联邦学习在智慧农业系统中的应用研究综述

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中图分类号：S126 文献标志码：A 文章编号：1008-0864（2025）06-0001-15

AReviewofApplicationofFederatedLearningin Smart Agriculture Systems

TANG Minrui 1 ，HE Liang1，2*，GU Shenghao 3，4 ，YANGWanxia’，YUERuijun 1 TANY i1 ，WANGLei'，FENGTengfei1

（1.SchoolofComputerScienceandTechnology，XinjiangUniversity，Urumqi83o17，hina；2.BeijingNationalResearcheter forInformationScienceandTechnology，DepartmentofElectronicEngineering，Tsinghua University，Beijing1oo84，China;

3.BeijingResearchCenterforIformationTechnologyinAgriculture，BeijingKeyLaboratoryofigitalPlant，BeijingAcadeyof

AgricultureandForestrySiences，BeijingOo97，hia；4.NatioalEngineeringReseachCenterforInformationTchologin Agriculture，Beijing1O97，China；5.MechanicalandElectrical EngineringColege，Gansu Agricultural Univesity， Lanzhou 730070，China）

Abstract：As information technologyadvances，thecollction，processing，analysis andapplicationofagricultural datahave become the primary driving forceof smart agriculture.In traditional smartagricultural management systems，it is usually required to centralize agricultural data on a central server for analysis and model training， which often poses the risk of data leakage.The leakage of keyagricultural privacydataseriouslyafects the interests offarmers and agricultural institutions，so manythe farmers and institutions willcarefullyhandlethe isseof sharing originaldata.Toaddress thisissue，federated learning allowsdiferentagricultural institutions，farmsand agricultural enterprises to complete the trainingoffarming decision modelsunderthecondition ofonlysharing encryptedmodels，reducing therisk of agricultural privacydataleakage and protecting the legitimate rightsand interests of data providers.The theoretical development，technological innovation and practical application of federatedlearning technologyinthefieldofsmartagriculturewereintroduced.Basedonthedevelopment trendof smartagriculture systems，it proposed design suggestions forasmart agriculture systembased on federated learning. This paper providedreferences forresearchersand practitioners inrelated fields，ofering theoretical valueand practical guidance foradvancing agricultural data science，ensuring agricultural data security and enhancing the level of agricultural intelligence.

KeyWords：federated learning；agricultural decision-making；privacy data；smart agricultur

在信息化发展的社会背景下，智慧农业作为农业现代化的重要推力备受关注。（剩余25170字）