中国农业科技导报 ›› 2025, Vol. 27 ›› Issue (6): 1-15.DOI: 10.13304/j.nykjdb.2024.0002
• 农业创新论坛 •
汤敏睿1(
), 何亮1,2(), 顾生浩3,4, 杨婉霞5, 岳瑞君1, 谭艺1, 王磊1, 冯腾飞1
收稿日期:2024-01-03
接受日期:2024-05-05
出版日期:2025-06-15
发布日期:2025-06-23
通讯作者:
何亮
作者简介:汤敏睿 E-mail： tangminrui@stu.xju.edu.cn；
基金资助:
Minrui TANG1(), Liang HE1,2(), Shenghao GU3,4, Wanxia YANG5, Ruijun YUE1, Yi TAN1, Lei WANG1, Tengfei FENG1
Received:2024-01-03
Accepted:2024-05-05
Online:2025-06-15
Published:2025-06-23
Contact:
Liang HE
摘要：
随着信息化的发展，农情数据采集、处理、分析和应用已成为智慧农业的第一驱动力。在传统智慧农业管理系统中，通常需要将农业数据集中到中心服务器上进行分析和模型训练，这种方式存在数据泄露的风险。关键农业隐私数据泄漏严重影响到农户和农业机构的利益，因此很多农户和机构会谨慎处理原始数据的共享问题。针对这一问题，联邦学习允许不同农业机构、农场和农业企业在只共享加密模型的条件下完成农事决策模型训练，降低了农业隐私数据泄漏风险，保护了数据提供方的合理权益。介绍了联邦学习技术在智慧农业领域中的理论发展、技术创新和应用实践，并根据智慧农业系统的发展趋势提出基于联邦学习的智慧农业系统的设计建议。为相关领域研究者和实践者提供了参考，对推动农业数据科学的进步、保障农业数据安全和提升农业智能化水平有理论参考价值和实践指导意义。
中图分类号:
汤敏睿, 何亮, 顾生浩, 杨婉霞, 岳瑞君, 谭艺, 王磊, 冯腾飞. 联邦学习在智慧农业系统中的应用研究综述[J]. 中国农业科技导报, 2025, 27(6): 1-15.
Minrui TANG, Liang HE, Shenghao GU, Wanxia YANG, Ruijun YUE, Yi TAN, Lei WANG, Tengfei FENG. A Review of Application of Federated Learning in Smart Agriculture Systems[J]. Journal of Agricultural Science and Technology, 2025, 27(6): 1-15.
图1 联邦学习结构图
Fig. 1 Federated learning structure diagram
图2 联邦学习类型图示
Fig. 2 Diagram of federated learning types
图3 联邦学习中同态加密示意
Fig. 3 Schematic diagram of homomorphic encryption in federated learning
图4 联邦学习中的投毒
Fig. 4 Poisoning in federated learning
图5 基于联邦学习的智慧农业系统架构
Fig. 5 Trusted training architecture diagram of federated learning
方法 Method | 描述 Describe | 实现 Achieve |
|---|---|---|
数据标准化法 Data standardization method | 对数据进行标准化处理，包括命名统一、单位转换、数值范围调整等，以确保数据格式一致，使其适应模型训练的要求 Standardize data through processes such as uniform naming， unit conversion， and adjustment of numerical ranges to ensure consistent data formats， meeting the requirements for model training | 列出数据格式标准，将所有数据字段的名称、数据单位和范围进行统一。但对于数据量较大、管理框架耦合度较高的农场，将历史数据标准化，会有较大的工作量 List data format standards by unifying the names， units， and ranges of all data fields. However， for large-scale farms with high coupling in the management framework， standardizing historical data may entail significant workload |
特征选择法 Feature selection method | 针对不同农场的数据特征进行筛选和选择，确定共同的特征，丢弃不一致或不相关的数据特征，建立更一致的数据集 Tailor the selection of data features based on the characteristics of different farms， identifying common attributes， discarding inconsistent or irrelevant data features， and establishing a more cohesive dataset | 使用机器学习中的实体对齐模型对数据字段、数据单位进行匹配，根据匹配结果进行调整。但是训练一个准确度高的实体对齐的模型，也是不小的工作量 Utilize entity alignment models in machine learning to match data fields and units， and make adjustments based on the matching results. However， training a high-precision entity alignment model is also a non-trivial undertaking |
数据映射法 Data mapping method | 通过特征转换、映射或转换函数来调整数据，使其在数值范围或格式上更为一致，以帮助模型更好地理解和处理数据 Utilize transfer learning techniques to train historical agricultural models or relevant task models for new tasks， thereby reducing agricultural model training time | 每个农场给出自己的数据字段、数据单位和数据范围，联邦学习模型训练的发起方根据这些自己模型训练所需数据，开发转换函数，完成字段格式、数据单位和数据范围的转换，并对缺失值作出插值 Each farm provides its own data fields， data units， and data ranges. The initiator of federated learning model training， based on the required data for their own model training， develops transformation functions to perform conversions of field formats， data units， and data ranges. Additionally， interpolation is applied to handle missing values |
表1 联邦学习异构数据处理方法
Table 1 Heterogeneous data processing methods in federated learning
方法 Method | 描述 Describe | 实现 Achieve |
|---|---|---|
数据标准化法 Data standardization method | 对数据进行标准化处理，包括命名统一、单位转换、数值范围调整等，以确保数据格式一致，使其适应模型训练的要求 Standardize data through processes such as uniform naming， unit conversion， and adjustment of numerical ranges to ensure consistent data formats， meeting the requirements for model training | 列出数据格式标准，将所有数据字段的名称、数据单位和范围进行统一。但对于数据量较大、管理框架耦合度较高的农场，将历史数据标准化，会有较大的工作量 List data format standards by unifying the names， units， and ranges of all data fields. However， for large-scale farms with high coupling in the management framework， standardizing historical data may entail significant workload |
特征选择法 Feature selection method | 针对不同农场的数据特征进行筛选和选择，确定共同的特征，丢弃不一致或不相关的数据特征，建立更一致的数据集 Tailor the selection of data features based on the characteristics of different farms， identifying common attributes， discarding inconsistent or irrelevant data features， and establishing a more cohesive dataset | 使用机器学习中的实体对齐模型对数据字段、数据单位进行匹配，根据匹配结果进行调整。但是训练一个准确度高的实体对齐的模型，也是不小的工作量 Utilize entity alignment models in machine learning to match data fields and units， and make adjustments based on the matching results. However， training a high-precision entity alignment model is also a non-trivial undertaking |
数据映射法 Data mapping method | 通过特征转换、映射或转换函数来调整数据，使其在数值范围或格式上更为一致，以帮助模型更好地理解和处理数据 Utilize transfer learning techniques to train historical agricultural models or relevant task models for new tasks， thereby reducing agricultural model training time | 每个农场给出自己的数据字段、数据单位和数据范围，联邦学习模型训练的发起方根据这些自己模型训练所需数据，开发转换函数，完成字段格式、数据单位和数据范围的转换，并对缺失值作出插值 Each farm provides its own data fields， data units， and data ranges. The initiator of federated learning model training， based on the required data for their own model training， develops transformation functions to perform conversions of field formats， data units， and data ranges. Additionally， interpolation is applied to handle missing values |
图6 联邦学习资源统筹模块示意注：TPU—张量处理器；NPU—神经网络计算处理器；GPU—图形处理器；CPU—中央处理器。
Fig. 6 Schematic diagram of tfederated learning resource coordination moduleNote： TPU—Tensor processing unit； NPU—Neural network processing unit； GPU—Graphic processing unit； CPU—central processing unit.
特征 Feature | 基于联邦学习的智慧农业系统 Smart agriculture system based on federated learning | 传统智慧农业系统 Traditional smart agriculture system |
|---|---|---|
数据来源 Data source | 可以从农业机构、学校、企业、各个农业平台（隐私保护后的模型梯度）和本地农场采集等获得数据 Collecting data from agricultural institutions， schools， businesses， various agricultural platforms （with privacy-protected model gradients） and local farms | 可以从农业机构、学校、企业和本地农场采集。其他农业平台较为隐私保密的数据无法获取利用 Collecting data from agricultural institutions， schools， businesses and local farms. Data from other agricultural platforms， which are more privacy-sensitive， cannot be accessed or utilized |
数据隐私和安全性 Data privacy and security | 参与方可以在本地更新模型梯度，发送给中心训练服务器时会使用隐私加密算法对梯度进行加密，可以减少数据隐私泄漏的风险 Participants can locally update model gradients， and when sending them to the central training server， privacy encryption algorithms will be used to encrypt the gradients， reducing the risk of data privacy leakage | 如果进行直接的数据共享，可能会存在着数据隐私泄漏的风险 If direct data sharing is carried out， there may be a risk of data privacy leakage |
模型泛化能力 Model generalization capability | 可以使用更多的数据进行模型训练，使得模型可以更好的适应不同的农业环境，提升模型性能 By utilizing a greater volume of data for model training， the model can better adapt to diverse agricultural environments， enhancing overall performance | 数据量不够大，可能在某些场景下会出现过拟合，使得模型性能波动较大 If the dataset is not sufficiently large， there may be instances of overfitting in certain scenarios， leading to significant fluctuations in model performance |
合规性和法规要求 Compliance and regulatory requirements | 联邦学习训练模型时，参与方可以在本地训练模型，不需要传输原始数据。本地训练模型过程中，可以通过同态加密和差分隐私的算法防止训练数据泄漏。这样的方式，可以满足大部分数据安全管理的法律法规要求 In federated learning model training， participants can train the model locally without the need to transmit raw data. During local model training， algorithms such as homomorphic encryption and differential privacy can be employed to prevent the leakage of training data. This approach ensures compliance with most legal and regulatory requirements for data security management | 在一些国家和地区，数据的共享受到严格的法律保护。传统智慧农业平台要想使用这批农业数据会非常的困难，甚至是无法使用 In some countries and regions， data sharing is subject to strict legal protection. Traditional smart agriculture platforms may find it challenging， or even impossible， to use this batch of agricultural data |
智慧农业系统的 可持续性 Sustainability of smart agriculture systems | 通过对数据的隐私保护，联邦学习可以打造更大的加密数据资源共享生态，为农业生产的可持续发展提供更好的支持 By ensuring privacy protection for data， federated learning can establish a larger ecosystem of encrypted data sharing， providing better support for the sustainable development of agricultural production | 需要考虑数据中心的可持续性 Need to consider the sustainability of data center |
表2 基于联邦学习的智慧农业系统与传统智慧农业系统对比
Table 2 Comparison between federated learning based smart agriculture systems and traditional smart agriculture systems
特征 Feature | 基于联邦学习的智慧农业系统 Smart agriculture system based on federated learning | 传统智慧农业系统 Traditional smart agriculture system |
|---|---|---|
数据来源 Data source | 可以从农业机构、学校、企业、各个农业平台（隐私保护后的模型梯度）和本地农场采集等获得数据 Collecting data from agricultural institutions， schools， businesses， various agricultural platforms （with privacy-protected model gradients） and local farms | 可以从农业机构、学校、企业和本地农场采集。其他农业平台较为隐私保密的数据无法获取利用 Collecting data from agricultural institutions， schools， businesses and local farms. Data from other agricultural platforms， which are more privacy-sensitive， cannot be accessed or utilized |
数据隐私和安全性 Data privacy and security | 参与方可以在本地更新模型梯度，发送给中心训练服务器时会使用隐私加密算法对梯度进行加密，可以减少数据隐私泄漏的风险 Participants can locally update model gradients， and when sending them to the central training server， privacy encryption algorithms will be used to encrypt the gradients， reducing the risk of data privacy leakage | 如果进行直接的数据共享，可能会存在着数据隐私泄漏的风险 If direct data sharing is carried out， there may be a risk of data privacy leakage |
模型泛化能力 Model generalization capability | 可以使用更多的数据进行模型训练，使得模型可以更好的适应不同的农业环境，提升模型性能 By utilizing a greater volume of data for model training， the model can better adapt to diverse agricultural environments， enhancing overall performance | 数据量不够大，可能在某些场景下会出现过拟合，使得模型性能波动较大 If the dataset is not sufficiently large， there may be instances of overfitting in certain scenarios， leading to significant fluctuations in model performance |
合规性和法规要求 Compliance and regulatory requirements | 联邦学习训练模型时，参与方可以在本地训练模型，不需要传输原始数据。本地训练模型过程中，可以通过同态加密和差分隐私的算法防止训练数据泄漏。这样的方式，可以满足大部分数据安全管理的法律法规要求 In federated learning model training， participants can train the model locally without the need to transmit raw data. During local model training， algorithms such as homomorphic encryption and differential privacy can be employed to prevent the leakage of training data. This approach ensures compliance with most legal and regulatory requirements for data security management | 在一些国家和地区，数据的共享受到严格的法律保护。传统智慧农业平台要想使用这批农业数据会非常的困难，甚至是无法使用 In some countries and regions， data sharing is subject to strict legal protection. Traditional smart agriculture platforms may find it challenging， or even impossible， to use this batch of agricultural data |
智慧农业系统的 可持续性 Sustainability of smart agriculture systems | 通过对数据的隐私保护，联邦学习可以打造更大的加密数据资源共享生态，为农业生产的可持续发展提供更好的支持 By ensuring privacy protection for data， federated learning can establish a larger ecosystem of encrypted data sharing， providing better support for the sustainable development of agricultural production | 需要考虑数据中心的可持续性 Need to consider the sustainability of data center |
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