用联邦学习提升玉米病叶识别隐私保护，首次评估其在农业中的适用性。
Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction
- 在分布式环境下训练5种CNN模型，避免数据集中上传
- 实验表明联邦学习在异构数据中保持较高分类准确率
- 适合关注数据隐私的农业智能诊断场景
基于机器学习的粮食作物病害诊断已显示出良好效果，卷积神经网络（CNN）在作物叶片图像识别方面表现准确，且文献中已有广泛改进。然而，这些方法在数据隐私方面存在不足，因需将数据上传至中心服务器进行训练，违背了竞争或监管要求。为此，联邦学习（FL）旨在支持分布式训练以弥补集中式训练的缺陷。据我们所知，本文首次开展并评估了联邦学习在玉米叶病识别中的应用。我们评估了五种在分布式范式下训练的CNN模型性能，并对比了其训练时间与分类表现。同时，考虑了分布式训练在通信流量和各CNN参数量方面的适用性。结果表明，联邦学习在异构领域中有望显著增强数据隐私保护。
原文摘要 · Abstract (English)
The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture of the crop leaf, being extensively enhanced in the literature. These machine learning techniques fall short in data privacy, as they require sharing the data in the training process with a central server, disregarding competitive or regulatory concerns. Thus, Federated Learning (FL) aims to support distributed training to address recognized gaps in centralized training. As far as we know, this paper inaugurates the use and evaluation of FL applied in maize leaf diseases. We evaluated the performance of five CNNs trained under the distributed paradigm and measured their training time compared to the classification performance. In addition, we consider the suitability of distributed training considering the volume of network traffic and the number of parameters of each CNN. Our results indicate that FL potentially enhances data privacy in heterogeneous domains.
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