示威者(Demonstrator)
As a demonstrator for federated learning applications, Digital Catapult has chosen to ground or work on a use case stemming from our observations in prior works with the agricultural sector. Crop scouting and diseases outbreak detection is an area where data could be leveraged to generate actionable insights for growers to improve productivity, reduce costs and enable more environmentally friendly treatment methods. In practice, gathering consistent data in quantity is costly and impractical for growers, because of the manual work involved, the lack of adequate tools and the expertise required. The diversity and range of crops and diseases observable is also limited for any grower. This operational data is also viewed as sensitive and sharing it represents a competitive risk, so attempts to bring consortium of growers to gather their private data has proven to be difficult. This represents a perfect use-case for FL.
作为联邦学习应用程序的演示者,Digital Catapult已选择根据我们在农业领域的先前工作中的观察得出的用例进行研究或开展工作。 作物侦查和疾病暴发检测是一个领域,可以利用数据为种植者提供可行的见解,以提高生产力,降低成本并采用更环保的治疗方法。 在实践中,由于涉及手工操作,缺乏足够的工具和所需的专业知识,因此收集一致的数据对于种植者而言既昂贵又不切实际。 可观察到的农作物和病害的多样性和范围也对任何种植者都是有限的。 该操作数据也被视为敏感数据,并且共享它代表着竞争风险,因此,试图让种植者财团来收集其私人数据非常困难。 这代表了FL的完美用例。
Using an open-source images dataset namedPlantVillage, we have simulated a collaborative ensemble of growers, shown in Figure 1, contributing to the development of a global model with their own private datasets of crops leaves presenting symptoms of various diseases. Using our own platform independent implementation of FL and the Federated Average algorithm, we demonstrated the potential for FL to achieve state of the art performance on this computer vision task. Each growers would consequently benefit from an increased performance and better generalisation power of the ML model, with a solution running on small and inexpensive edge computing devices, jetson nanos. We dive into the technical details of this FL library in an upcoming blog post.
使用名为PlantVillage的开源图像数据集,我们模拟了种植者的协作集合,如图1所示,这有助于建立具有自己的农作物叶片私有数据集的全球模型,该数据集呈现出各种疾病的症状。 使用我们自己的平台独立实现的FL和联合平均算法,我们展示了FL在此计算机视觉任务上实现最新性能的潜力。 因此,每个种植者都将受益于ML模型更高的性能和更强的泛化能力,并且该解决方案可以在小型廉价的边缘计算设备jetson nanos上运行。 我们将在即将发布的博客文章中深入探讨该FL库的技术细节。