CPS: Medium: Connected Federated Farms: Privacy-Preserving Cyber Infrastructure for Collaborative Smart Farming

随着传感技术的进步，农业农场管理已经转变为一个数据驱动的过程。在农场收集的数据使人工智能（AI）框架能够开发能够预测作物产量和健康状况等特征的模型，从而实现基于数据的决策。然而，在目前的实践状态下，这些智能农场是孤立的，仅根据从一个农场获得的数据开发人工智能模型，而忽略了其他农场产生的数据。农场之间缺乏协作导致模型的泛化能力有限，并直接影响农场管理决策。虽然将农场网络中的数据汇集到中央服务器以生成更健壮的模型是可能的，但由于数据隐私问题，大多数农民不愿分享他们的数据。因此，该项目旨在开发一种新的整体框架，允许农场之间的协作，保护隐私，并鼓励在数据驱动的农场建模中同时进行协作和个性化。所构建的模型可用于农场决策和管理。该框架将减轻农民对数据隐私的担忧，从而进一步采用智能农业技术。因此，该项目可能会导致农场更普遍地使用数字工具，改善管理决策并提高农场生产力。最终，数字解决方案的接受和使用将提高食品质量，减少环境足迹。预计将作出若干教育和外联努力，将研究纳入本科和研究生课程，并扩大代表性不足群体的参与。该项目旨在为智能农业农场中常见的高维和大数据开发一个联合分析框架。该项目将设计一种新颖的基于联合鲁棒张量的建模范式，能够利用智能农场数据集的时空结构。当使用建议的方法时，每个农场创建一个本地模型，然后将该模型传输到聚合器，聚合器创建一个聚合模型。然后将聚合模型广播到每个农场，以生成支持本地决策的个性化模型。张量模型的低维嵌入允许减少农场和聚合器之间的模型通信。将研究不同的隐私方法，以增强所提出框架的隐私保护特性。开发的人工智能增强的连接多农场系统将作为案例研究在柑橘上进行测试。所提出的框架可以为其他领域做出贡献，例如多农场可再生能源系统和多设施先进制造系统的建模和监测。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With the advancements in sensing technologies, agricultural farm management has transformed into a data-enabled process. Data collected at farms enabled artificial intelligence (AI) frameworks to develop models capable of predicting traits such as crop yields and health conditions, allowing for data-informed decision-making. However, in the current state of practice, these smart farms are siloed, developing AI models solely based on data obtained from a farm, ignoring the data generated in other farms. This lack of collaboration among farms results in limited generalization capability of models and directly impacts farm management decisions. While pooling data from a network of farms into a centralized server to generate more robust models is possible, most farmers are reluctant to share their data due to data privacy concerns. Therefore, this project aims to develop a novel holistic framework that allows for collaboration between farms, preserves privacy, and encourages simultaneous collaboration and personalization in the data-driven modeling of agricultural farms. The constructed models are used in farm decision-making and management. This framework will alleviate farmers’ data privacy concerns, resulting in further adoption of smart farming technologies. Therefore, the project may result in the more prevalent use of digital tools by farms, improving management decisions and increasing farm productivity. Eventually, the acceptance and use of digital solutions will enhance food quality and decrease the environmental footprint. Several educational and outreach efforts for the integration of research into undergraduate and graduate courses and broadening the participation of underrepresented groups are envisioned.The project aims to develop a federated analytics framework for high-dimensional and big data common in smart agricultural farms. The project will design a novel federated robust tensor-based modeling paradigm that enables exploiting the spatiotemporal structure of smart farm datasets. When the proposed approach is used, each farm creates a local model that is then transmitted to an aggregator, which creates an aggregated model. The aggregated model is then broadcast to each farm to generate a personalized model that supports local decision-making. The low-dimensional embedding of the tensor model allows for reduced model communication between the farms and the aggregator. Differential privacy approaches will be investigated to enhance the privacy-preservation properties of the proposed framework. The developed AI-enhanced connected multi-farm system will be tested in citrus as a case study. The proposed framework can contribute to other areas, such as modeling and monitoring multi-farm renewable energy systems and multi-facility advanced manufacturing systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.