发表机构
Cornell University; IBM Research; University of Maryland（康奈尔大学; IBM研究院; 马里兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对美国农民数据隐私担忧限制AI在农业应用的问题，推出开源私有计算空间PCS，结合多集群编排、异步FL、DP和TEEs，在两项农业任务中提升模型准确率并保护隐私。
AI 中文摘要
人工智能已被证实有助于改进农业实践，但其应用仍受到限制：69%的美国农民对共享数据存在隐私担忧，而这些担忧必须得到解决才能实现广泛应用。虽然联邦学习已被证明可为其他行业大规模保护隐私，但在农业领域部署系统面临独特挑战；该问题需要一个既能保护农民数据和身份、同时保留模型效用、可在普通硬件上运行且能适应脆弱农村基础设施的系统。为解决这些担忧，我们推出了私有计算空间（Private Computation Space, PCS），这是一个已部署的开源机器学习系统，用于安全配置和处理农民数据。我们设计了适配农业场景的系统，采用多集群编排以保障农村地区的可靠性，结合异步联邦学习（Federated Learning, FL）、差分隐私（Differential Privacy, DP）和可信执行环境（Trusted Execution Environments, TEEs），使农场能够参与该框架同时保护其数据隐私。我们在两个已部署的工作负载上评估该系统：纽约州利用活体植物传感器监测氮含量，持续六个月；加利福尼亚州利用气象站预测蒸散量，持续十个月。评估发现，对应工作负载的戴斯相似系数（Dice Similarity Coefficient, DSC）为0.71，R²准确率为0.84，在保留隐私的同时，分别将最差单站点模型的准确率提升了22.4%和9.1%。
英文摘要
Artificial Intelligence has shown to help improve agricultural practices, yet adoption remains limited: 69% of U.S. farmers have privacy concerns with sharing their data, and these concerns must be addressed before adoption is widespread. While Federated Learning has been demonstrated to protect privacy at scale for other sectors, deploying a system for agriculture comes with its own set of challenges; the problem necessitates a system that can protect farmer data and identities while preserving model utility, runs on commodity hardware, and is resilient to fragile rural infrastructure. To address these concerns, we introduce the Private Computation Space (PCS), a deployed, open-source Machine Learning system to provision and process farmer data securely. We design a system tailored to an agricultural setting, with multi-cluster orchestration for reliability in rural areas with asynchronous Federated Learning (FL), Differential Privacy (DP), and Trusted Execution Environments (TEEs), to allow farms to participate in the framework while keeping their data private. We evaluate the system on two deployed workloads: monitoring nitrogen with living plant sensors in NY for six months and predicting evapotranspiration from weather stations in CA for ten months. Our evaluation finds a Dice Similarity Coefficient (DSC) of 0.71 and $R^2$ accuracy of 0.84 for the respective workloads, improving the worst single-site model accuracy by 22.4% and 9.1%, respectively, while preserving privacy.
Comments15 pages, 13 figures, 3 tables
