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arXiv Machine Learning By Shuangyu Lei, Muhammad Salman Abid, Jacob Belding, Sam Mosher, Manushi B. Trivedi, Shivranjani Baruah, Liam Wickes-Do, Andrew Anderson, Braulio Dumba, Alyssa Whitcraft, Ritvik Sahajpal, Sijin Li, Kelly Robbins, Michael Gore, Margaret Frank, Steven Wolf, Liz Jones, Abraham Stroock, Kaitlin Gold, Hakim Weatherspoon

Private Computation Space: Experience with Trusted Multi-Cluster Federated Learning for Agriculture

Read the original on arXiv Machine Learning →

The paper introduces the Private Computation Space (PCS), an open‑source federated learning system designed for agriculture that protects farmer data using asynchronous federated learning, differential privacy, and trusted execution environments. PCS runs on commodity hardware and is resilient to rural infrastructure challenges. In two real‑world deployments—nitrogen monitoring in New York and evapotranspiration prediction in California—PCS achieved a Dice Similarity Coefficient of 0.71 and an $R^2$ of 0.84, improving single‑site model accuracy by 22.4% and 9.1% respectively while preserving privacy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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