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Private Computation Space: Experience with Trusted Multi-Cluster Federated Learning for Agriculture — arXiv:2609.01667

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.01667
License (as archived): CC0-1.0
Archived: 2026-10-17T04:40:48.887Z
SHA-256 of the archived text: 24b0c8da1fff9bb4a6a958fa4ef701325399c33be0efa9094d441dc60dd78e66
Stable URL of this page: https://metraipolis.com/documento/dc_4a45b02c8aee

How to cite

Private Computation Space: Experience with Trusted Multi-Cluster Federated Learning for Agriculture — arXiv:2609.01667 — arXiv (en), https://arxiv.org/abs/2609.01667 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4a45b02c8aee (Metraipolis archive dc_4a45b02c8aee, sha256 24b0c8da1fff9bb4a6a958fa4ef701325399c33be0efa9094d441dc60dd78e66)

BibTeX

@misc{dc4a45b02c8aee,
  title = {Private Computation Space: Experience with Trusted Multi-Cluster Federated Learning for Agriculture — arXiv:2609.01667},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.01667},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4a45b02c8aee; sha256 24b0c8da1fff9bb4a6a958fa4ef701325399c33be0efa9094d441dc60dd78e66},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.01667. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: fiducia digitale e crittografia (retro-catalogo arXiv: annunci del 2026-09-03 in cs.CR).
Categorie arXiv: Cryptography and Security (cs.CR) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.01667 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T04:09:09.334Z dalla pagina https://arxiv.org/catchup/cs.CR/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).