Private Computation Space: Experience with Trusted Multi-Cluster Federated Learning for Agriculture
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 Ma...
| Authors | Shuangyu Lei, Muhammad Salman Abid, Jacob Belding et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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