Privacy-Preserving AI for Smart Farm Microgrids with Built-In Auditability

A developer proposes a framework combining quantized neural networks, differential privacy, and zero-knowledge proofs to train AI for optimizing farm microgrids without exposing sensitive data. The system lets edge devices compute local uncertainty and share only compressed model uncertainty representations. Every decision is recorded on a cryptographic ledger for ethical auditability.