Privacy-Preserving Active Learning for smart agriculture microgrid orchestration with ethical auditability baked in

Short summary

The author built a privacy-preserving active learning framework for agricultural microgrids, combining quantized neural networks on edge devices, differential privacy with domain constraints, and zero-knowledge proofs for auditability. Edge devices compute local entropy without exposing raw sensor data; the central model queries only compressed signals. Testing showed 12% accuracy improvement with adaptive noise compared to fixed privacy budgets.

• •Local uncertainty estimation using quantized neural networks on edge devices avoids sending raw farm data to central servers
• •Differential privacy with domain-aware constraints prevents information leakage while respecting physical microgrid limits
• •Zero-knowledge proofs enable full auditability of all decisions without revealing underlying data

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