HierFedCEA: Hierarchical Federated Edge Learning for Privacy-Preserving Climate Control Optimization Across Heterogeneous Controlled Environment Agriculture Facilities
This work addresses the critical need for cross-facility knowledge transfer in CEA while preserving proprietary grow recipe privacy, reducing HVAC energy by 30-38% and commissioning time from months to days.
HierFedCEA enables privacy-preserving climate control optimization across heterogeneous CEA facilities, achieving 94% of centralized training performance with under 1 MB communication cost and excess privacy risk < 0.15%.
Cross-facility knowledge transfer in Controlled Environment Agriculture (CEA) can reduce HVAC energy consumption by 30-38% and accelerate new facility commissioning from months to days. However, facility operators refuse to share raw operational data because it encodes commercially sensitive grow recipes. We present HierFedCEA, a hierarchical federated learning framework that enables privacy-preserving climate control optimization across heterogeneous CEA facilities. HierFedCEA decomposes the neural network PID auto-tuning model into three tiers aligned with the physical structure of the control problem: (1) a global physics tier capturing universal thermodynamic relationships; (2) a crop-cluster tier encoding cultivar-specific VPD-to-gain mappings; and (3) a local personalization tier adapting to facility-specific equipment dynamics. The framework applies tier-specific differential privacy budgets and leverages the extreme compactness of the 36-parameter PID model to achieve privacy essentially for free (excess risk < 0.15%). Simulation experiments calibrated from 7+ years of production deployment across 30+ commercial facilities in 8 U.S. climate zones demonstrate that HierFedCEA achieves 94% of centralized training performance while reducing total communication cost to under 1 MB. To the best of our knowledge, this is the first federated learning framework designed for CEA climate control.