HierFedCEA delivers a hierarchical federated learning framework for privacy-preserving climate control optimization across heterogeneous CEA facilities, reaching 94% of centralized performance with under 1 MB communication.
Communication-efficient learning of deep networks from decentralized data
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Conditioning a global FL model on local PCA statistics of client data matches oracle cluster performance across heterogeneous settings and is robust to sparse data with zero added communication.
citing papers explorer
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HierFedCEA: Hierarchical Federated Edge Learning for Privacy-Preserving Climate Control Optimization Across Heterogeneous Controlled Environment Agriculture Facilities
HierFedCEA delivers a hierarchical federated learning framework for privacy-preserving climate control optimization across heterogeneous CEA facilities, reaching 94% of centralized performance with under 1 MB communication.
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Client-Conditional Federated Learning via Local Training Data Statistics
Conditioning a global FL model on local PCA statistics of client data matches oracle cluster performance across heterogeneous settings and is robust to sparse data with zero added communication.