Secure and Distributed Edge Intelligence for Consumer- Grade Smart Agriculture

Consumer-grade Agricultural AIoT (Agri-AIoT) systems increasingly rely on cloud-based intelligence, which introduces latency, privacy, and connectivity limitations. These limitations are particularly severe for heterogeneous consumer devices operating under strict cost, energy, and computational constraints. This review advocates a shift from cloud-centric architectures toward distributed, edge-centric intelligence. We examine lightweight model compression techniques, including pruning, quantization, and knowledge distillation, that enable real-time and on-device decision-making. We further analyze security threats such as data poisoning and adversarial attacks that arise in decentralized agricultural systems. Privacy-preserving learning mechanisms, including Federated Learning, are discussed as key enablers of collaborative intelligence without raw data sharing. By integrating lightweight Artificial Intelligence techniques with AI-native networking principles, this paper provides a unified perspective on distributed intelligence in consumer Agri-AIoT ecosystems. We conclude that the convergence of these approaches is essential for building sustainable, secure, and self-adaptive consumer-grade agricultural electronics.