Conventional approaches to Internet of Things (IoT) device coordination often exhibit rigidity, lacking adaptability in modifying cooperative relationships between devices when confronted with dynamic operational conditions. This study proposes a collaborative optimization framework integrating Graph Neural Networks (GNNs) with federated learning methodologies. The implemented solution models IoT nodes and their interaction patterns as graph-structured representations, subsequently employing distributed machine learning techniques to train Graph Convolutional Networks (GCNs) using decentralized data sources. Experimental evaluations demonstrated that the federated graph network model achieved an aggregate Mean Squared Error (MSE) of 0.968 with a standard deviation of 0.0353 during training, reaching convergence within 435.82 seconds. Notably, computational resource allocation analysis revealed model training constituted 72.9% of total processing time versus 27.1% for data transmission. Practical implementation in smart home environments demonstrated operational efficacy through maintaining desired environmental conditions for 87 minutes during a 120-minute test cycle while reducing energy consumption by 0.69 kW·h. Comparative analysis with centralized learning approaches indicates this method enhances cooperative efficiency while minimizing computational overhead, though it presents limitations in predictive accuracy enhancement and introduces potential stability trade-offs during distributed model aggregation phases.

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