FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows

Within the networks is a global imperative to tackle increasing food agrifood systems, food supply networks are pivotal in upholding insecurity. However, the complexity of these networks, with their global food security and facilitating the transit, dissemination, multidimensional interactions and decisions, presents significant and sale of food. It's imperative that these networks demonstrate challenges. This paper proposes FLEE-GNN, a novel Federated resilience and sturdiness [12, 15, 21]. Learning System for Edge-Enhanced Graph Neural Network, However, the complexity inherent in them, arising from designed to overcome these challenges and enhance the analysis of diverse food needs, shipment timeframes and costs, promotional geospatial resilience of multicommodity food flow network, which strategies, cultural and environmental considerations, among is one type of spatial networks. FLEE-GNN addresses the limitations others, complicates the assessment of their durability and of current methodologies, such as entropy-based methods, in terms adaptability [2, 23]. Given the intricate nature of food supply of generalizability, scalability, and data privacy. It combines the networks, the concept of resilience is often interpreted in diverse robustness and adaptability of graph neural networks with the ways by different individuals and groups [6, 12, 16, 22]. The term privacy-conscious and decentralized aspects of federated learning "resilience" in this study predominantly pertains to the capacity of on food supply network resilience analysis across geographical the food flow networks to sustain essential food supplies across regions.