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Understanding and measuring the resilience of food supply networks is aglobal imperative to tackle increasing food insecurity. However, the complexityof these networks, with their multidimensional interactions and decisions,presents significant challenges. This paper proposes FLEE-GNN, a novelFederated Learning System for Edge-Enhanced Graph Neural Network, designed toovercome these challenges and enhance the analysis of geospatial resilience ofmulticommodity food flow network, which is one type of spatial networks.FLEE-GNN addresses the limitations of current methodologies, such asentropy-based methods, in terms of generalizability, scalability, and dataprivacy. It combines the robustness and adaptability of graph neural networkswith the privacy-conscious and decentralized aspects of federated learning onfood supply network resilience analysis across geographical regions. This paperalso discusses FLEE-GNN's innovative data generation techniques, experimentaldesigns, and future directions for improvement. The results show theadvancements of this approach to quantifying the resilience of multicommodityfood flow networks, contributing to efforts towards ensuring global foodsecurity using AI methods. The developed FLEE-GNN has the potential to beapplied in other spatial networks with spatially heterogeneous sub-networkdistributions.

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