FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural
  Network in Analyzing Geospatial Resilience of Multicommodity Food Flows
Yuxiao Qu Jinmeng Rao Song Gao Qianheng Zhang Wei-Lun Chao Yu Su Michelle Miller Alfonso Morales Patrick Huber
Understanding and measuring the resilience of food supply networks is a
global imperative to tackle increasing food insecurity. However, the complexity
of these networks, with their multidimensional interactions and decisions,
presents significant challenges. This paper proposes FLEE-GNN, a novel
Federated Learning System for Edge-Enhanced Graph Neural Network, designed to
overcome these challenges and enhance the analysis of geospatial resilience of
multicommodity food flow network, which is one type of spatial networks.
FLEE-GNN addresses the limitations of current methodologies, such as
entropy-based methods, in terms of generalizability, scalability, and data
privacy. It combines the robustness and adaptability of graph neural networks
with the privacy-conscious and decentralized aspects of federated learning on
food supply network resilience analysis across geographical regions. This paper
also discusses FLEE-GNN's innovative data generation techniques, experimental
designs, and future directions for improvement. The results show the
advancements of this approach to quantifying the resilience of multicommodity
food flow networks, contributing to efforts towards ensuring global food
security using AI methods. The developed FLEE-GNN has the potential to be
applied in other spatial networks with spatially heterogeneous sub-network
distributions.