FLEE-GNN: a federated learning system for edge-enhanced graph neural network in analyzing geospatial resilience of multicommodity food flows

Authors: Yuxiao Qu, Jinmeng Rao, Song Gao, Qianheng Zhang, Wei-Lun Chao, Yu Su, Michelle Miller, Alfonso Morales, Patrick R Huber

Publication date: 2023/11/13 Book: Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery Pages: 63-72 PDF: Available from ACM

Abstract: 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 networks, which are a type of spatial network. 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 for food supply network resilience analysis across geographical regions.