Causal Federated Graph Neural Networks for Multiobjective Facility Location

Xueming Yan, Yaochu Jin, Chuyue Wang, Shangshang Yang

Abstract: Multiobjective facility location problems (MO-FLPs) are common in real-world applications, involving tradeoffs among cost, reliability, and service quality. Recent advances in deep learning have shown potential in solving MO-FLPs; however, existing approaches often require centralized data, which is impractical due to privacy constraints across distributed data owners. To address this issue, we propose a causally federated graph neural network (CFGNN) for solving MO-FLPs in a privacy-preserving manner. We represent MO-FLPs as bipartite graphs to capture relationships between facility sites and customer zones. On each client, dual graph neural networks (GNNs) learn representations of nodes and edges, while a causal instance graph extracts stable interinstance relationships. On the server side, a federated causal hypergraph module facilitates collaborative learning without compromising data privacy. In addition, a multilayer perceptron (MLP) surrogate model with causal embeddings generates approximate Pareto-optimal solutions. Extensive experiments on a newly constructed benchmark dataset with different scales demonstrate that CFGNN achieves superior solution quality and generalization performance compared to state-of-the-art approaches.

External IDs:doi:10.1109/tsmc.2025.3647753