Modern supply chains operate as highly interconnected networks characterized by decentralization, data silos, and increasing sustainability constraints. Although Graph Neural Networks (GNNs) have demonstrated strong capability in modeling relational dependencies in such systems, their deployment is often restricted by limited inter-organizational data sharing. Federated learning (FL) enables collaborative model training without exposing proprietary data; however, existing federated approaches rarely integrate graph structure and sustainability objectives within a unified framework. This study proposes a Sustainability-Aware Federated Graph Attention Network (FedGAT) for decentralized supply chain process modeling. The framework combines Graph Attention Networks with federated optimization and introduces an emission-weighted attention modulation mechanism that embeds environmental considerations directly into the message-passing process. A multi-tier synthetic supply chain benchmark is constructed to evaluate the approach under realistic governance and data-locality constraints. Experiments are conducted across multiple random seeds, graph scales (up to 500 nodes), and client partition settings. Results show that while centralized graph learning achieves the lowest prediction error, the proposed sustainability-aware federated model maintains statistically indistinguishable predictive performance compared to standard federated baselines (paired sign test p = 1.000), while systematically reducing attention allocated to high-emission transport links. A structured label sensitivity analysis confirms that performance gains are not attributable to circular label construction. Furthermore, a λ-ablation study demonstrates a smooth and controllable trade-off between predictive accuracy and sustainability alignment through a single governance parameter. These findings establish the feasibility of privacy-preserving, sustainability-modulated graph learning for decentralized supply chain analytics and provides a principled foundation for environmentally aligned AI deployment in multi-enterprise networks.
Alexiadis et al. (Fri,) studied this question.