Accurately estimating gas usage is essential for the efficient functioning of gas distribution networks and saving operational costs. Traditional methods rely on centralized data processing, which poses privacy risks. Federated learning (FL) offers a solution to this problem by enabling local data processing on each participant, such as gas companies and heating stations. However, local training and communication overhead may discourage gas companies and heating stations from actively participating in the FL training process. To address this challenge, we propose a Hierarchical FL Incentive Mechanism for Gas Usage Estimation (HI-GAS), which has been testbedded in the ENN Group, one of the leading players in the natural gas and green energy industry. It is designed to support horizontal FL among gas companies, and vertical FL among each gas company and heating station within a hierarchical FL ecosystem, rewarding participants based on their contributions to FL. In addition, a hierarchical FL model aggregation approach is also proposed to improve the gas usage estimation performance by aggregating models at different levels of the hierarchy. The incentive scheme employs a multi-dimensional contribution-aware reward distribution function that combines the evaluation of data quality and model contribution to incentivize both gas companies and heating stations within their jurisdiction while maintaining fairness. Results of extensive experiments validate the effectiveness of the proposed mechanism.

翻译：准确估计天然气用量对于配气网络的高效运行和降低运营成本至关重要。传统方法依赖集中式数据处理，存在隐私风险。联邦学习（FL）通过允许每个参与者（如燃气公司和供热站）在本地处理数据，为解决该问题提供了方案。然而，本地训练与通信开销可能阻碍燃气公司和供热站积极参与联邦学习训练过程。针对这一挑战，我们提出了一种面向天然气用量估计的分层联邦学习激励机制（HI-GAS），该机制已在天然气与绿色能源行业领军企业新奥集团完成测试部署。该系统支持燃气公司间的水平联邦学习，以及分层联邦学习生态中各燃气公司与其管辖供热站之间的垂直联邦学习，并根据参与者对联邦学习的贡献进行奖励。此外，我们还提出了一种分层联邦学习模型聚合方法，通过在不同层级聚合模型来提升天然气用量估计性能。该激励机制采用多维度贡献感知的奖励分配函数，结合数据质量与模型贡献评估，在保持公平性的同时激励燃气公司及其管辖范围内的供热站。大量实验结果验证了所提机制的有效性。