Hierarchical Federated Learning Incentivization for Gas Usage Estimation
Has Sun Xiaoli Tang Chengyi Yang Zhenpeng Yu Xiuli Wang Qijie Ding Zengxiang Li Han Yu
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.