Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning
It addresses data privacy and collaboration challenges for participants in the geoenergy sector, though it is incremental as it applies existing federated learning to a specific domain.
The study tackled data barriers in geoenergy by proposing a federated learning framework based on XGBoost, which achieved superior accuracy and generalization compared to separate models while offering privacy benefits over centralized approaches.
Machine learning algorithms emerge as a promising approach in energy fields, but its practical is hindered by data barriers, stemming from high collection costs and privacy concerns. This study introduces a novel federated learning (FL) framework based on XGBoost models, enabling safe collaborative modeling with accessible yet concealed data from multiple parties. Hyperparameter tuning of the models is achieved through Bayesian Optimization. To ascertain the merits of the proposed FL-XGBoost method, a comparative analysis is conducted between separate and centralized models to address a classical binary classification problem in geoenergy sector. The results reveal that the proposed FL framework strikes an optimal balance between privacy and accuracy. FL models demonstrate superior accuracy and generalization capabilities compared to separate models, particularly for participants with limited data or low correlation features and offers significant privacy benefits compared to centralized model. The aggregated optimization approach within the FL agreement proves effective in tuning hyperparameters. This study opens new avenues for assessing unconventional reservoirs through collaborative and privacy-preserving FL techniques.