Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning

TL;DR

This paper presents a federated learning framework using XGBoost and Bayesian Optimization for privacy-preserving, accurate geoenergy modeling across multiple parties, overcoming data barriers and enhancing collaboration.

Contribution

It introduces a novel FL-XGBoost framework with hyperparameter tuning for collaborative geoenergy modeling, balancing privacy and accuracy.

Findings

FL models outperform separate models in accuracy and generalization.

FL provides significant privacy advantages over centralized models.

Hyperparameter tuning via Bayesian Optimization is effective within FL.

Abstract

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.