  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.
