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

arXiv:2404.18527v1 Announce Type: new
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 …

abstract algorithms arxiv collection concerns costs cs.ai cs.lg data enabling energy federated learning fields framework machine machine learning machine learning algorithms novel practical privacy safe stat.ap stemming study through type xgboost