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

arXiv: https://arxiv.org/abs/2404.18527 · HTML: https://mlsanity.com/paper/2404.18527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 74 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019)
- [FedKD: Communication Efficient Federated Learning via Knowledge Distillation](https://mlsanity.com/paper/2108.13323/) (2021)
- [Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://mlsanity.com/paper/1909.00907/) (2019)
- [Continual Local Training for Better Initialization of Federated Models](https://mlsanity.com/paper/2005.12657/) (2020)
- [Ensemble Neural Networks (ENN): A gradient-free stochastic method](https://mlsanity.com/paper/1908.01113/) (2019)

## Abstract (arXiv)

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.