HFedRF: Horizontal Federated Random Forest

Real-world data is typically dispersed among numerous businesses or governmental agencies, making it difficult to integrate them into data privacy laws like the General Data Protection Regulation of the European Union (GDPR). Two significant obstacles to the use of machine learning models in applications are the existence of such data islands and privacy issues. In this paper, we address these issues and propose ‘HFedRF: Horizontal Federated Random Forest’, a privacy-preserving federated model which is approximately lossless. Our proposed algorithm merges d random forests computed on d different devices and returns a global random forest which is used for prediction on local devices. In our methodology, we compare IIDs (Independent and Identically Distributed) and non-IIDs variant of our algorithm HFedRF with traditional machine learning (ML) methods i.e., decision tree and random forest. Our results show that we achieve benchmark comparable results with our algorithm for IID as well as non-IID settings of federated learning.