A Hybrid Approach for Improving Machine Learning Models Using Federated Learning and Ensemble Techniques

Abstract

To have robust software systems, software reliability prediction plays a major role. A hybrid and advanced design to enhance the quality and performance of machine learning models are proposed in this research; it consists of different steps of data preprocessing, feature engineering, model refinement with the assistance of federated learning, hyperparameter optimization, and the hybrid learning methods. One initially works with the data with the help of algorithms like SMOTE and SMOTEENN to address the issue of uneven data, and then the elements are resolved and adjusted in an innovative manner. The ensemble learning and hyperparameter optimization are then used to optimize the models. Specifically, data privacy in the distributed models is maintained using the federated learning approach. Lastly, generated models are tested on complex data with the help of deep neural nets (DNN) and their performance is measured with the means of precision, recall, prediction accuracy, and F1 score. The resulting methods, which are mainly useful in assisting to enhance the efficiency and prediction accuracy of machine learning models, apply in complex problems.