Privacy-Preserving Consumer Churn Prediction in Telecommunication Through Federated Machine Learning

In the competitive telecommunications industry, understanding and predicting customer churn-customers discontinuing service-is crucial for revenue and subscriber retention. Traditional customer churn prediction (CCP) methods require extensive user data, raising privacy concerns when sharing data across different companies. This paper introduces a novel federated learning (FL) framework for CCP that enhances prediction accuracy while safeguarding privacy.