Federated learning (FL) represents a revolutionary approach to machine learning, particularly addressing data privacy and security challenges in industrial applications. The chapter the concept and implementation framework of FL and provides practical implications more precise than traditional centralized machine learning methods. FL allows collaborative models to be trained on nodes without data centralization, thus supporting data privacy and enhancing security. The chapter discusses different classifications of FL based on data classification and learning models and provides a mathematical framework to identify integrated learning models. Furthermore, it explores the importance of FL in data governance and addressing data silo issues, in particular under complex regulations such as the GDPR. FL has the potential to be applied in various areas such as health care, processing natural languages, computer vision, autonomous vehicles, and IoT. Integration of cryptographic solutions with customer security measures highlights FL’s ability to maintain data integrity and privacy in data-sensitive environments paving the way for greater adoption.