Introduction: In the age of data privacy concerns and the need for collaborative machine learning, a novel approach called federated learning has emerged. Federated learning enables training machine learning models on decentralized data sources while preserving privacy. This groundbreaking technique has the potential to revolutionize various domains, from healthcare to finance and beyond. In this blog post, we will provide an in-depth introduction to federated learning, discussing its principles, benefits, and implementation in Python. What is Federated Learning?  Definition and Concept: Federated learning is a distributed learning approach where the training process takes place on decentralized devices or servers instead of a central location. The data remains on the local devices, and only model updates are shared. Centralized vs. Federated Learning: Unlike traditional centralized machine learning, federated learning brings the model to the data instead of sending data to a central