Federated learning is a machine learning approach that enables multiple parties to collaborate on a common machine learning model without sharing their raw data. Instead, each party trains a local model on their own dataset and only shares model updates with a central server or coordinator. These updates are then aggregated to improve the global model without compromising data privacy. Federated learning is particularly useful in scenarios where data cannot be easily centralized due to privacy concerns, regulatory constraints, or limited network bandwidth. It allows organizations to leverage the collective knowledge embedded in their data without directly accessing or sharing it. This research area has gained significant attention in recent years, with applications in various domains such as healthcare, finance, and telecommunications. It presents unique challenges related to communication efficiency, model aggregation, and privacy preservation, making it an active area of research in the machine learning community.