In today’s interconnected world, where data is generated at an unprecedented scale across distributed systems, there is a growing need for efficient, collaborative, and privacy-preserving machine learning techniques. Federated learning (FL) has emerged as a groundbreaking paradigm, enabling multiple devices or systems to collaboratively train machine learning models without transferring sensitive raw data to a central location. This decentralized approach not only ensures privacy but also reduces the computational and communication overhead associated with traditional centralized learning models.