Federated Learning (FL) is a distributed machine learning paradigm that enables collaborative model training across decentralized devices or servers without centralizing raw data. Originally introduced by Google in 2016 for improving Gboard's next-word prediction, FL has become foundational for privacy-preserving AI in healthcare, finance, IoT, and edge computing. Unlike traditional centralized learning where all data is uploaded to a central server, FL keeps data local while sharing only model updates—a critical distinction that preserves user privacy, complies with regulations like GDPR and HIPAA, and reduces communication overhead. The key challenge: achieving global model convergence despite heterogeneous data distributions, unreliable network connections, and resource-constrained devices requires sophisticated aggregation algorithms, communication-efficient protocols, and robust defenses against adversarial attacks.