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Federated Learning (FL) is a decentralized machine learning paradigm that enables multiple clients to collaboratively train a global model while preserving data privacy. Unlike traditional centralized learning, FL ensures that raw data remains local, with only model updates being shared, thereby mitigating privacy risks and reducing communication overhead. FL has gained significant traction in research for its applications in privacy-preserving AI and efficiency in distributed systems, making it particularly valuable in domains such as healthcare, finance, and edge computing.