The role of federated learning in machine learning is to enable decentralized model training while preserving data privacy. Instead of collecting data in a central server, federated learning allows devices to train a model locally on their own data, sharing only model updates instead of raw data. This approach enhances data security and privacy, which is particularly crucial in sensitive sectors like healthcare and finance. It enables collaborative learning across multiple devices while maintaining confidentiality.