Federated learning is a distributed machine learning approach that allows models to be trained on decentralized data sources while preserving data privacy. This technique is particularly useful for applications such as IoT and edge computing, where data is generated by a large number of devices in different locations. Federated learning enables real-time decision-making and more efficient data processing, making it an important tool in the era of big data and IoT.