This article examines federated learning (FOE) as an innovative approach to machine learning, different from traditional methods. In conventional machine learning (MO), data is collected on a central server to train the model. However, in the case of FO, the learning model is directed to data distributed across local devices, and learning takes place directly on these devices. In addition, the article discusses methods and algorithms of federated learning, identifies the advantages and real areas of application of federated learning. FO is used in various fields, including working with medical data and personal data of customers in sales companies. This approach is especially valuable for ensuring data confidentiality and privacy.
key words: federated learning, machine learning, local devices, Internet of Things, artificial intelligence.
