Federated machine learning (FML) allows collaborative model training via parameter exchange, ensuring privacy and compliance. A device–edge–cloud framework, with edge servers as intermediaries, enhances accuracy, resource balance, and scalability, requiring standardized protocols plus strategies for resource use, privacy, and aggregation. This Recommendation introduces the framework and requirements for device-edge-cloud collaborative FML, and specifies its concept, procedure, requirements and use cases.