Federated machine learning (FML) is an emerging distributed machine learning paradigm that enables collaborative model training, learning, utilizing and construction from a large number of distributed datasets on the basis of ensuring data security and legal compliance. It performs where the computing is where the data, and data available is not visible and so is data computing. There are some challenges for FML-based services in aspects of trust for they work in distributed or decentralized environments. All the challenges are often brought about by a lack of trust in the multiple participants of FML-based services, usually in the progresses of model training and utilizing, such as data indexing, data computing, parameter exchanging, etc. Specific functional components are needed to enhance the trustworthiness of FML-based services, such as to enhance dataset indexing, data computing, parameter exchanging, and model utilization. Distributed ledger technology (DLT) system can be as one type of trustworthy shared data system to store the data of FML-based service as well. Convergence between FML and those components can make benefits for FML-based service, especially for helping for addressing the challenges for FML-based services in aspects of trust. This Recommendation provides a trustworthy FML-based service, and specifies its concept, general characteristics and requirements and reference framework.