Abstract: Currently, the need for distributed ML training solutions in the world is increasing. However, existing tools, in particular TensorFlow Federated, are at the very beginning of their development, difficult to implement, and currently suitable exclusively for simulation on servers. For mobile devices, reliable approaches for this purpose do not exist. This article has designed and presented an approach to such distributed training of the ML-model on mobile devices, implemented on existing technologies. It is based on the concept of model personalization. In this approach, this concept is improved as a consequence of mitigating the identified drawbacks. The implementation process is structured so that at all stages of working with the ML-model use only one Swift programming language (Swift for TensorFlow and Core ML 3 are used), making this approach even more convenient and reliable due to the common code base.