Introducing Federated Learning into Internet of Things ecosystems - preliminary considerations

Federated learning (FL) was proposed to train models in distributed environments. It facilitates data privacy and uses local resources for model training. Until now, the majority of research has been devoted to the "core issues", such as adaptation of machine learning algorithms to FL, data privacy protection, or dealing with effects of unbalanced data distribution. This contribution is anchored in a practical use case, where FL is to be actually deployed within an Internet of Things ecosystem. Hence, different issues that need to be considered are identified. Moreover, an architecture that enables the building of flexible, and adaptable, FL solutions is introduced.