Federated Learning allows multiple distributed agents to contribute to a global machine learning model. Each agent trains locally and contributes to a global model by sending gradients to a central parameter server. The approach has some limitations: 1) some events may only occur in the local environment, so a global model may not perform as well as a specialized model; 2) changes in the local environment may require an agent to use some dedicated model, that is not available in a single global model; 3) a single global model approach is unable to derive new models from dealing with complex environments. This paper proposes a novel federated learning approach that is local environment aware and can compose new dedicated models for complicated environments. The approach is implemented in Elixir to exploit pattern matching and hot-code-swapping to maximize versatility. Our proposed approach outperforms the state of the art FL by an average of 7-10% for the MNIST dataset and 2% for the FashionMNIST dataset in specific and complex environments.