Federated Learning (FL) is a new area of distributed Machine Learning (ML) that emerged to deal with data privacy concerns. In FL, each client has access to a local private dataset. At every round, a client trains the model with its local dataset and sends the weights to a central server. The latter aggregates all client weights and then sends the final weights back to the clients. This approach is attractive in many domains as it allows multiple institutions to collaborate on an ML task without sharing their data. However, most ML models used in FL have millions of weights exchanged in each message. The messages sent between a client and the server can achieve gigabytes of size and are exchanged several times in the whole FL execution. This work presents a preliminary analysis of execution times and costs of a FL application in a multi-cloud scenario. Experiments were conducted considering executions on the Amazon Web Services, Google Cloud Provider, and also in both cloud providers at the same time.