Tensorflow Federated learning on multiple machines

I am trying to implement federated learning with Tensorflow Federated. I am not able to run the tensorflow model on the dataset existing on the client machine. The process followed is as below.

1. I have one server machine which host the dataset to be used for federated learning. I have created the model and TFF learning average process in the server.
2. The remote executor service is running on a client machine(GCP VM). The server broadcast is working fine and the model training is executing on the client machine.
3. But the data for the model training is passed as a parameter to client machine with the broadcast process. Is there a way to train the model with the data hosted on the client machine?