In the federated learning context, One such classmethod that should work would be tff.simulation.ClientData.from_clients_and_fn. Here, if I pass a list of client_ids and a function which returns the appropriate dataset when given a client id, you will have your hands on a fully functional ClientData.
I think here, an approach for defining the function I may use is to construct a Python dict which maps client IDs to tf.data.Dataset objects--you could then define a function which takes a client id, looks up the dataset in the dict, and returns the dataset. So I define function as below but I think it is wrong, what do you think?
list = ["0","1","2"]
tab = {"0":ds, "1":ds, "2":ds}
def create_tf_dataset_for_client_fn(id):
return ds
source = tff.simulation.ClientData.from_clients_and_fn(list, create_tf_dataset_for_client_fn)
I suppose here that the 4 clients have the same dataset :'ds'