def setUp(self) -> None:
self.weight_summarizer = fed_learn.FedAvg()
nb_clients = 3
nb_weight_arrays = 6
self.all_clients_weights = []
for i in range(nb_clients):
client_weight_arrays = []
for k in range(nb_weight_arrays):
rnd_weight_array = np.ones((8, 12))
rnd_weight_array += i
client_weight_arrays.append(rnd_weight_array)
self.all_clients_weights.append(client_weight_arrays)
self.avg_weights = self.weight_summarizer.process(self.all_clients_weights)
experiment_folder_path = Path(__file__).resolve().parent / "experiments" / args.name
experiment = fed_learn.Experiment(experiment_folder_path, args.overwrite_experiment)
experiment.serialize_args(args)
tf_scalar_logger = experiment.create_scalar_logger()
client_train_params = {"epochs": args.client_epochs, "batch_size": args.batch_size}
def model_fn():
return fed_learn.create_model_cnn((28, 28,1), 10, init_with_imagenet=False, learning_rate=args.learning_rate)
weight_summarizer = fed_learn.FedAvg()
server = fed_learn.Server(model_fn,
weight_summarizer,
args.clients,
args.fraction)
weight_path = args.weights_file
if weight_path is not None:
server.load_model_weights(weight_path)
server.update_client_train_params(client_train_params)
server.create_clients_plus()
(x_train, y_train), (x_test, y_test) = datasets.fashion_mnist.load_data()
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
