def test_serialization_deserialization_multidimensional_2D_array():
num_clients = 10
num_rows_params = 3
num_cols_params = 9
clients_params = []
for i in range(num_clients):
clients_params.append(np.random.rand(num_rows_params, num_cols_params))
avgfa = NormClipAggregator(clip=100)
serialized_params = np.array(
[avgfa._serialize(client) for client in clients_params])
deserialized_params = np.array(
[avgfa._deserialize(client) for client in serialized_params])
assert np.array_equal(deserialized_params, clients_params)
def test_serialization_deserialization_mixed_list():
num_clients = 10
clients_params = []
for i_client in range(num_clients):
clients_params.append(
[np.random.rand(),
np.random.rand(20, 30),
np.random.rand(50, 40)])
avgfa = NormClipAggregator(clip=100)
serialized_params = np.array(
[avgfa._serialize(client) for client in clients_params])
for i, client in enumerate(serialized_params):
deserialized = avgfa._deserialize(client)
for j, arr in enumerate(deserialized):
assert np.array_equal(arr, clients_params[i][j])
def test_serialization_deserialization():
num_clients = 10
num_layers = 5
tams = [[128, 64], [64, 64], [64, 64], [64, 32], [32, 10]]
weights = []
for i in range(num_clients):
weights.append([
np.random.rand(tams[j][0], tams[j][1]) for j in range(num_layers)
])
clients_params = weights
avgfa = NormClipAggregator(clip=100)
serialized_params = np.array(
[avgfa._serialize(client) for client in clients_params])
for i, client in enumerate(serialized_params):
deserialized = avgfa._deserialize(client)
for j, arr in enumerate(deserialized):
assert np.array_equal(arr, clients_params[i][j])