src/test/scripts/functions/federated/paramserv/EncryptedFederatedParamservTest.dml

#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
#
#-------------------------------------------------------------

source("src/test/scripts/functions/federated/paramserv/TwoNN.dml") as TwoNN
source("src/test/scripts/functions/federated/paramserv/TwoNNModelAvg.dml") as TwoNNModelAvg
source("src/test/scripts/functions/federated/paramserv/CNN.dml") as CNN
source("src/test/scripts/functions/federated/paramserv/CNNModelAvg.dml") as CNNModelAvg
source("scripts/nn/examples/u-net.dml") as UNet


# create federated input matrices
features = read($features)
labels = read($labels)

if($network_type == "TwoNN") {
  if(!as.logical($modelAvg)) {
    model = TwoNN::train_paramserv(features, labels, matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), 0, $epochs, $utype, $freq, $batch_size, $scheme, $runtime_balancing, $weighting, $eta, $seed)
    print("Test results:")
    [loss_test, accuracy_test] = TwoNN::validate(matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), model, list())
    print("[+] test loss: " + loss_test + ", test accuracy: " + accuracy_test + "\n")
  }
  else if (as.logical($modelAvg)){
    model = TwoNNModelAvg::train_paramserv(features, labels, matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), 0, $epochs, $utype, $freq, $batch_size, $scheme, $runtime_balancing, $weighting, $eta, $seed, $modelAvg)
    print("Test results:")
    [loss_test, accuracy_test] = TwoNNModelAvg::validate(matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), model, list())
    print("[+] test loss: " + loss_test + ", test accuracy: " + accuracy_test + "\n")
  }
}
else if($network_type == "CNN") {
  if(!as.logical($modelAvg)) {
    model = CNN::train_paramserv(features, labels, matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), 0, $epochs, $utype, $freq, $batch_size, $scheme, $runtime_balancing, $weighting, $eta, $channels, $hin, $win, $seed)
    print("Test results:")
    hyperparams = list(learning_rate=$eta, C=$channels, Hin=$hin, Win=$win)
    [loss_test, accuracy_test] = CNN::validate(matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), model, hyperparams)
    print("[+] test loss: " + loss_test + ", test accuracy: " + accuracy_test + "\n")
  }
  else if (as.logical($modelAvg)){
    model = CNNModelAvg::train_paramserv(features, labels, matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), 0, $epochs, $utype, $freq, $batch_size, $scheme, $runtime_balancing, $weighting, $eta, $channels, $hin, $win, $seed, $modelAvg)
    print("Test results:")
    hyperparams = list(learning_rate=$eta, C=$channels, Hin=$hin, Win=$win)
    [loss_test, accuracy_test] = CNNModelAvg::validate(matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), model, hyperparams)
    print("[+] test loss: " + loss_test + ", test accuracy: " + accuracy_test + "\n")
  }
}
else if($network_type == "UNet") {
    numFeatures = $channels*$hin*$win
    numRows = nrow(features)
    F1 = 2

    x_hw = $hin + 184 # Padded input height and width
    x_val = matrix(0, rows=numRows, cols=$channels*x_hw*x_hw)
    y_val = matrix(0, rows=numRows, cols=numFeatures*2)
    K = 2
    M = numFeatures
    labels_one_hot = cbind((labels - 1) * -1, labels) # (N,KCHW) encoded labels

    model = UNet::train_paramserv(features, labels_one_hot, x_val, y_val, $channels, x_hw, x_hw, $epochs, 0, $utype, $freq, $batch_size, $scheme, $eta, $seed, M, K, TRUE, F1)
    print("Test results:")
    hyperparams = list(learning_rate=$eta, C=$channels, Hin=x_hw, Win=x_hw, M=numFeatures, K=K)
    [loss_test, accuracy_test] = UNet::validate(matrix(0, rows=numRows, cols=$channels*x_hw*x_hw), matrix(0, rows=numRows, cols=numFeatures*2), model, hyperparams, F1, $batch_size)
    print("[+] test loss: " + loss_test + ", test accuracy: " + accuracy_test + "\n")
}
else {
    print("Network type not recognized: " + $network_type)
}