diff --git a/test.py b/test.py
new file mode 100644
index 0000000..6eb5aff
--- /dev/null
+++ b/test.py
@@ -0,0 +1,91 @@
+# Application of FL task
+
+from MLModel import *
+from FLModel import *
+from utils import *
+
+from torchvision import datasets, transforms
+import torch
+import numpy as np
+import os
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+
+def choose_two_digit(split_data_lst):
+    available_digit = []
+    for i, digit in enumerate(split_data_lst):
+        if len(digit) > 0:
+            available_digit.append(i)
+    try:
+        lst = np.random.choice(available_digit, 2, replace=False).tolist()
+    except:
+        print(available_digit)
+    return lst
+
+	
+	
+def load_cnn_mnist(num_users):
+    data_train = datasets.MNIST(root="~/data/", train=True, transform=transforms.Compose([
+                       transforms.ToTensor(),
+                       transforms.Normalize((0.1307,), (0.3081,))
+                   ]))
+
+    data_test = datasets.MNIST(root="~/data/", train=False, transform=transforms.Compose([
+                       transforms.ToTensor(),
+                       transforms.Normalize((0.1307,), (0.3081,))
+                   ]))
+
+    # split MNIST (training set) into non-iid data sets
+    non_iid = []
+    user_dict = mnist_noniid(data_train, num_users)
+    for i in range(num_users):
+        idx = user_dict[i]
+        d = data_train.data[idx].float().unsqueeze(1)
+        targets = data_train.targets[idx].float()
+        non_iid.append((d, targets))
+    non_iid.append((data_test.data.float().unsqueeze(1), data_test.targets.float()))
+    return non_iid
+
+"""
+1. load_data
+2. generate clients (step 3)
+3. generate aggregator
+4. training
+"""
+client_num = 10
+d = load_cnn_mnist(client_num)
+
+lr = 0.01
+fl_param = {
+    'output_size': 10,
+    'client_num': client_num,
+    'model': NeuralNet,
+    'data': d,
+    'lr': lr,
+    'E': 1,
+    'C': 1,
+    'eps': 4.0, 
+    'delta': 1e-5,
+    'q': 0.03,
+    'clip': 32,
+    'tot_T': 150,
+    'batch_size': 128,
+	'optimizer': 'Fed_GD_HT',   #'Fed_SGD_HT' or 'Fed_GD_HT'
+	'thred_k': 10000,
+    'device': device
+}
+import warnings
+warnings.filterwarnings("ignore")
+fl_entity = FLServer(fl_param).to(device)
+
+print("mnist")
+acc = []
+for e in range(150):
+    #if e+1 % 10 == 0:
+    #    lr *= 0.1
+    fl_entity.set_lr(lr/np.sqrt(e+1))
+    acc += [fl_entity.global_update()]
+    print("global epochs = {:d}, acc = {:.4f}".format(e+1, acc[-1]))
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