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utils.py

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+"""
+Implementation of several useful tool
+Please copy this script to target path
+"""
+import numpy as np
+import random
+import torch
+
+
+def mnist_noniid(dataset, num_users):
+    """
+    Sample non-I.I.D client data from MNIST dataset
+    :param dataset:
+    :param num_users:
+    :return:
+    """
+    # num_shards, num_imgs = 30, 2000
+    num_shards = int(num_users*3)
+    num_imgs = int(60000 / num_shards)
+    idx_shard = [i for i in range(num_shards)]
+    dict_users = {i: np.array([], dtype='int64') for i in range(num_users)}
+    idxs = np.arange(num_shards*num_imgs)
+    labels = dataset.targets.numpy()
+
+    # sort labels
+    idxs_labels = np.vstack((idxs, labels))
+    idxs_labels = idxs_labels[:,idxs_labels[1,:].argsort()]
+    idxs = idxs_labels[0,:]
+
+    # divide and assign
+    for i in range(num_users):
+        rand_set = set(np.random.choice(idx_shard, 3, replace=False))
+        idx_shard = list(set(idx_shard) - rand_set)
+        for rand in rand_set:
+            dict_users[i] = np.concatenate((dict_users[i], idxs[rand*num_imgs:(rand+1)*num_imgs]), axis=0)
+    return dict_users
+
+
+def gaussian_noise(data_shape, s, sigma, device=None):
+    """
+    Gaussian noise for CDP-FedAVG-LS Algorithm
+    """
+    return torch.normal(0, sigma * s, data_shape).to(device)