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DP-Sparse-Learning/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) |