联邦学习-Tensorflow实现联邦模型AlexNet on CIFAR-10

分享一种实现联邦学习的方法，它具有以下优点：

不需要读写文件来保存、切换Client模型 不需要在每次epoch重新初始化Client变量 内存占用尽可能小（参数量仅翻一倍，即Client端+Server端） 切换Client只增加了一些赋值操作

学习的目标是一个更好的模型，由Server保管，Clients提供更新 数据（Data）由Clients保管、使用文章的代码环境、库依赖：

Python 3.7 Tensorflow v1.14.x tqdm（一个Python模块）

接下来本文会分成Client端、Server端代码设计与实现进行讲解。懒得看讲解的可以直接拉到最后的完整代码章节，共有四个代码文件，运行python Server.py即可以立马体验原汁原味的（单机模拟）联邦学习。

Client端

明确一下Client端的任务，包含下面三个步骤：

将Server端发来的模型变量加载到模型上 用自己的所有数据更新当前模型 将更新后的模型变量发回给Server 在这些任务下，我们可以设计出Client代码需要具备的一些功能：

创建、训练Tensorflow模型（也就是计算图） 加载Server端发过来的模型变量值 提取当前模型的变量值，发送给Server 维护自己的数据集用于训练 其实，仔细一想也就比平时写的tf模型代码多了个加载、提取模型变量。假设Client类已经构建好了模型，那么sess.run()一下每个变量，即可得到模型变量的值了。下面的代码展示了部分Clients类的定义，get_client_vars函数将返回计算图中所有可训练的变量值：

class Clients: def __init__(self, input_shape, num_classes, learning_rate, clients_num): self.graph = tf.Graph() self.sess = tf.Session(graph=self.graph) """ 本函数未完待续... """ def get_client_vars(self): """ Return all of the variables list """ with self.graph.as_default(): client_vars = self.sess.run(tf.trainable_variables()) return client_vars

加载Server端发过来的global_vars到模型变量上，核心在于tf.Variable.load()函数，把一个Tensor的值加载到模型变量中，例如：

variable.load(tensor, sess)

将tensor（类型为tf.Tensor）的值赋值给variable（类型为tf.Varibale），sess是tf.Session。

如果要把整个模型中的变量值都加载，可以用tf.trainable_variables()获取计算图中的所有可训练变量（一个list），保证它和global_vars的顺序对应后，可以这样实现：

def set_global_vars(self, global_vars): """ Assign all of the variables with global vars """ with self.graph.as_default(): all_vars = tf.trainable_variables() for variable, value in zip(all_vars, global_vars): variable.load(value, self.sess)

此外，Clients类还需要进行模型定义和训练。我相信这不是实现联邦的重点，因此在下面的代码中，我将函数体去掉只留下接口定义（完整代码在最后一个章节）：

import tensorflow as tf import numpy as np from collections import namedtuple import math

# 自定义的模型定义函数 from Model import AlexNet # 自定义的数据集类 from Dataset import Dataset

# The definition of fed model # 用namedtuple来储存一个模型，依次为： # X: 输入 # Y: 输出 # DROP_RATE: 顾名思义 # train_op: tf计算图中的训练节点（一般是optimizer.minimize(xxx)） # loss_op: 顾名思义 # loss_op: 顾名思义 FedModel = namedtuple('FedModel', 'X Y DROP_RATE train_op loss_op acc_op')

class Clients: def __init__(self, input_shape, num_classes, learning_rate, clients_num): self.graph = tf.Graph() self.sess = tf.Session(graph=self.graph)

# Call the create function to build the computational graph of AlexNet # `net` 是一个list，依次包含模型中FedModel需要的计算节点（看上面） net = AlexNet(input_shape, num_classes, learning_rate, self.graph) self.model = FedModel(*net)

# initialize 初始化 with self.graph.as_default(): self.sess.run(tf.global_variables_initializer())

# Load Cifar-10 dataset # NOTE: len(self.dataset.train) == clients_num # 加载数据集。对于训练集：`self.dataset.train[56]`可以获取56号client的数据集 # `self.dataset.train[56].next_batch(32)`可以获取56号client的一个batch，大小为32 # 对于测试集，所有client共用一个测试集，因此： # `self.dataset.test.next_batch(1000)`将获取大小为1000的数据集（无随机） self.dataset = Dataset(tf.keras.datasets.cifar10.load_data, split=clients_num)

def run_test(self, num): """ Predict the testing set, and report the acc and loss 预测测试集，返回准确率和loss num: number of testing instances """ pass

def train_epoch(self, cid, batch_size=32, dropout_rate=0.5): """ Train one client with its own data for one epoch 用`cid`号的client的数据对模型进行训练 cid: Client id """ pass def choose_clients(self, ratio=1.0): """ randomly choose some clients 随机选择`ratio`比例的clients，返回编号（也就是下标） """ client_num = self.get_clients_num() choose_num = math.floor(client_num * ratio) return np.random.permutation(client_num)[:choose_num] def get_clients_num(self): """ 返回clients的数量 """ return len(self.dataset.train)

细心的同学可能已经发现了，类名是Clients是复数，表示一堆Clients的集合。但模型self.model只有一个，原因是：不同Clients的模型实际上是一样的，只是数据不同；类成员self.dataset已经对数据进行了划分，需要不同client参与训练时，只需要用Server给的变量值把模型变量覆盖掉，再用下标cid找到该Client的数据进行训练就得了。

当然，这样实现的最重要原因，是避免构建那么多个Client的计算图。咱没那么多显存TAT 概括一下：联邦学习的Clients，只是普通TF训练模型代码上，加上模型变量的值提取、赋值功能。

Server端

按照套路，明确一下Server端代码的主要任务：

使用Clients：给一组模型变量给某个Client进行更新，把更新后的变量值拿回来 管理全局模型：每一轮更新，收集多个Clients更新后的模型进行归总，成为新一轮的模型 简单起见，我们Server端的代码不再抽象成一个类，而是以脚本的形式编写。首先，实例化咱们上面定义的Clients：

from Client import Clients

def buildClients(num): learning_rate = 0.0001 num_input = 32 # image shape: 32*32 num_input_channel = 3 # image channel: 3 num_classes = 10 # Cifar-10 total classes (0-9 digits)

#create Client and model return Clients(input_shape=[None, num_input, num_input, num_input_channel], num_classes=num_classes, learning_rate=learning_rate, clients_num=num)

CLIENT_NUMBER = 100 client = buildClients(CLIENT_NUMBER) global_vars = client.get_client_vars()

client变量储存着CLIENT_NUMBER个Clients的模型（实际上只有一个计算图）和数据。global_vars储存着Server端的模型变量值，也就是我们大名鼎鼎的训练目标，目前它只是Client端模型初始化的值。

接下来，对于Server的一个epoch，Server会随机挑选一定比例的Clients参与这轮训练，分别把当前的Server端模型global_vars交给它们进行更新，并分别收集它们更新后的变量。本轮参与训练的Clients都收集后，平均一下这些更新后的变量值，就得到新一轮的Server端模型，然后进行下一个epoch。下面是循环epoch更新的代码，仔细看注释哦：

def run_global_test(client, global_vars, test_num): """ 跑一下测试集，输出ACC和Loss """ client.set_global_vars(global_vars) acc, loss = client.run_test(test_num) print("[epoch {}, {} inst] Testing ACC: {:.4f}, Loss: {:.4f}".format( ep + 1, test_num, acc, loss))

CLIENT_RATIO_PER_ROUND = 0.12 # 每轮挑选clients跑跑看的比例 epoch = 360 # epoch上限

for ep in range(epoch): # We are going to sum up active clients' vars at each epoch # 用来收集Clients端的参数，全部叠加起来（节约内存） client_vars_sum = None

# Choose some clients that will train on this epoch # 随机挑选一些Clients进行训练 random_clients = client.choose_clients(CLIENT_RATIO_PER_ROUND)

# Train with these clients # 用这些Clients进行训练，收集它们更新后的模型 for client_id in tqdm(random_clients, ascii=True): # Restore global vars to client's model # 将Server端的模型加载到Client模型上 client.set_global_vars(global_vars)

# train one client # 训练这个下标的Client client.train_epoch(cid=client_id)

# obtain current client's vars # 获取当前Client的模型变量值 current_client_vars = client.get_client_vars()

# sum it up # 把各个层的参数叠加起来 if client_vars_sum is None: client_vars_sum = current_client_vars else: for cv, ccv in zip(client_vars_sum, current_client_vars): cv += ccv

# obtain the avg vars as global vars # 把叠加后的Client端模型变量 除以 本轮参与训练的Clients数量 # 得到平均模型、作为新一轮的Server端模型参数 global_vars = [] for var in client_vars_sum: global_vars.append(var / len(random_clients))

# run test on 1000 instances # 跑一下测试集、输出一下 run_global_test(client, global_vars, test_num=600)

经过那么一些轮的迭代，我们就可以得到Server端的训练好的模型参数global_vars了。虽然它逻辑很简单，但我希望观众老爷们能注意到其中的两个联邦点：Server端代码没有接触到数据；每次参与训练的Clients数量相对于整体来说是很少的。

扩展

如果要更换模型，只需要实现新的模型计算图构造函数，替换Client端的AlexNet函数，保证它能返回那一系列的计算节点即可。

如果要实现Non-I.I.D.的数据分布，只需要修改Dataset.py中的数据划分方式。但是，我稍微试验了一下，目前这个模型+训练方式，不能应对极度Non-I.I.D.的情况。也反面证明了，Non-I.I.D.确实是联邦学习的一个难题。

如果要Clients和Server之间传模型梯度，需要把Client端的计算梯度和更新变量分开，中间插入和Server端的交互，交互内容就是梯度。这样说有点抽象，很多同学可能经常用Optimizer.minimize（文档在这），但并不知道它是另外两个函数的组合，分别为：compute_gradients()和apply_gradients()。前者是计算梯度，后者是把梯度按照学习率更新到变量上。把梯度拿到后，交给Server，Server返回一个全局平均后的梯度再更新模型。尝试过是可行的，但是并不能减少传输量，而且单机模拟实现难度大了许多。

如果要分布式部署，那就把Clients端代码放在flask等web后端服务下进行部署，Server端通过网络传输与Clients进行通信。需要注意，Server端发起请求的时候，可能因为参数量太大导致一些问题，考虑换个非HTTP协议。

完整代码 一共有四个代码文件，他们应当放在同一个文件目录下：

Client.py：Client端代码，管理模型、数据 Server.py：Server端代码，管理Clients、全局模型 Dataset.py：定义数据的组织形式 Model.py：定义TF模型的计算图 我也将它们传到了Github上，仓库链接：https://github.com/Zing22/tf-fed-demo。

下面开始分别贴出它们的完整代码，其中的注释只有我边打码边写的一点点，上文的介绍中补充了更多中文注释。运行方法非常简单：

python Server.py

Client.py

import tensorflow as tf import numpy as np from collections import namedtuple import math

from Model import AlexNet from Dataset import Dataset

# The definition of fed model FedModel = namedtuple('FedModel', 'X Y DROP_RATE train_op loss_op acc_op')

class Clients: def __init__(self, input_shape, num_classes, learning_rate, clients_num): self.graph = tf.Graph() self.sess = tf.Session(graph=self.graph)

# Call the create function to build the computational graph of AlexNet net = AlexNet(input_shape, num_classes, learning_rate, self.graph) self.model = FedModel(*net)

# initialize with self.graph.as_default(): self.sess.run(tf.global_variables_initializer())

# Load Cifar-10 dataset # NOTE: len(self.dataset.train) == clients_num self.dataset = Dataset(tf.keras.datasets.cifar10.load_data, split=clients_num)

def run_test(self, num): with self.graph.as_default(): batch_x, batch_y = self.dataset.test.next_batch(num) feed_dict = { self.model.X: batch_x, self.model.Y: batch_y, self.model.DROP_RATE: 0 } return self.sess.run([self.model.acc_op, self.model.loss_op], feed_dict=feed_dict)

def train_epoch(self, cid, batch_size=32, dropout_rate=0.5): """ Train one client with its own data for one epoch cid: Client id """ dataset = self.dataset.train[cid]

with self.graph.as_default(): for _ in range(math.ceil(dataset.size / batch_size)): batch_x, batch_y = dataset.next_batch(batch_size) feed_dict = { self.model.X: batch_x, self.model.Y: batch_y, self.model.DROP_RATE: dropout_rate } self.sess.run(self.model.train_op, feed_dict=feed_dict)

def get_client_vars(self): """ Return all of the variables list """ with self.graph.as_default(): client_vars = self.sess.run(tf.trainable_variables()) return client_vars

def set_global_vars(self, global_vars): """ Assign all of the variables with global vars """ with self.graph.as_default(): all_vars = tf.trainable_variables() for variable, value in zip(all_vars, global_vars): variable.load(value, self.sess)

def choose_clients(self, ratio=1.0): """ randomly choose some clients """ client_num = self.get_clients_num() choose_num = math.ceil(client_num * ratio) return np.random.permutation(client_num)[:choose_num]

def get_clients_num(self): return len(self.dataset.train)

Server.py

import tensorflow as tf from tqdm import tqdm

from Client import Clients

def buildClients(num): learning_rate = 0.0001 num_input = 32 # image shape: 32*32 num_input_channel = 3 # image channel: 3 num_classes = 10 # Cifar-10 total classes (0-9 digits)

#create Client and model return Clients(input_shape=[None, num_input, num_input, num_input_channel], num_classes=num_classes, learning_rate=learning_rate, clients_num=num)

def run_global_test(client, global_vars, test_num): client.set_global_vars(global_vars) acc, loss = client.run_test(test_num) print("[epoch {}, {} inst] Testing ACC: {:.4f}, Loss: {:.4f}".format( ep + 1, test_num, acc, loss))

#### SOME TRAINING PARAMS #### CLIENT_NUMBER = 100 CLIENT_RATIO_PER_ROUND = 0.12 epoch = 360

#### CREATE CLIENT AND LOAD DATASET #### client = buildClients(CLIENT_NUMBER)

#### BEGIN TRAINING #### global_vars = client.get_client_vars() for ep in range(epoch): # We are going to sum up active clients' vars at each epoch client_vars_sum = None

# Choose some clients that will train on this epoch random_clients = client.choose_clients(CLIENT_RATIO_PER_ROUND)

# Train with these clients for client_id in tqdm(random_clients, ascii=True): # Restore global vars to client's model client.set_global_vars(global_vars)

# train one client client.train_epoch(cid=client_id)

# obtain current client's vars current_client_vars = client.get_client_vars()

# sum it up if client_vars_sum is None: client_vars_sum = current_client_vars else: for cv, ccv in zip(client_vars_sum, current_client_vars): cv += ccv

# obtain the avg vars as global vars global_vars = [] for var in client_vars_sum: global_vars.append(var / len(random_clients))

# run test on 600 instances run_global_test(client, global_vars, test_num=600)

#### FINAL TEST #### run_global_test(client, global_vars, test_num=10000)

Dataset.py

import numpy as np from tensorflow.keras.utils import to_categorical

class BatchGenerator: def __init__(self, x, yy): self.x = x self.y = yy self.size = len(x) self.random_order = list(range(len(x))) np.random.shuffle(self.random_order) self.start = 0 return

def next_batch(self, batch_size): perm = self.random_order[self.start:self.start + batch_size]

self.start += batch_size if self.start > self.size: self.start = 0

return self.x[perm], self.y[perm]

# support slice def __getitem__(self, val): return self.x[val], self.y[val]

class Dataset(object): def __init__(self, load_data_func, one_hot=True, split=0): (x_train, y_train), (x_test, y_test) = load_data_func() print("Dataset: train-%d, test-%d" % (len(x_train), len(x_test)))

if one_hot: y_train = to_categorical(y_train, 10) y_test = to_categorical(y_test, 10)

x_train = x_train.astype('float32') / 255 x_test = x_test.astype('float32') / 255

if split == 0: self.train = BatchGenerator(x_train, y_train) else: self.train = self.splited_batch(x_train, y_train, split)

self.test = BatchGenerator(x_test, y_test)

def splited_batch(self, x_data, y_data, split): res = [] for x, y in zip(np.split(x_data, split), np.split(y_data, split)): assert len(x) == len(y) res.append(BatchGenerator(x, y)) return res

Model.py

import tensorflow as tf import numpy as np from tensorflow.compat.v1.train import AdamOptimizer

#### Create tf model for Client ####

def AlexNet(input_shape, num_classes, learning_rate, graph): """ Construct the AlexNet model. input_shape: The shape of input (`list` like) num_classes: The number of output classes (`int`) learning_rate: learning rate for optimizer (`float`) graph: The tf computation graph (`tf.Graph`) """ with graph.as_default(): X = tf.placeholder(tf.float32, input_shape, name='X') Y = tf.placeholder(tf.float32, [None, num_classes], name='Y') DROP_RATE = tf.placeholder(tf.float32, name='drop_rate')

# 1st Layer: Conv (w ReLu) -> Lrn -> Pool # conv1 = conv(X, 11, 11, 96, 4, 4, padding='VALID', name='conv1') conv1 = conv(X, 11, 11, 96, 2, 2, name='conv1') norm1 = lrn(conv1, 2, 2e-05, 0.75, name='norm1') pool1 = max_pool(norm1, 3, 3, 2, 2, padding='VALID', name='pool1')

# 2nd Layer: Conv (w ReLu) -> Lrn -> Pool with 2 groups conv2 = conv(pool1, 5, 5, 256, 1, 1, groups=2, name='conv2') norm2 = lrn(conv2, 2, 2e-05, 0.75, name='norm2') pool2 = max_pool(norm2, 3, 3, 2, 2, padding='VALID', name='pool2')

# 3rd Layer: Conv (w ReLu) conv3 = conv(pool2, 3, 3, 384, 1, 1, name='conv3')

# 4th Layer: Conv (w ReLu) splitted into two groups conv4 = conv(conv3, 3, 3, 384, 1, 1, groups=2, name='conv4')

# 5th Layer: Conv (w ReLu) -> Pool splitted into two groups conv5 = conv(conv4, 3, 3, 256, 1, 1, groups=2, name='conv5') pool5 = max_pool(conv5, 3, 3, 2, 2, padding='VALID', name='pool5')

# 6th Layer: Flatten -> FC (w ReLu) -> Dropout # flattened = tf.reshape(pool5, [-1, 6*6*256]) # fc6 = fc(flattened, 6*6*256, 4096, name='fc6')

flattened = tf.reshape(pool5, [-1, 1 * 1 * 256]) fc6 = fc_layer(flattened, 1 * 1 * 256, 1024, name='fc6') dropout6 = dropout(fc6, DROP_RATE)

# 7th Layer: FC (w ReLu) -> Dropout # fc7 = fc(dropout6, 4096, 4096, name='fc7') fc7 = fc_layer(dropout6, 1024, 2048, name='fc7') dropout7 = dropout(fc7, DROP_RATE)

# 8th Layer: FC and return unscaled activations logits = fc_layer(dropout7, 2048, num_classes, relu=False, name='fc8')

# loss and optimizer loss_op = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=Y)) optimizer = AdamOptimizer( learning_rate=learning_rate) train_op = optimizer.minimize(loss_op)

# Evaluate model prediction = tf.nn.softmax(logits) pred = tf.argmax(prediction, 1)

# accuracy correct_pred = tf.equal(pred, tf.argmax(Y, 1)) accuracy = tf.reduce_mean( tf.cast(correct_pred, tf.float32))

return X, Y, DROP_RATE, train_op, loss_op, accuracy

def conv(x, filter_height, filter_width, num_filters, stride_y, stride_x, name, padding='SAME', groups=1): """Create a convolution layer.

Adapted from: https://github.com/ethereon/caffe-tensorflow """ # Get number of input channels input_channels = int(x.get_shape()[-1])

# Create lambda function for the convolution convolve = lambda i, k: tf.nn.conv2d( i, k, strides=[1, stride_y, stride_x, 1], padding=padding)

with tf.variable_scope(name) as scope: # Create tf variables for the weights and biases of the conv layer weights = tf.get_variable('weights', shape=[ filter_height, filter_width, input_channels / groups, num_filters ]) biases = tf.get_variable('biases', shape=[num_filters])

if groups == 1: conv = convolve(x, weights)

# In the cases of multiple groups, split inputs & weights and else: # Split input and weights and convolve them separately input_groups = tf.split(axis=3, num_or_size_splits=groups, value=x) weight_groups = tf.split(axis=3, num_or_size_splits=groups, value=weights) output_groups = [ convolve(i, k) for i, k in zip(input_groups, weight_groups) ]

# Concat the convolved output together again conv = tf.concat(axis=3, values=output_groups)

# Add biases bias = tf.reshape(tf.nn.bias_add(conv, biases), tf.shape(conv))

# Apply relu function relu = tf.nn.relu(bias, name=scope.name)

return relu

def fc_layer(x, input_size, output_size, name, relu=True, k=20): """Create a fully connected layer."""

with tf.variable_scope(name) as scope: # Create tf variables for the weights and biases. W = tf.get_variable('weights', shape=[input_size, output_size]) b = tf.get_variable('biases', shape=[output_size]) # Matrix multiply weights and inputs and add biases. z = tf.nn.bias_add(tf.matmul(x, W), b, name=scope.name)

if relu: # Apply ReLu non linearity. a = tf.nn.relu(z) return a

else: return z

def max_pool(x, filter_height, filter_width, stride_y, stride_x, name, padding='SAME'): """Create a max pooling layer.""" return tf.nn.max_pool2d(x, ksize=[1, filter_height, filter_width, 1], strides=[1, stride_y, stride_x, 1], padding=padding, name=name)

def lrn(x, radius, alpha, beta, name, bias=1.0): """Create a local response normalization layer.""" return tf.nn.local_response_normalization(x, depth_radius=radius, alpha=alpha, beta=beta, bias=bias, name=name)

def dropout(x, rate): """Create a dropout layer.""" return tf.nn.dropout(x, rate=rate)