 在 TensorFlow.org 上查看
|
 在 Google Colab 中运行
|
 在 GitHub 上查看源代码
|
 下载笔记本
|
本教程展示了如何使用 TFF 训练一个非常大的模型，其中每个客户端设备仅下载和更新模型的一小部分，使用 tff.federated_select 和稀疏聚合。虽然本教程相当独立，但 tff.federated_select 教程 和 自定义 FL 算法教程 提供了对这里使用的一些技术的良好介绍。
具体来说，在本教程中，我们考虑了用于多标签分类的逻辑回归，根据词袋特征表示预测与文本字符串关联的“标签”。重要的是，通信和客户端计算成本由一个固定常数 (MAX_TOKENS_SELECTED_PER_CLIENT) 控制，并且不随整体词汇量的大小而变化，在实际设置中，词汇量可能非常大。
pip install --quiet --upgrade tensorflow-federated
import collections
from collections.abc import Callable
import itertools
import numpy as np
import tensorflow as tf
import tensorflow_federated as tff
每个客户端将 federated_select 模型权重的行，最多包含这么多唯一标记。这限制了客户端本地模型的大小以及执行的服务器 -> 客户端 (federated_select) 和客户端 -> 服务器 (federated_aggregate) 通信量。
即使您将此值设置为 1（确保不会选择每个客户端的所有标记）或设置为一个较大的值，本教程也应该仍然可以正常运行，尽管模型收敛可能会受到影响。
MAX_TOKENS_SELECTED_PER_CLIENT = 6
我们还为各种类型定义了一些常量。对于此 colab，标记是在解析数据集后特定单词的整数标识符。
# There are some constraints on types
# here that will require some explicit type conversions:
# - `tff.federated_select` requires int32
# - `tf.SparseTensor` requires int64 indices.
TOKEN_DTYPE = np.int64
SELECT_KEY_DTYPE = np.int32
# Type for counts of token occurences.
TOKEN_COUNT_DTYPE = np.int32
# A sparse feature vector can be thought of as a map
# from TOKEN_DTYPE to FEATURE_DTYPE.
# Our features are {0, 1} indicators, so we could potentially
# use np.int8 as an optimization.
FEATURE_DTYPE = np.int32
设置问题：数据集和模型
在本教程中，我们构建了一个微型玩具数据集，便于进行实验。但是，数据集的格式与 联邦 StackOverflow 兼容，并且 预处理 和 模型架构 采用自 自适应联邦优化 的 StackOverflow 标签预测问题。
数据集解析和预处理
NUM_OOV_BUCKETS = 1
BatchType = collections.namedtuple('BatchType', ['tokens', 'tags'])
def build_to_ids_fn(word_vocab: list[str],
tag_vocab: list[str]) -> Callable[[tf.Tensor], tf.Tensor]:
"""Constructs a function mapping examples to sequences of token indices."""
word_table_values = np.arange(len(word_vocab), dtype=np.int64)
word_table = tf.lookup.StaticVocabularyTable(
tf.lookup.KeyValueTensorInitializer(word_vocab, word_table_values),
num_oov_buckets=NUM_OOV_BUCKETS)
tag_table_values = np.arange(len(tag_vocab), dtype=np.int64)
tag_table = tf.lookup.StaticVocabularyTable(
tf.lookup.KeyValueTensorInitializer(tag_vocab, tag_table_values),
num_oov_buckets=NUM_OOV_BUCKETS)
def to_ids(example):
"""Converts a Stack Overflow example to a bag-of-words/tags format."""
sentence = tf.strings.join([example['tokens'], example['title']],
separator=' ')
# We represent that label (output tags) densely.
raw_tags = example['tags']
tags = tf.strings.split(raw_tags, sep='|')
tags = tag_table.lookup(tags)
tags, _ = tf.unique(tags)
tags = tf.one_hot(tags, len(tag_vocab) + NUM_OOV_BUCKETS)
tags = tf.reduce_max(tags, axis=0)
# We represent the features as a SparseTensor of {0, 1}s.
words = tf.strings.split(sentence)
tokens = word_table.lookup(words)
tokens, _ = tf.unique(tokens)
# Note: We could choose to use the word counts as the feature vector
# instead of just {0, 1} values (see tf.unique_with_counts).
tokens = tf.reshape(tokens, shape=(tf.size(tokens), 1))
tokens_st = tf.SparseTensor(
tokens,
tf.ones(tf.size(tokens), dtype=FEATURE_DTYPE),
dense_shape=(len(word_vocab) + NUM_OOV_BUCKETS,))
tokens_st = tf.sparse.reorder(tokens_st)
return BatchType(tokens_st, tags)
return to_ids
def build_preprocess_fn(word_vocab, tag_vocab):
@tf.function
def preprocess_fn(dataset):
to_ids = build_to_ids_fn(word_vocab, tag_vocab)
# We *don't* shuffle in order to make this colab deterministic for
# easier testing and reproducibility.
# But real-world training should use `.shuffle()`.
return dataset.map(to_ids, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return preprocess_fn
一个微型玩具数据集
我们构建了一个微型玩具数据集，其中包含 12 个单词的全局词汇表和 3 个客户端。这个微型示例对于测试边缘情况很有用（例如，我们有两个客户端的独特标记少于 MAX_TOKENS_SELECTED_PER_CLIENT = 6，而一个客户端的独特标记更多），并且可以开发代码。
但是，这种方法的实际用例将是数千万甚至更多的全局词汇表，每个客户端可能出现数千个独特标记。由于数据的格式相同，因此扩展到更现实的测试平台问题（例如 tff.simulation.datasets.stackoverflow.load_data() 数据集）应该很简单。
首先，我们定义单词和标签词汇表。
# Features
FRUIT_WORDS = ['apple', 'orange', 'pear', 'kiwi']
VEGETABLE_WORDS = ['carrot', 'broccoli', 'arugula', 'peas']
FISH_WORDS = ['trout', 'tuna', 'cod', 'salmon']
WORD_VOCAB = FRUIT_WORDS + VEGETABLE_WORDS + FISH_WORDS
# Labels
TAG_VOCAB = ['FRUIT', 'VEGETABLE', 'FISH']
现在，我们创建 3 个具有小型本地数据集的客户端。如果您在 colab 中运行本教程，使用“在选项卡中镜像单元格”功能将此单元格及其输出固定起来可能会有所帮助，以便解释/检查下面开发的函数的输出。
preprocess_fn = build_preprocess_fn(WORD_VOCAB, TAG_VOCAB)
def make_dataset(raw):
d = tf.data.Dataset.from_tensor_slices(
# Matches the StackOverflow formatting
collections.OrderedDict(
tokens=tf.constant([t[0] for t in raw]),
tags=tf.constant([t[1] for t in raw]),
title=['' for _ in raw]))
d = preprocess_fn(d)
return d
# 4 distinct tokens
CLIENT1_DATASET = make_dataset([
('apple orange apple orange', 'FRUIT'),
('carrot trout', 'VEGETABLE|FISH'),
('orange apple', 'FRUIT'),
('orange', 'ORANGE|CITRUS') # 2 OOV tag
])
# 6 distinct tokens
CLIENT2_DATASET = make_dataset([
('pear cod', 'FRUIT|FISH'),
('arugula peas', 'VEGETABLE'),
('kiwi pear', 'FRUIT'),
('sturgeon', 'FISH'), # OOV word
('sturgeon bass', 'FISH') # 2 OOV words
])
# A client with all possible words & tags (13 distinct tokens).
# With MAX_TOKENS_SELECTED_PER_CLIENT = 6, we won't download the model
# slices for all tokens that occur on this client.
CLIENT3_DATASET = make_dataset([
(' '.join(WORD_VOCAB + ['oovword']), '|'.join(TAG_VOCAB)),
# Mathe the OOV token and 'salmon' occur in the largest number
# of examples on this client:
('salmon oovword', 'FISH|OOVTAG')
])
print('Word vocab')
for i, word in enumerate(WORD_VOCAB):
print(f'{i:2d} {word}')
print('\nTag vocab')
for i, tag in enumerate(TAG_VOCAB):
print(f'{i:2d} {tag}')
Word vocab 0 apple 1 orange 2 pear 3 kiwi 4 carrot 5 broccoli 6 arugula 7 peas 8 trout 9 tuna 10 cod 11 salmon Tag vocab 0 FRUIT 1 VEGETABLE 2 FISH
定义输入特征（标记/单词）和标签（帖子标签）的原始数量的常量。我们的实际输入/输出空间是 NUM_OOV_BUCKETS = 1 较大，因为我们添加了一个 OOV 标记/标签。
NUM_WORDS = len(WORD_VOCAB)
NUM_TAGS = len(TAG_VOCAB)
WORD_VOCAB_SIZE = NUM_WORDS + NUM_OOV_BUCKETS
TAG_VOCAB_SIZE = NUM_TAGS + NUM_OOV_BUCKETS
创建数据集的批处理版本和单个批处理，这在测试代码时很有用。
batched_dataset1 = CLIENT1_DATASET.batch(2)
batched_dataset2 = CLIENT2_DATASET.batch(3)
batched_dataset3 = CLIENT3_DATASET.batch(2)
batch1 = next(iter(batched_dataset1))
batch2 = next(iter(batched_dataset2))
batch3 = next(iter(batched_dataset3))
定义具有稀疏输入的模型
我们对每个标签使用一个简单的独立逻辑回归模型。
def create_logistic_model(word_vocab_size: int, vocab_tags_size: int):
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=(word_vocab_size,), sparse=True),
tf.keras.layers.Dense(
vocab_tags_size,
activation='sigmoid',
kernel_initializer=tf.keras.initializers.zeros,
# For simplicity, don't use a bias vector; this means the model
# is a single tensor, and we only need sparse aggregation of
# the per-token slices of the model. Generalizing to also handle
# other model weights that are fully updated
# (non-dense broadcast and aggregate) would be a good exercise.
use_bias=False),
])
return model
让我们先确保它可以正常工作，首先进行预测
model = create_logistic_model(WORD_VOCAB_SIZE, TAG_VOCAB_SIZE)
p = model.predict(batch1.tokens)
print(p)
[[0.5 0.5 0.5 0.5] [0.5 0.5 0.5 0.5]]
以及一些简单的集中式训练
model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.001),
loss=tf.keras.losses.BinaryCrossentropy())
model.train_on_batch(batch1.tokens, batch1.tags)
联邦计算的构建块
我们将实现一个简单的联邦平均算法版本，主要区别在于每个设备只下载模型的相关子集，并且只对该子集贡献更新。
我们使用M作为MAX_TOKENS_SELECTED_PER_CLIENT的简写。从高层次来看，一轮训练涉及以下步骤：
每个参与的客户端扫描其本地数据集，解析输入字符串并将它们映射到正确的标记（整数索引）。这需要访问全局（大型）字典（这可以通过使用特征哈希技术来避免）。然后，我们稀疏地统计每个标记出现的次数。如果设备上出现
U个唯一标记，我们将选择num_actual_tokens = min(U, M)个最频繁的标记进行训练。客户端使用
federated_select从服务器检索num_actual_tokens个选定标记的模型系数。每个模型切片是一个形状为(TAG_VOCAB_SIZE, )的张量，因此传输到客户端的总数据大小最多为TAG_VOCAB_SIZE * M（见下文说明）。客户端构建一个映射
global_token -> local_token，其中本地标记（整数索引）是全局标记在选定标记列表中的索引。客户端使用一个“小型”的全局模型，该模型只有最多
M个标记的系数，范围为[0, num_actual_tokens)。使用global -> local映射从选定的模型切片初始化该模型的密集参数。客户端使用SGD在使用
global -> local映射预处理的数据上训练其本地模型。客户端使用
local -> global映射将本地模型的参数转换为IndexedSlices更新，以索引行。服务器使用稀疏求和聚合来聚合这些更新。服务器获取上述聚合的（密集）结果，将其除以参与的客户端数量，并将得到的平均更新应用于全局模型。
在本节中，我们构建了这些步骤的构建块，这些构建块将在最终的federated_computation中组合在一起，以捕获一轮训练的完整逻辑。
统计客户端标记并决定要federated_select哪些模型切片
每个设备都需要决定哪些模型“切片”与其本地训练数据集相关。对于我们的问题，我们通过（稀疏地！）统计客户端训练数据集中每个标记出现的次数来实现这一点。
@tf.function
def token_count_fn(token_counts, batch):
"""Adds counts from `batch` to the running `token_counts` sum."""
# Sum across the batch dimension.
flat_tokens = tf.sparse.reduce_sum(
batch.tokens, axis=0, output_is_sparse=True)
flat_tokens = tf.cast(flat_tokens, dtype=TOKEN_COUNT_DTYPE)
return tf.sparse.add(token_counts, flat_tokens)
# Simple tests
# Create the initial zero token counts using empty tensors.
initial_token_counts = tf.SparseTensor(
indices=tf.zeros(shape=(0, 1), dtype=TOKEN_DTYPE),
values=tf.zeros(shape=(0,), dtype=TOKEN_COUNT_DTYPE),
dense_shape=(WORD_VOCAB_SIZE,))
client_token_counts = batched_dataset1.reduce(initial_token_counts,
token_count_fn)
tokens = tf.reshape(client_token_counts.indices, (-1,)).numpy()
print('tokens:', tokens)
np.testing.assert_array_equal(tokens, [0, 1, 4, 8])
# The count is the number of *examples* in which the token/word
# occurs, not the total number of occurences, since we still featurize
# multiple occurences in the same example as a "1".
counts = client_token_counts.values.numpy()
print('counts:', counts)
np.testing.assert_array_equal(counts, [2, 3, 1, 1])
tokens: [0 1 4 8] counts: [2 3 1 1]
我们将选择与设备上MAX_TOKENS_SELECTED_PER_CLIENT个最常出现的标记相对应的模型参数。如果设备上出现的标记少于此数量，我们将填充列表以启用federated_select的使用。
请注意，其他策略可能更好，例如，随机选择标记（可能基于其出现概率）。这将确保模型的所有切片（客户端有数据）都有机会被更新。
@tf.function
def keys_for_client(client_dataset, max_tokens_per_client):
"""Computes a set of max_tokens_per_client keys."""
initial_token_counts = tf.SparseTensor(
indices=tf.zeros((0, 1), dtype=TOKEN_DTYPE),
values=tf.zeros((0,), dtype=TOKEN_COUNT_DTYPE),
dense_shape=(WORD_VOCAB_SIZE,))
client_token_counts = client_dataset.reduce(initial_token_counts,
token_count_fn)
# Find the most-frequently occuring tokens
tokens = tf.reshape(client_token_counts.indices, shape=(-1,))
counts = client_token_counts.values
perm = tf.argsort(counts, direction='DESCENDING')
tokens = tf.gather(tokens, perm)
counts = tf.gather(counts, perm)
num_raw_tokens = tf.shape(tokens)[0]
actual_num_tokens = tf.minimum(max_tokens_per_client, num_raw_tokens)
selected_tokens = tokens[:actual_num_tokens]
paddings = [[0, max_tokens_per_client - tf.shape(selected_tokens)[0]]]
padded_tokens = tf.pad(selected_tokens, paddings=paddings)
# Make sure the type is statically determined
padded_tokens = tf.reshape(padded_tokens, shape=(max_tokens_per_client,))
# We will pass these tokens as keys into `federated_select`, which
# requires SELECT_KEY_DTYPE=np.int32 keys.
padded_tokens = tf.cast(padded_tokens, dtype=SELECT_KEY_DTYPE)
return padded_tokens, actual_num_tokens
# Simple test
# Case 1: actual_num_tokens > max_tokens_per_client
selected_tokens, actual_num_tokens = keys_for_client(batched_dataset1, 3)
assert tf.size(selected_tokens) == 3
assert actual_num_tokens == 3
# Case 2: actual_num_tokens < max_tokens_per_client
selected_tokens, actual_num_tokens = keys_for_client(batched_dataset1, 10)
assert tf.size(selected_tokens) == 10
assert actual_num_tokens == 4
将全局标记映射到本地标记
上述选择为我们提供了一个密集的标记集，范围为[0, actual_num_tokens)，我们将用于设备上的模型。但是，我们读取的数据集中的标记来自更大的全局词汇范围[0, WORD_VOCAB_SIZE)。
因此，我们需要将全局标记映射到其对应的本地标记。本地标记 ID 由前一步计算的selected_tokens张量中的索引给出。
@tf.function
def map_to_local_token_ids(client_data, client_keys):
global_to_local = tf.lookup.StaticHashTable(
# Note int32 -> int64 maps are not supported
tf.lookup.KeyValueTensorInitializer(
keys=tf.cast(client_keys, dtype=TOKEN_DTYPE),
# Note we need to use tf.shape, not the static
# shape client_keys.shape[0]
values=tf.range(0, limit=tf.shape(client_keys)[0],
dtype=TOKEN_DTYPE)),
# We use -1 for tokens that were not selected, which can occur for clients
# with more than MAX_TOKENS_SELECTED_PER_CLIENT distinct tokens.
# We will simply remove these invalid indices from the batch below.
default_value=-1)
def to_local_ids(sparse_tokens):
indices_t = tf.transpose(sparse_tokens.indices)
batch_indices = indices_t[0] # First column
tokens = indices_t[1] # Second column
tokens = tf.map_fn(
lambda global_token_id: global_to_local.lookup(global_token_id), tokens)
# Remove tokens that aren't actually available (looked up as -1):
available_tokens = tokens >= 0
tokens = tokens[available_tokens]
batch_indices = batch_indices[available_tokens]
updated_indices = tf.transpose(
tf.concat([[batch_indices], [tokens]], axis=0))
st = tf.sparse.SparseTensor(
updated_indices,
tf.ones(tf.size(tokens), dtype=FEATURE_DTYPE),
# Each client has at most MAX_TOKENS_SELECTED_PER_CLIENT distinct tokens.
dense_shape=[sparse_tokens.dense_shape[0], MAX_TOKENS_SELECTED_PER_CLIENT])
st = tf.sparse.reorder(st)
return st
return client_data.map(lambda b: BatchType(to_local_ids(b.tokens), b.tags))
# Simple test
client_keys, actual_num_tokens = keys_for_client(
batched_dataset3, MAX_TOKENS_SELECTED_PER_CLIENT)
client_keys = client_keys[:actual_num_tokens]
d = map_to_local_token_ids(batched_dataset3, client_keys)
batch = next(iter(d))
all_tokens = tf.gather(batch.tokens.indices, indices=1, axis=1)
# Confirm we have local indices in the range [0, MAX):
assert tf.math.reduce_max(all_tokens) < MAX_TOKENS_SELECTED_PER_CLIENT
assert tf.math.reduce_max(all_tokens) >= 0
在每个客户端上训练本地（子）模型
请注意federated_select将以与选择键相同的顺序返回选定的切片作为tf.data.Dataset。因此，我们首先定义一个实用程序函数，用于获取这样的数据集并将其转换为单个密集张量，该张量可以用作客户端模型的模型权重。
@tf.function
def slices_dataset_to_tensor(slices_dataset):
"""Convert a dataset of slices to a tensor."""
# Use batching to gather all of the slices into a single tensor.
d = slices_dataset.batch(MAX_TOKENS_SELECTED_PER_CLIENT,
drop_remainder=False)
iter_d = iter(d)
tensor = next(iter_d)
# Make sure we have consumed everything
opt = iter_d.get_next_as_optional()
tf.Assert(tf.logical_not(opt.has_value()), data=[''], name='CHECK_EMPTY')
return tensor
# Simple test
weights = np.random.random(
size=(MAX_TOKENS_SELECTED_PER_CLIENT, TAG_VOCAB_SIZE)).astype(np.float32)
model_slices_as_dataset = tf.data.Dataset.from_tensor_slices(weights)
weights2 = slices_dataset_to_tensor(model_slices_as_dataset)
np.testing.assert_array_equal(weights, weights2)
现在，我们拥有定义简单本地训练循环所需的所有组件，该循环将在每个客户端上运行。
@tf.function
def client_train_fn(model, client_optimizer,
model_slices_as_dataset, client_data,
client_keys, actual_num_tokens):
initial_model_weights = slices_dataset_to_tensor(model_slices_as_dataset)
assert len(model.trainable_variables) == 1
model.trainable_variables[0].assign(initial_model_weights)
# Only keep the "real" (unpadded) keys.
client_keys = client_keys[:actual_num_tokens]
client_data = map_to_local_token_ids(client_data, client_keys)
loss_fn = tf.keras.losses.BinaryCrossentropy()
for features, labels in client_data:
with tf.GradientTape() as tape:
predictions = model(features)
loss = loss_fn(labels, predictions)
grads = tape.gradient(loss, model.trainable_variables)
client_optimizer.apply_gradients(zip(grads, model.trainable_variables))
model_weights_delta = model.trainable_weights[0] - initial_model_weights
model_weights_delta = tf.slice(model_weights_delta, begin=[0, 0],
size=[actual_num_tokens, -1])
return client_keys, model_weights_delta
# Simple test
# Note if you execute this cell a second time, you need to also re-execute
# the preceeding cell to avoid "tf.function-decorated function tried to
# create variables on non-first call" errors.
on_device_model = create_logistic_model(MAX_TOKENS_SELECTED_PER_CLIENT,
TAG_VOCAB_SIZE)
client_optimizer = tf.keras.optimizers.SGD(learning_rate=0.001)
client_keys, actual_num_tokens = keys_for_client(
batched_dataset2, MAX_TOKENS_SELECTED_PER_CLIENT)
model_slices_as_dataset = tf.data.Dataset.from_tensor_slices(
np.zeros((MAX_TOKENS_SELECTED_PER_CLIENT, TAG_VOCAB_SIZE),
dtype=np.float32))
keys, delta = client_train_fn(
on_device_model,
client_optimizer,
model_slices_as_dataset,
client_data=batched_dataset3,
client_keys=client_keys,
actual_num_tokens=actual_num_tokens)
print(delta)
聚合 IndexedSlices
我们使用tff.federated_aggregate为IndexedSlices构建一个联邦稀疏求和。这个简单的实现有一个约束，即dense_shape在预先静态地已知。还要注意，此求和只是半稀疏的，因为客户端到服务器的通信是稀疏的，但服务器在accumulate和merge中维护求和的密集表示，并输出此密集表示。
def federated_indexed_slices_sum(slice_indices, slice_values, dense_shape):
"""
Sums IndexedSlices@CLIENTS to a dense @SERVER Tensor.
Intermediate aggregation is performed by converting to a dense representation,
which may not be suitable for all applications.
Args:
slice_indices: An IndexedSlices.indices tensor @CLIENTS.
slice_values: An IndexedSlices.values tensor @CLIENTS.
dense_shape: A statically known dense shape.
Returns:
A dense tensor placed @SERVER representing the sum of the client's
IndexedSclies.
"""
slices_dtype = slice_values.type_signature.member.dtype
zero = tff.tensorflow.computation(
lambda: tf.zeros(dense_shape, dtype=slices_dtype))()
@tf.function
def accumulate_slices(dense, client_value):
indices, slices = client_value
# There is no built-in way to add `IndexedSlices`, but
# tf.convert_to_tensor is a quick way to convert to a dense representation
# so we can add them.
return dense + tf.convert_to_tensor(
tf.IndexedSlices(slices, indices, dense_shape))
return tff.federated_aggregate(
(slice_indices, slice_values),
zero=zero,
accumulate=tff.tensorflow.computation(accumulate_slices),
merge=tff.tensorflow.computation(lambda d1, d2: tf.add(d1, d2, name='merge')),
report=tff.tensorflow.computation(lambda d: d))
构建一个最小的federated_computation作为测试
dense_shape = (6, 2)
indices_type = tff.TensorType(np.int64, (None,))
values_type = tff.TensorType(np.float32, (None, 2))
client_slice_type = tff.FederatedType(
(indices_type, values_type), tff.CLIENTS)
@tff.federated_computation(client_slice_type)
def test_sum_indexed_slices(indices_values_at_client):
indices, values = indices_values_at_client
return federated_indexed_slices_sum(indices, values, dense_shape)
print(test_sum_indexed_slices.type_signature)
({<int64[?],float32[?,2]>}@CLIENTS -> float32[6,2]@SERVER)
x = tf.IndexedSlices(
values=np.array([[2., 2.1], [0., 0.1], [1., 1.1], [5., 5.1]],
dtype=np.float32),
indices=[2, 0, 1, 5],
dense_shape=dense_shape)
y = tf.IndexedSlices(
values=np.array([[0., 0.3], [3.1, 3.2]], dtype=np.float32),
indices=[1, 3],
dense_shape=dense_shape)
# Sum one.
result = test_sum_indexed_slices([(x.indices, x.values)])
np.testing.assert_array_equal(tf.convert_to_tensor(x), result)
# Sum two.
expected = [[0., 0.1], [1., 1.4], [2., 2.1], [3.1, 3.2], [0., 0.], [5., 5.1]]
result = test_sum_indexed_slices([(x.indices, x.values), (y.indices, y.values)])
np.testing.assert_array_almost_equal(expected, result)
将所有内容整合到一个federated_computation中
现在，我们使用TFF将这些组件绑定到一个tff.federated_computation中。
DENSE_MODEL_SHAPE = (WORD_VOCAB_SIZE, TAG_VOCAB_SIZE)
client_data_type = tff.SequenceType(batched_dataset1.element_spec)
model_type = tff.TensorType(np.float32, shape=DENSE_MODEL_SHAPE)
我们使用基于联邦平均的基本服务器训练函数，以1.0的服务器学习率应用更新。重要的是，我们对模型应用更新（增量），而不是简单地平均客户端提供的模型，因为否则，如果模型的某个切片在给定轮次中没有被任何客户端训练，则其系数可能会归零。
@tff.tensorflow.computation
def server_update(current_model_weights, update_sum, num_clients):
average_update = update_sum / num_clients
return current_model_weights + average_update
我们需要几个额外的tff.tensorflow.computation组件
# Function to select slices from the model weights in federated_select:
select_fn = tff.tensorflow.computation(
lambda model_weights, index: tf.gather(model_weights, index))
# We need to wrap `client_train_fn` as a `tff.tensorflow.computation`, making
# sure we do any operations that might construct `tf.Variable`s outside
# of the `tf.function` we are wrapping.
@tff.tensorflow.computation
def client_train_fn_tff(model_slices_as_dataset, client_data, client_keys,
actual_num_tokens):
# Note this is amaller than the global model, using
# MAX_TOKENS_SELECTED_PER_CLIENT which is much smaller than WORD_VOCAB_SIZE.
# We would like a model of size `actual_num_tokens`, but we
# can't build the model dynamically, so we will slice off the padded
# weights at the end.
client_model = create_logistic_model(MAX_TOKENS_SELECTED_PER_CLIENT,
TAG_VOCAB_SIZE)
client_optimizer = tf.keras.optimizers.SGD(learning_rate=0.1)
return client_train_fn(client_model, client_optimizer,
model_slices_as_dataset, client_data, client_keys,
actual_num_tokens)
@tff.tensorflow.computation
def keys_for_client_tff(client_data):
return keys_for_client(client_data, MAX_TOKENS_SELECTED_PER_CLIENT)
现在，我们准备将所有部分组合在一起！
@tff.federated_computation(
tff.FederatedType(model_type, tff.SERVER), tff.FederatedType(client_data_type, tff.CLIENTS))
def sparse_model_update(server_model, client_data):
max_tokens = tff.federated_value(MAX_TOKENS_SELECTED_PER_CLIENT, tff.SERVER)
keys_at_clients, actual_num_tokens = tff.federated_map(
keys_for_client_tff, client_data)
model_slices = tff.federated_select(keys_at_clients, max_tokens, server_model,
select_fn)
update_keys, update_slices = tff.federated_map(
client_train_fn_tff,
(model_slices, client_data, keys_at_clients, actual_num_tokens))
dense_update_sum = federated_indexed_slices_sum(update_keys, update_slices,
DENSE_MODEL_SHAPE)
num_clients = tff.federated_sum(tff.federated_value(1.0, tff.CLIENTS))
updated_server_model = tff.federated_map(
server_update, (server_model, dense_update_sum, num_clients))
return updated_server_model
print(sparse_model_update.type_signature)
(<server_model=float32[13,4]@SERVER,client_data={<tokens=<indices=int64[?,2],values=int32[?],dense_shape=int64[2]>,tags=float32[?,4]>*}@CLIENTS> -> float32[13,4]@SERVER)
让我们训练一个模型！
现在我们有了训练函数，让我们试一试。
server_model = create_logistic_model(WORD_VOCAB_SIZE, TAG_VOCAB_SIZE)
server_model.compile( # Compile to make evaluation easy.
optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.0), # Unused
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=[
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.AUC(name='auc'),
tf.keras.metrics.Recall(top_k=2, name='recall_at_2'),
])
def evaluate(model, dataset, name):
metrics = model.evaluate(dataset, verbose=0)
metrics_str = ', '.join([f'{k}={v:.2f}' for k, v in
(zip(server_model.metrics_names, metrics))])
print(f'{name}: {metrics_str}')
print('Before training')
evaluate(server_model, batched_dataset1, 'Client 1')
evaluate(server_model, batched_dataset2, 'Client 2')
evaluate(server_model, batched_dataset3, 'Client 3')
model_weights = server_model.trainable_weights[0]
client_datasets = [batched_dataset1, batched_dataset2, batched_dataset3]
for _ in range(10): # Run 10 rounds of FedAvg
# We train on 1, 2, or 3 clients per round, selecting
# randomly.
cohort_size = np.random.randint(1, 4)
clients = np.random.choice([0, 1, 2], cohort_size, replace=False)
print('Training on clients', clients)
model_weights = sparse_model_update(
model_weights, [client_datasets[i] for i in clients])
server_model.set_weights([model_weights])
print('After training')
evaluate(server_model, batched_dataset1, 'Client 1')
evaluate(server_model, batched_dataset2, 'Client 2')
evaluate(server_model, batched_dataset3, 'Client 3')
Before training Client 1: loss=0.69, precision=0.00, auc=0.50, recall_at_2=0.60 Client 2: loss=0.69, precision=0.00, auc=0.50, recall_at_2=0.50 Client 3: loss=0.69, precision=0.00, auc=0.50, recall_at_2=0.40 Training on clients [0 1] Training on clients [0 2 1] Training on clients [2 0] Training on clients [1 0 2] Training on clients [2] Training on clients [2 0] Training on clients [1 2 0] Training on clients [0] Training on clients [2] Training on clients [1 2] After training Client 1: loss=0.67, precision=0.80, auc=0.91, recall_at_2=0.80 Client 2: loss=0.68, precision=0.67, auc=0.96, recall_at_2=1.00 Client 3: loss=0.65, precision=1.00, auc=0.93, recall_at_2=0.80