來(lái)源：DeepHub IMBA
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本文為你介紹聯(lián)邦學(xué)習(xí)的詳細(xì)講解及示例代碼。

聯(lián)邦學(xué)習(xí)也稱為協(xié)同學(xué)習(xí)，它可以在產(chǎn)生數(shù)據(jù)的設(shè)備上進(jìn)行大規(guī)模的訓(xùn)練，并且這些敏感數(shù)據(jù)保留在數(shù)據(jù)的所有者那里，本地收集、本地訓(xùn)練。在本地訓(xùn)練后，中央的訓(xùn)練協(xié)調(diào)器通過(guò)獲取分布模型的更新獲得每個(gè)節(jié)點(diǎn)的訓(xùn)練貢獻(xiàn)，但是不訪問(wèn)實(shí)際的敏感數(shù)據(jù)。

• 手機(jī)輸入法的下一個(gè)詞預(yù)測(cè)（e.g. McMahan et al. 2017, Hard et al. 2019）
• 健康研究（e.g. Kaissis et al. 2020, Sadilek et al. 2021）
• 汽車(chē)自動(dòng)駕駛（e.g. Zeng et al. 2021, OpenMined 的文章）
• “智能家居”系統(tǒng)（e.g. Matchi et al. 2019, Wu et al. 2020）

聯(lián)邦學(xué)習(xí)簡(jiǎn)介

聯(lián)邦學(xué)習(xí)代碼實(shí)現(xiàn)

virtualenv flower_env python==python3
source flower_env/bin/activate
pip install flwr==0.17.0# I'm running this example on a laptop (no gpu)
# so I am installing the cpu only version of PyTorch
# follow the instructions at https://pytorch.org/get-started/locally/
# if you want the gpu optionpip install torch==1.9.1+cpu torchvision==0.10.1+cpu \
? -f https://download.pytorch.org/whl/torch_stable.htmlpip install scikit-learn==0.24.0

import argparse
import flwr as fl
import torch
from pt_client import get_data, PTMLPClient

def get_eval_fn(model):
? # This `evaluate` function will be called after every round
? def evaluate(parameters: fl.common.Weights):
? ? ? loss, _, accuracy_dict = model.evaluate(parameters)
? ? ? return loss, accuracy_dict
return evaluate

if __name__ == "__main__":
? parser = argparse.ArgumentParser()
? parser.add_argument("-r", "--rounds", type=int, default=3,\
? ? ? ? ? help="number of rounds to train")
? args = parser.parse_args()
? torch.random.manual_seed(42)
? model = PTMLPClient(split="val")
? strategy = fl.server.strategy.FedAvg( \
? ? ? eval_fn=get_eval_fn(model),\
? ? ? )
? fl.server.start_server("[::]:8080", strategy=strategy, \
? ? ? ? ? config={"num_rounds": args.rounds})

class PTMLPClient(fl.client.NumPyClient, nn.Module):
? def __init__(self, dim_in=4, dim_h=32, \
? ? ? ? ? num_classes=3, lr=3e-4, split="alice"):
? ? ? super(PTMLPClient, self).__init__()
? ? ? self.dim_in = dim_in
? ? ? self.dim_h = dim_h
? ? ? self.num_classes = num_classes
? ? ? self.split = split
? ? ? ?
? ? ? self.w_xh = nn.Parameter(torch.tensor(\
? ? ? ? ? torch.randn(self.dim_in, self.dim_h) \
? ? ? ? ? / np.sqrt(self.dim_in * self.dim_h))\
? ? ? ? ? )
? ? ? self.w_hh = nn.Parameter(torch.tensor(\
? ? ? ? ? torch.randn(self.dim_h, self.dim_h) \
? ? ? ? ? / np.sqrt(self.dim_h * self.dim_h))\
? ? ? ? ? )
? ? ? self.w_hy = nn.Parameter(torch.tensor(\
? ? ? ? ? torch.randn(self.dim_h, self.num_classes) \
? ? ? ? ? / np.sqrt(self.dim_h * self.num_classes))\
? ? ? ? ? )
? ? ? self.lr = lr

def get_parameters(self):
? ? ? my_parameters = np.append(\
? ? ? ? ? self.w_xh.reshape(-1).detach().numpy(), \
? ? ? ? ? self.w_hh.reshape(-1).detach().numpy() \
? ? ? ? ? )
? ? ? my_parameters = np.append(\
? ? ? my_parameters, \
? ? ? ? ? self.w_hy.reshape(-1).detach().numpy() \
? ? ? ? ? )
? ? ? return my_parameters

def set_parameters(self, parameters):
? ? ? parameters = np.array(parameters)
? ? ? total_params = reduce(lambda a,b: a*b,\
? ? ? ? ? np.array(parameters).shape)
? ? ? expected_params = self.dim_in * self.dim_h \
? ? ? ? ? + self.dim_h**2 \
? ? ? ? ? + self.dim_h * self.num_classes
? ? ? ?
? ? ? start = 0
? ? ? stop = self.dim_in * self.dim_h
? ? ? self.w_xh = nn.Parameter(torch.tensor(\
? ? ? ? ? ? ? parameters[start:stop])\
? ? ? ? ? ? ? .reshape(self.dim_in, self.dim_h).float() \
? ? ? ? ? ? ? )
? ? ? ? ? ? ? ?
? ? ? start = stop
? ? ? stop += self.dim_h**2
? ? ? self.w_hh = nn.Parameter(torch.tensor(\
? ? ? ? ? ? ? parameters[start:stop])\
? ? ? ? ? ? ? .reshape(self.dim_h, self.dim_h).float() \
? ? ? ? ? ? ? )
? ? ? start = stop
? ? ? stop += self.dim_h * self.num_classes
? ? ? self.w_hy = nn.Parameter(torch.tensor(\
? ? ? ? ? ? ? parameters[start:stop])\
? ? ? ? ? ? ? .reshape(self.dim_h, self.num_classes).float()\
? ? ? ? ? ? ? )
? ? ? self.act = torch.relu
? ? ? ?
? ? ? self.optimizer = torch.optim.Adam(self.parameters())
? ? ? self.loss_fn = nn.CrossEntropyLoss()

def forward(self, x):
? ? ? x = self.act(torch.matmul(x, self.w_xh))
? ? ? x = self.act(torch.matmul(x, self.w_hh))
? ? ? x = torch.matmul(x, self.w_hy)
? ? ? return x
? ? ? ?
def get_loss(self, x, y):
? ? ? prediction = self.forward(x)
? ? ? loss = self.loss_fn(prediction, y)
? ? ? return loss

def fit(self, parameters, config=None, epochs=10):
? ? ? self.set_parameters(parameters)
? ? ? x, y = get_data(split=self.split)
? ? ? x, y = torch.tensor(x).float(), torch.tensor(y).long()
? ? ? self.train()
? ? ? for ii in range(epochs):
? ? ? ? ? self.optimizer.zero_grad()
? ? ? ? ? loss = self.get_loss(x, y)
? ? ? ? ? loss.backward()
? ? ? ? ? self.optimizer.step()
? ? ? ?
? ? ? loss, _, accuracy_dict = self.evaluate(self.get_parameters())
? ? ? return self.get_parameters(), len(y), \
? ? ? ? ? ? ? {"loss": loss, "accuracy": \
? ? ? ? ? ? ? accuracy_dict["accuracy"]}

? def evaluate(self, parameters, config=None):
? ? ? self.set_parameters(parameters)
? ? ? val_x, val_y = get_data(split="val")
? ? ? val_x = torch.tensor(val_x).float()
? ? ? val_y = torch.tensor(val_y).long()
? ? ? ?
? ? ? self.eval()
? ? ? prediction = self.forward(val_x)
? ? ? ?
? ? ? loss = self.loss_fn(prediction, val_y).detach().numpy()
? ? ? ?
? ? ? prediction_class = np.argmax(\
? ? ? ? ? prediction.detach().numpy(), axis=-1)
? ? ? ?
? ? ? accuracy = sklearn.metrics.accuracy_score(\
? ? ? ? ? val_y.numpy(), prediction_class)
? ? ? ?
? ? ? return float(loss), len(val_y), \
? ? ? ? ? {"accuracy":float(accuracy)}

def get_data(split="all"):
x, y = sklearn.datasets.load_iris(return_X_y=True)
? np.random.seed(42); np.random.shuffle(x)
? np.random.seed(42); np.random.shuffle(y)
? val_split = int(0.2 * x.shape[0])
? train_split = (x.shape[0] - val_split) // 2
? eval_x, eval_y = x[:val_split], y[:val_split] ? ?
? ?
? alice_x, alice_y = x[val_split:val_split + train_split], y[val_split:val_split + train_split]
? ?
? bob_x, bob_y = x[val_split + train_split:], y[val_split + train_split:]
? ?
? train_x, train_y = x[val_split:], y[val_split:]
? ?
? if split == "all":
? ? ? return train_x, train_y
? elif split == "alice":
? ? ? return alice_x, alice_y
? elif split == "bob":
? ? ? return bob_x, bob_y
? elif split == "val":
? ? ? return eval_x, eval_y
? else:
? ? ? print("error: split not recognized.")
? ? ? return None

if __name__ == "__main__":
? parser = argparse.ArgumentParser()
? parser.add_argument("-s", "--split", type=str, default="alice",\
? help="The training split to use, options are 'alice', 'bob', or 'all'")
? ?
? args = parser.parse_args()
? torch.random.manual_seed(42)
? ?
? fl.client.start_numpy_client("localhost:8080", client=PTMLPClient(split=args.split))

import argparse
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.metrics
import torch
import torch.nn as nn
from functools import reduce
import flwr as fl

python -m pt_server -r 40

python -m pt_client -s alice

python -m pt_client -s bob

聯(lián)邦學(xué)習(xí)的未來(lái)

編輯：王菁

校對(duì)：汪雨晴