使用sklearn，构建联邦逻辑回归模型代码

联邦逻辑回归模型是一种机器学习模型，用于在多个数据源上训练模型，而不将数据传输到集中式服务器。在本例中，我们将使用scikit-learn库来构建联邦逻辑回归模型。代码如下：

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import numpy as np

# 创建两个数据集
X1, y1 = make_classification(n_samples=1000, n_features=10, n_informative=5, n_classes=2, random_state=1)
X2, y2 = make_classification(n_samples=1000, n_features=10, n_informative=5, n_classes=2, random_state=2)

# 将两个数据集分别分成训练集和测试集
X1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size=0.2, random_state=1)
X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y2, test_size=0.2, random_state=2)

# 定义联邦逻辑回归模型
class FedLogisticRegression:
    def __init__(self, num_clients, alpha=0.01, max_iter=100):
        self.num_clients = num_clients
        self.alpha = alpha
        self.max_iter = max_iter
        self.models = [LogisticRegression() for _ in range(num_clients)]

    def fit(self, X, y):
        num_samples = len(X)
        chunk_size = num_samples // self.num_clients

        for i in range(self.max_iter):
            # 每个客户端训练自己的模型
            for j in range(self.num_clients):
                X_chunk = X[j*chunk_size:(j+1)*chunk_size]
                y_chunk = y[j*chunk_size:(j+1)*chunk_size]
                self.models[j].fit(X_chunk, y_chunk)

            # 合并所有模型的权重
            weights = np.array([model.coef_ for model in self.models])
            avg_weights = np.mean(weights, axis=0)

            # 更新所有模型的权重
            for model in self.models:
                model.coef_ = avg_weights

    def predict(self, X):
        preds = []
        for model in self.models:
            preds.append(model.predict(X))
        return np.mean(preds, axis=0)

# 训练联邦逻辑回归模型
fed_lr = FedLogisticRegression(num_clients=2)
fed_lr.fit([X1_train, X2_train], [y1_train, y2_train])

# 在测试集上评估模型性能
X_test = np.concatenate((X1_test, X2_test))
y_test = np.concatenate((y1_test, y2_test))
y_pred = fed_lr.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

在上面的代码中，我们首先创建了两个不同的数据集。然后，我们将这些数据集分别分成训练集和测试集，并定义了一个`FedLogisticRegression`类来实现联邦逻辑回归模型。该模型将训练数据分配给多个客户端，每个客户端训练自己的逻辑回归模型。然后，所有客户端的模型权重被平均，并用于更新所有模型的权重。最后，我们在测试集上评估了模型的性能。