# Generate synthetic data (pretending it's medical data)

import torch

import numpy as np

# Generate synthetic data for two hospitals

np.random.seed(0)

hospital1_data = np.random.randn(100, 5)  # 100 samples, 5 features

hospital1_labels = (np.random.randn(100) > 0).astype(int)  # Binary labels for readmission risk

np.random.seed(1)

hospital2_data = np.random.randn(200, 5)  # 200 samples, 5 features

hospital2_labels = (np.random.randn(200) > 0).astype(int)  # Binary labels for readmission risk

# Implement the Federated Learning process

import syft as sy

from torch import nn, optim

# Hook PyTorch to PySyft

hook = sy.TorchHook(torch)

# Create virtual workers representing two hospitals

hospital1 = sy.VirtualWorker(hook, id="hospital1")

hospital2 = sy.VirtualWorker(hook, id="hospital2")

# Share synthetic data with respective hospitals

data_ptr_hospital1 = torch.tensor(hospital1_data).send(hospital1)

labels_ptr_hospital1 = torch.tensor(hospital1_labels).send(hospital1)

data_ptr_hospital2 = torch.tensor(hospital2_data).send(hospital2)

labels_ptr_hospital2 = torch.tensor(hospital2_labels).send(hospital2)

# Define the Federated Learning model (neural network)

class FederatedModel(nn.Module):

    def __init__(self):

        super(FederatedModel, self).__init__()

        self.fc = nn.Linear(5, 1)

    def forward(self, x):

        return torch.sigmoid(self.fc(x))

# Define the loss function and optimizer

model = FederatedModel()

criterion = nn.BCELoss()

optimizer = optim.SGD(model.parameters(), lr=0.1)

# Federated Learning training loop

epochs = 10

for epoch in range(epochs):

    # Train on hospital1 data

    model.send(hospital1)

    optimizer.zero_grad()

    pred_hospital1 = model(data_ptr_hospital1.float())

    loss_hospital1 = criterion(pred_hospital1.view(-1), labels_ptr_hospital1.float())

    loss_hospital1.backward()

    optimizer.step()

    model.get()

    # Train on hospital2 data

    model.send(hospital2)

    optimizer.zero_grad()

    pred_hospital2 = model(data_ptr_hospital2.float())

    loss_hospital2 = criterion(pred_hospital2.view(-1), labels_ptr_hospital2.float())

    loss_hospital2.backward()

    optimizer.step()

    model.get()

# Aggregate the models (average)

model_avg_params = (model.fc.weight.data + model.copy().send(hospital1).get().fc.weight.data) / 2

model_avg_bias = (model.fc.bias.data + model.copy().send(hospital1).get().fc.bias.data) / 2

# Update the model with averaged parameters

model_avg = FederatedModel()

model_avg.fc.weight.data = model_avg_params

model_avg.fc.bias.data = model_avg_bias

# Get the final model from any hospital (e.g., hospital1)

final_model = model_avg.get()

# For simplicity, let's use the same synthetic data from hospital1 as the test data

test_data = torch.tensor(hospital1_data)

test_labels = torch.tensor(hospital1_labels)

# Evaluate the federated model on the test data

with torch.no_grad():

    model_avg.eval()  # Set the model to evaluation mode

    predictions = model_avg(test_data.float()).round().squeeze().detach().numpy()

# Calculate accuracy

accuracy = (predictions == test_labels.numpy()).mean()

print("Accuracy:", accuracy)