--- a
+++ b/federated_learning_healthcare.py
@@ -0,0 +1,92 @@
+# 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)
+