diff --git a/src/ai/computer_vision.py b/src/ai/computer_vision.py
new file mode 100644
index 000000000..5f4b5b422
--- /dev/null
+++ b/src/ai/computer_vision.py
@@ -0,0 +1,66 @@
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+
+class ComputerVision:
+    def __init__(self, model_path):
+        self.model = load_model(model_path)
+
+    def preprocess_image(self, image_path):
+        """Load and preprocess the image."""
+        image = cv2.imread(image_path)
+        image = cv2.resize(image, (224, 224))  # Resize to match model input
+        image = image.astype('float32') / 255.0  # Normalize
+        return np.expand_dims(image, axis=0)
+
+    def predict(self, image_path):
+        """Predict the class of the image."""
+        processed_image = self.preprocess_image(image_path)
+        predictions = self.model.predict(processed_image)
+        return predictions
+
+    def detect_objects(self, image_path):
+        """Detect objects in the image using a pre-trained model."""
+        image = cv2.imread(image_path)
+        # Assuming a YOLO model for object detection
+        # Load YOLO model and perform detection (pseudo-code)
+        # net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
+        # blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), 0, crop=False)
+        # net.setInput(blob)
+        # outs = net.forward(output_layers)
+        # Process the outputs to extract bounding boxes and class labels
+        # return detected_objects
+
+### federated_learning.py
+
+This file implements a simple federated learning setup using TensorFlow Federated.
+
+```python
+import tensorflow as tf
+import tensorflow_federated as tff
+
+class FederatedLearning:
+    def __init__(self, model_fn):
+        self.model_fn = model_fn
+
+    def create_federated_data(self, client_data):
+        """Create federated data from client datasets."""
+        return [tff.simulation.ClientData.from_clients_and_fn(
+            client_ids=[str(i)],
+            create_client_data_fn=lambda: client_data[i]
+        ) for i in range(len(client_data))]
+
+    def train(self, federated_data):
+        """Train the model using federated learning."""
+        federated_averaging = tff.learning.build_federated_averaging_process(self.model_fn)
+        state = federated_averaging.initialize()
+
+        for round_num in range(1, 11):  # Train for 10 rounds
+            state, metrics = federated_averaging.next(state, federated_data)
+            print(f'Round {round_num}, Metrics: {metrics}')
+
+    def evaluate(self, federated_data):
+        """Evaluate the model on federated data."""
+        evaluation = tff.learning.build_federated_evaluation(self.model_fn)
+        metrics = evaluation(state.model, federated_data)
+        print(f'Evaluation Metrics: {metrics}')