From 6f81222d439c0dee87941f65f106b564b8a3ab5f Mon Sep 17 00:00:00 2001
From: "Pant, Akshay" <akshay.akshaypant@gmail.com>
Date: Fri, 10 Jan 2025 21:14:30 +0530
Subject: [PATCH] feat(secure aggregation): aggregation function WIP

---
 .../secure_aggregation.py                     | 124 ++++++++++++++++++
 1 file changed, 124 insertions(+)
 create mode 100644 openfl/interface/aggregation_functions/secure_aggregation.py

diff --git a/openfl/interface/aggregation_functions/secure_aggregation.py b/openfl/interface/aggregation_functions/secure_aggregation.py
new file mode 100644
index 0000000000..141214d4ff
--- /dev/null
+++ b/openfl/interface/aggregation_functions/secure_aggregation.py
@@ -0,0 +1,124 @@
+# Copyright 2020-2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+
+"""Secure aggregation module."""
+
+from typing import Iterator, Tuple
+
+import numpy as np
+import pandas as pd
+
+from openfl.interface.aggregation_functions.core import AggregationFunction
+from openfl.utilities import LocalTensor
+
+
+class SecureAggregation(AggregationFunction):
+    """
+    SecureAggregation class for performing secure aggregation of local tensors.
+    """
+    def call(
+        self,
+        local_tensors: list[LocalTensor],
+        db_iterator: Iterator[pd.Series],
+        tensor_name: str,
+        fl_round: int,
+        tags: Tuple[str],
+    ) -> np.ndarray:
+        """
+        Perform secure aggregation by calling the appropriate aggregation
+        methods based on the tags.
+
+        Args:
+            local_tensors (list[LocalTensor]): List of local tensors to be
+                aggregated.
+            db_iterator (Iterator[pd.Series]): Iterator over the database
+                series.
+            tensor_name (str): Name of the tensor.
+            fl_round (int): Federated learning round number.
+            tags (Tuple[str]): Tags indicating the type of aggregation to
+                perform.
+
+        Returns:
+            np.ndarray: Aggregated tensor.
+        """
+        self._aggregate_public_keys(local_tensors, tags)
+        self._aggregate_ciphertexts(local_tensors, tags)
+        self._aggregate_deciphertexts(local_tensors, tags)
+
+    def _aggregate_public_keys(
+        self,
+        local_tensors: list[LocalTensor],
+        tags: Tuple[str],
+    ):
+        """
+        Aggregate public keys from the local tensors.
+
+        Args:
+            local_tensors (list[LocalTensor]): List of local tensors
+                containing public keys.
+            tags (Tuple[str]): Tags indicating the type of aggregation to
+                perform.
+
+        Returns:
+            np.ndarray: Aggregated public keys tensor.
+        """
+        aggregated_tensor = []
+        if "public_key" in tags:
+            # Setting indices for the collaborators.
+            index = 1
+            for tensor in local_tensors:
+                # tensor[0] is public_key_1
+                # tensor[1] is public_key_2
+                aggregated_tensor.append(
+                    [index, tensor.tensor[0], tensor.tensor[1]]
+                )
+                index += 1
+
+            return np.array(aggregated_tensor)
+
+    def _aggregate_ciphertexts(
+        self,
+        local_tensors: list[LocalTensor],
+        tags: Tuple[str],
+    ):
+        """
+        Aggregate ciphertexts from the local tensors.
+
+        Args:
+            local_tensors (list[LocalTensor]): List of local tensors
+                containing ciphertexts.
+            tags (Tuple[str]): Tags indicating the type of aggregation to
+                perform.
+
+        Returns:
+            np.ndarray: Aggregated ciphertexts tensor.
+        """
+        aggregated_tensor = []
+        if "ciphertext" in tags:
+            aggregated_tensor = [tensor.tensor for tensor in local_tensors]
+
+            return np.array(aggregated_tensor)
+
+    def _aggregate_deciphertexts(
+        self,
+        local_tensors: list[LocalTensor],
+        tags: Tuple[str],
+    ) -> np.ndarray:
+        """
+        Aggregate deciphertexts from the local tensors.
+
+        Args:
+            local_tensors (list[LocalTensor]): List of local tensors
+                containing deciphertexts.
+            tags (Tuple[str]): Tags indicating the type of aggregation to
+                perform.
+
+        Returns:
+            np.ndarray: Aggregated deciphertexts tensor.
+        """
+        aggregated_tensor = []
+        if "deciphertext" in tags:
+            aggregated_tensor = [tensor.tensor for tensor in local_tensors]
+
+            return np.array(aggregated_tensor)