diff --git a/framework/py/flwr/serverapp/strategy/multikrum.py b/framework/py/flwr/serverapp/strategy/multikrum.py
index 2198c36086bf..cdb9d804b024 100644
--- a/framework/py/flwr/serverapp/strategy/multikrum.py
+++ b/framework/py/flwr/serverapp/strategy/multikrum.py
@@ -171,12 +171,16 @@ def compute_distances(records: list[ArrayRecord]) -> NDArray:
         A 2D array representing the distance matrix of squared L2 distances
         between input ArrayRecords
     """
-    # Formula: ||x - y||^2 = ||x||^2 + ||y||^2 - 2 * x.y
-    # Flatten records and stack them into a matrix
-    flat_w = np.stack(
-        [np.concatenate(rec.to_numpy_ndarrays(), axis=None).ravel() for rec in records],
-        axis=0,
-    )  # shape: (n, d) with n number of records and d the dimension of model
+    # Flatten each record to a 1D array efficiently
+    flat_model_arrays = []
+    for rec in records:
+        arrs = rec.to_numpy_ndarrays()
+        if len(arrs) == 1:
+            flat = arrs[0].ravel()
+        else:
+            flat = np.concatenate(arrs, axis=None).ravel()
+        flat_model_arrays.append(flat)
+    flat_w = np.stack(flat_model_arrays, axis=0)
 
     # Compute squared norms of each vector
     norms: NDArray = np.square(flat_w).sum(axis=1)  # shape (n,)
@@ -220,28 +224,19 @@ def select_multikrum(
     If `num_nodes_to_select` is set to 1, Multi-Krum reduces to classical Krum
     and only a single RecordDict is selected.
     """
-    # Construct list of ArrayRecord objects from replies
     record_key = list(contents[0].array_records.keys())[0]
-    # Recall aggregate_train first ensures replies only contain one ArrayRecord
     array_records = [cast(ArrayRecord, reply[record_key]) for reply in contents]
     distance_matrix = compute_distances(array_records)
 
-    # For each node, take the n-f-2 closest parameters vectors
     num_closest = max(1, len(array_records) - num_malicious_nodes - 2)
-    closest_indices = []
-    for distance in distance_matrix:
-        closest_indices.append(
-            np.argsort(distance)[1 : num_closest + 1].tolist()  # noqa: E203
-        )
+    # Get closest indices for each node efficiently using numpy
+    sorted_indices = np.argsort(distance_matrix, axis=1)
+    # Remove self (index 0) and get the n-f-2 closest (index 1 to num_closest)
+    closest_indices = sorted_indices[:, 1 : num_closest + 1]
 
-    # Compute the score for each node, that is the sum of the distances
-    # of the n-f-2 closest parameters vectors
-    scores = [
-        np.sum(distance_matrix[i, closest_indices[i]])
-        for i in range(len(distance_matrix))
-    ]
+    # Efficiently compute scores as sum of closest distances (vectorized)
+    scores = np.take_along_axis(distance_matrix, closest_indices, axis=1).sum(axis=1)
 
     # Choose the num_nodes_to_select lowest-scoring nodes (MultiKrum)
-    # and return their updates
     best_indices = np.argsort(scores)[:num_nodes_to_select]
     return [contents[i] for i in best_indices]