✅ Add krum test

tests/test_aggregator.py

@@ -3,6 +3,7 @@
 import torch
 from federatedlearning.server.aggregations.aggregators import (
     average_weights,
+    krum,
     median_weights,
 )
 
@@ -87,5 +88,63 @@ def test_median_weights(self) -> None:
             )
 
 
+class TestKrumAlgorithm(unittest.TestCase):
+    def test_krum_basic(self) -> None:
+        weights = [
+            {
+                "a": torch.tensor([1.0, 2.0]),
+                "b": torch.tensor([3.0, 4.0]),
+            },  # Benign
+            {
+                "a": torch.tensor([1.1, 2.1]),
+                "b": torch.tensor([3.1, 4.1]),
+            },  # Benign
+            {
+                "a": torch.tensor([10.0, 20.0]),
+                "b": torch.tensor([30.0, 40.0]),
+            },  # Malicious
+        ]
+        f = 1
+        # Weights to be considered most normal.
+        expected_result: dict[str, torch.Tensor] = {
+            "a": torch.tensor([1.0, 2.0]),
+            "b": torch.tensor([3.0, 4.0]),
+        }
+        result: dict[str, torch.Tensor] = krum(weights, f)
+        for key in expected_result.keys():
+            self.assertTrue(
+                torch.equal(expected_result[key], result[key]),
+                f"Weights median incorrectly for key '{key}'.",
+            )
+
+    def test_not_enough_weights(self) -> None:
+        weights = [
+            {"a": torch.tensor([1.0, 2.0]), "b": torch.tensor([3.0, 4.0])}
+        ]
+        f = 1
+
+        with self.assertRaises(ValueError):
+            krum(weights, f)
+
+    def test_all_same_weights(self) -> None:
+        weights = [
+            {"a": torch.tensor([1.0, 1.0]), "b": torch.tensor([1.0, 1.0])},
+            {"a": torch.tensor([1.0, 1.0]), "b": torch.tensor([1.0, 1.0])},
+            {"a": torch.tensor([1.0, 1.0]), "b": torch.tensor([1.0, 1.0])},
+        ]
+        f = 1
+        # All weights are equal, so any of them can be chosen
+        expected_result: dict[str, torch.Tensor] = {
+            "a": torch.tensor([1.0, 1.0]),
+            "b": torch.tensor([1.0, 1.0]),
+        }
+        result: dict[str, torch.Tensor] = krum(weights, f)
+        for key in expected_result.keys():
+            self.assertTrue(
+                torch.equal(expected_result[key], result[key]),
+                f"Weights median incorrectly for key '{key}'.",
+            )
+
+
 if __name__ == "__main__":
     unittest.main()