fix(framework) Fix aggregate_inplace() in strategy.py

src/py/flwr/server/strategy/aggregate.py

@@ -15,8 +15,8 @@
 """Aggregation functions for strategy implementations."""
 # mypy: disallow_untyped_calls=False
 
-from functools import reduce
-from typing import Any, Callable
+from functools import partial, reduce
+from typing import Any, Callable, Union
 
 import numpy as np
 
@@ -52,17 +52,31 @@ def aggregate_inplace(results: list[tuple[ClientProxy, FitRes]]) -> NDArrays:
         fit_res.num_examples / num_examples_total for _, fit_res in results
     ]
 
+    def _try_inplace(
+        x: NDArray, y: Union[NDArray, float], np_binary_op: np.ufunc
+    ) -> NDArray:
+        return (  # type: ignore[no-any-return]
+            np_binary_op(x, y, out=x)
+            if np.can_cast(y, x.dtype, casting="same_kind")
+            else np_binary_op(x, np.array(y, x.dtype), out=x)
+        )
+
     # Let's do in-place aggregation
     # Get first result, then add up each other
     params = [
-        scaling_factors[0] * x for x in parameters_to_ndarrays(results[0][1].parameters)
+        _try_inplace(x, scaling_factors[0], np_binary_op=np.multiply)
+        for x in parameters_to_ndarrays(results[0][1].parameters)
     ]
-    for i, (_, fit_res) in enumerate(results[1:]):
+
+    for i, (_, fit_res) in enumerate(results[1:], start=1):
         res = (
-            scaling_factors[i + 1] * x
+            _try_inplace(x, scaling_factors[i], np_binary_op=np.multiply)
             for x in parameters_to_ndarrays(fit_res.parameters)
         )
-        params = [reduce(np.add, layer_updates) for layer_updates in zip(params, res)]
+        params = [
+            reduce(partial(_try_inplace, np_binary_op=np.add), layer_updates)
+            for layer_updates in zip(params, res)
+        ]
 
     return params
 
@@ -128,7 +142,7 @@ def aggregate_bulyan(
 
     Parameters
     ----------
-    results: List[Tuple[NDArrays, int]]
+    results: list[tuple[NDArrays, int]]
         Weights and number of samples for each of the client.
     num_malicious: int
         The maximum number of malicious clients.
@@ -332,7 +346,7 @@ def _aggregate_n_closest_weights(
     ----------
     reference_weights: NDArrays
         The weights from which the distances will be computed
-    results: List[Tuple[NDArrays, int]]
+    results: list[tuple[NDArrays, int]]
         The weights from models
     beta_closest: int
         The number of the closest distance weights that will be averaged