diff --git a/examples/mxnet_mnist.py b/examples/mxnet_mnist.py
index 3ed7f8f426..63f3dddc76 100644
--- a/examples/mxnet_mnist.py
+++ b/examples/mxnet_mnist.py
@@ -112,27 +112,23 @@ def evaluate(model, data_iter, context):
 model.cast(args.dtype)
 model.hybridize()
 
-# Define hyper parameters
+# Create optimizer
 optimizer_params = {'momentum': args.momentum,
                     'learning_rate': args.lr * hvd.size(),
                     'rescale_grad': 1.0 / args.batch_size}
-
-# Add Horovod Distributed Optimizer
 opt = mx.optimizer.create('sgd', **optimizer_params)
+# Horovod: wrap optimizer with DistributedOptimizer
 opt = hvd.DistributedOptimizer(opt)
 
 # Initialize parameters
 initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="in",
                              magnitude=2)
+# Horovod: wrap initializer with DistributedInitializer
+initializer = hvd.DistributedInitializer(initializer)
 model.initialize(initializer, ctx=context)
 
-# Fetch and broadcast parameters
-params = model.collect_params()
-if params is not None:
-    hvd.broadcast_parameters(params, root_rank=0)
-
 # Create trainer, loss function and train metric
-trainer = gluon.Trainer(params, opt, kvstore=None)
+trainer = gluon.Trainer(model.collect_params(), opt, kvstore=None)
 loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
 metric = mx.metric.Accuracy()
 
diff --git a/horovod/mxnet/__init__.py b/horovod/mxnet/__init__.py
index 5250d7f95f..9b48b747a8 100644
--- a/horovod/mxnet/__init__.py
+++ b/horovod/mxnet/__init__.py
@@ -68,6 +68,21 @@ def set_wd_mult(self, args_wd_mult):
         self._optimizer.set_wd_mult(args_wd_mult)
 
 
+# DistributedInitializer wraps MXNet Initializer which initializes and broadcasts parameter.
+class DistributedInitializer(mx.initializer.Initializer):
+    def __init__(self, init, root_rank=0):
+        self._init = init
+        self._root_rank = root_rank
+
+    def __call__(self, desc, arr):
+        self._init(desc, arr)
+        broadcast_(arr, self._root_rank, desc)
+        arr.wait_to_read()
+
+    def _init_weight(self, name, arr):
+        self._init._init_weight(name, arr)
+
+
 def broadcast_parameters(params, root_rank=0):
     """
     Broadcasts the parameters from root rank to all other processes.