diff --git a/examples/FedSimSiam/client/data.py b/examples/FedSimSiam/client/data.py
index 95b10e7db..c96724e1e 100644
--- a/examples/FedSimSiam/client/data.py
+++ b/examples/FedSimSiam/client/data.py
@@ -39,7 +39,7 @@ def load_data(data_path, is_train=True):
         data_path = os.environ.get(
             "FEDN_DATA_PATH", abs_path+"/data/clients/1/cifar10.pt")
 
-    data = torch.load(data_path)
+    data = torch.load(data_path, weights_only=True)
 
     if is_train:
         X = data["x_train"]
diff --git a/examples/huggingface/client/data.py b/examples/huggingface/client/data.py
index dcbc0d167..d1c517578 100644
--- a/examples/huggingface/client/data.py
+++ b/examples/huggingface/client/data.py
@@ -11,7 +11,7 @@
 def load_data(data_path=None, is_train=True):
     if data_path is None:
         data_path = os.environ.get("FEDN_DATA_PATH", abs_path + "/data/clients/1/enron_spam.pt")
-    data = torch.load(data_path)
+    data = torch.load(data_path, weights_only=True)
     if is_train:
         X = data["X_train"]
         y = data["y_train"]
diff --git a/examples/mnist-pytorch-DPSGD/client/data.py b/examples/mnist-pytorch-DPSGD/client/data.py
index b7cc5b8e7..18a674355 100644
--- a/examples/mnist-pytorch-DPSGD/client/data.py
+++ b/examples/mnist-pytorch-DPSGD/client/data.py
@@ -35,7 +35,7 @@ def load_data(data_path, is_train=True):
         data_path = os.environ.get("FEDN_DATA_PATH", abs_path + "/data/clients/1/mnist.pt")
 
     print("data path: ", data_path)
-    data = torch.load(data_path)
+    data = torch.load(data_path, weights_only=True)
 
     if is_train:
         X = data["x_train"]
diff --git a/examples/mnist-pytorch/client/data.py b/examples/mnist-pytorch/client/data.py
index b921f3132..f10fd1558 100644
--- a/examples/mnist-pytorch/client/data.py
+++ b/examples/mnist-pytorch/client/data.py
@@ -33,7 +33,7 @@ def load_data(data_path, is_train=True):
     if data_path is None:
         data_path = os.environ.get("FEDN_DATA_PATH", abs_path + "/data/clients/1/mnist.pt")
 
-    data = torch.load(data_path)
+    data = torch.load(data_path, weights_only=True)
 
     if is_train:
         X = data["x_train"]