diff --git a/src/utils_data.py b/src/utils_data.py
index 838586092434938865f6c91de08b3b3d8449d6b5..1a0058d33746eb509b0bd323878436af448acb3b 100644
--- a/src/utils_data.py
+++ b/src/utils_data.py
@@ -518,9 +518,8 @@ def apply_quantity_skew(list_clients : list, row_exp : dict, list_skews : list)
     dict_clients = [get_clients_data(n_clients_by_skew,
                                     int(n_max_samples * skew),
                                     row_exp['dataset'],
-                                    seed=row_exp['seed']) 
+                                    row_exp['nn_model']) 
                                     for skew in list_skews] 
-           
     list_clients = []
 
     for c in range(n_clients_by_skew):
diff --git a/src/utils_training.py b/src/utils_training.py
index ca2e523a3553279c11a0f244402c83a2929e9dc8..d1cc6d03d6e94316f29a4538d7e2bbc6cfda779f 100644
--- a/src/utils_training.py
+++ b/src/utils_training.py
@@ -212,7 +212,7 @@ def train_central(model: ImageClassificationBase, train_loader: DataLoader, val_
     # Move the model to the appropriate device
     model.to(device)
 
-    opt_func = torch.optim.SGD  # if row_exp['nn_model'] == "linear" else torch.optim.Adam
+    opt_func = torch.optim.Adam  # if row_exp['nn_model'] == "linear" else torch.optim.Adam
     lr = 0.001
     history = []
     optimizer = opt_func(model.parameters(), lr)