diff --git a/fed_algo_cs/client_base.py b/fed_algo_cs/client_base.py
index 7d86735..85ea227 100644
--- a/fed_algo_cs/client_base.py
+++ b/fed_algo_cs/client_base.py
@@ -120,7 +120,7 @@ class FedYoloClient(object):
         # track_model = self.model.module if is_ddp else self.model
         ema = util.EMA(self.model) if args.local_rank == 0 else None
 
-        print(type(self.train_dataset))
+        # print(type(self.train_dataset))
 
         # ---- Data ----
         dataset = Dataset(
@@ -188,7 +188,7 @@ class FedYoloClient(object):
             loss_dfl_meter = util.AverageMeter()
 
             for i, (images, targets) in enumerate(loader):
-                print(f"Client {self.name} - Epoch {epoch + 1}/{args.epochs} - Step {i + 1}/{num_steps}")
+                # print(f"Client {self.name} - Epoch {epoch + 1}/{args.epochs} - Step {i + 1}/{num_steps}")
                 step = i + epoch * num_steps
 
                 # scheduler per-step (your util.LinearLR expects step)
@@ -257,9 +257,9 @@ class FedYoloClient(object):
                             else self.model
                         )
                     # print loss to test
-                    print(
-                        f"loss: {total_loss.item() * accumulate:.4f}, box: {box_loss.item():.4f}, cls: {cls_loss.item():.4f}, dfl: {dfl_loss.item():.4f}"
-                    )
+                    # print(
+                    #     f"loss: {total_loss.item() * accumulate:.4f}, box: {box_loss.item():.4f}, cls: {cls_loss.item():.4f}, dfl: {dfl_loss.item():.4f}"
+                    # )
                 torch.cuda.synchronize()
 
         # ---- Final average loss (per image) over the whole epoch span ----