diff --git a/openfl-tutorials/interactive_api/PyTorch_TinyImageNet/workspace/pytorch_tinyimagenet.ipynb b/openfl-tutorials/interactive_api/PyTorch_TinyImageNet/workspace/pytorch_tinyimagenet.ipynb
index bbfadfd0860..09d1511c698 100644
--- a/openfl-tutorials/interactive_api/PyTorch_TinyImageNet/workspace/pytorch_tinyimagenet.ipynb
+++ b/openfl-tutorials/interactive_api/PyTorch_TinyImageNet/workspace/pytorch_tinyimagenet.ipynb
@@ -43,6 +43,8 @@
     "from torch.utils.data import Dataset\n",
     "from torch.utils.data import DataLoader\n",
     "import tqdm\n",
+    "import wandb\n",
+    "LOG_WANDB = True\n",
     "\n",
     "torch.manual_seed(0)\n",
     "np.random.seed(0)"
@@ -384,16 +386,22 @@
     "    net_model.to(device)\n",
     "\n",
     "    losses = []\n",
+    "    epochs = 1 #change this if you want more epochs per round\n",
     "\n",
-    "    for data, target in train_loader:\n",
-    "        data, target = torch.tensor(data).to(device), torch.tensor(\n",
-    "            target).to(device)\n",
-    "        optimizer.zero_grad()\n",
-    "        output = net_model(data)\n",
-    "        loss = loss_fn(output=output, target=target)\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "        losses.append(loss.detach().cpu().numpy())\n",
+    "    for epoch in range(epochs):\n",
+        "    for data, target in train_loader:\n",
+        "        data, target = torch.tensor(data).to(device), torch.tensor(\n",
+        "            target).to(device)\n",
+        "        optimizer.zero_grad()\n",
+        "        output = net_model(data)\n",
+        "        loss = loss_fn(output=output, target=target)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "        losses.append(loss.detach().cpu().numpy())\n",
+
+        "    if LOG_WANDB:\n",
+                "wandb.run.summary['step'] = epoch\n",
+                "wandb.log({'Training loss': np.mean(losses)})\n",
     "        \n",
     "    return {'train_loss': np.mean(losses),}\n",
     "\n",
diff --git a/openfl/component/aggregator/aggregator.py b/openfl/component/aggregator/aggregator.py
index f2d3c17b5fd..b864c720d0a 100644
--- a/openfl/component/aggregator/aggregator.py
+++ b/openfl/component/aggregator/aggregator.py
@@ -18,6 +18,8 @@
 from openfl.utilities import TensorKey
 from openfl.utilities.logs import write_metric
 
+import wandb
+LOG_WANDB = True
 
 class Aggregator:
     r"""An Aggregator is the central node in federated learning.
@@ -55,6 +57,18 @@ def __init__(self,
                  log_metric_callback=None,
                  **kwargs):
         """Initialize."""
+        #INITIALIZE WANDB WITH CORRECT NAME
+        #The following variable, my_aggregator_name, can be changed to whatever you want. Right now I suppose that the name of the collaborators of the federation is in the format 
+DATASETNAME_ENV_NUMBER
+        my_aggregator_name = '_'.join(set(element.split('_')[0] for element in authorized_cols))
+        if LOG_WANDB:
+            wandb.init(project="my_project", entity="my_group", group=f"{my_aggregator_name}", tags=["my_tag"],
+                       config={
+                           "num_clients": 4,
+                           "rounds": 100
+                       },
+                       name=f"Aggregator_{my_aggregator_name}"
+)
         self.round_number = 0
         self.single_col_cert_common_name = single_col_cert_common_name
 
@@ -837,6 +851,8 @@ def _compute_validation_related_task_metrics(self, task_name):
                 if agg_function:
                     self.logger.metric(f'Round {round_number}, aggregator: {task_name} '
                                        f'{agg_function} {agg_tensor_name}:\t{agg_results:f}')
+                    if LOG_WANDB:
+                        wandb.log({f"{task_name} {agg_tensor_name}": float(f"{agg_results}")}, step=round_number)
                 else:
                     self.logger.metric(f'Round {round_number}, aggregator: {task_name} '
                                        f'{agg_tensor_name}:\t{agg_results:f}')
@@ -893,6 +909,8 @@ def _end_of_round_check(self):
         # TODO This needs to be fixed!
         if self._time_to_quit():
             self.logger.info('Experiment Completed. Cleaning up...')
+            if LOG_WANDB:
+                wandb.finish()
         else:
             self.logger.info(f'Starting round {self.round_number}...')
 
diff --git a/openfl/component/collaborator/collaborator.py b/openfl/component/collaborator/collaborator.py
index 59193983880..eb346f15ce1 100644
--- a/openfl/component/collaborator/collaborator.py
+++ b/openfl/component/collaborator/collaborator.py
@@ -14,6 +14,8 @@
 from openfl.protocols import utils
 from openfl.utilities import TensorKey
 
+import wandb
+LOG_WANDB = True
 
 class DevicePolicy(Enum):
     """Device assignment policy."""
@@ -133,6 +135,14 @@ def set_available_devices(self, cuda: Tuple[str] = ()):
 
     def run(self):
         """Run the collaborator."""
+        if LOG_WANDB:
+            wandb.init(project="my_project", entity="my_group", tags=["my_tags"],
+                       config={
+                           "num_clients": 4,
+                           "rounds": 100,
+                       },
+                       name=self.collaborator_name
+)
         while True:
             tasks, round_number, sleep_time, time_to_quit = self.get_tasks()
             if time_to_quit:
@@ -148,6 +158,8 @@ def run(self):
                 self.tensor_db.clean_up(self.db_store_rounds)
 
         self.logger.info('End of Federation reached. Exiting...')
+        if LOG_WANDB:
+            wandb.finish()
 
     def run_simulation(self):
         """