diff --git a/fltk/core/distributed/client.py b/fltk/core/distributed/client.py
index 8212d015..b031638d 100644
--- a/fltk/core/distributed/client.py
+++ b/fltk/core/distributed/client.py
@@ -13,6 +13,7 @@
 from fltk.nets import get_net
 from fltk.nets.util import calculate_class_precision, calculate_class_recall, save_model, load_model_from_file
 from fltk.schedulers import MinCapableStepLR, LearningScheduler
+from fltk.strategy import get_optimizer
 from fltk.util.config import DistributedConfig, DistLearningConfig
 from fltk.util.results import EpochData
 
@@ -68,8 +69,8 @@ def prepare_learner(self, distributed: bool = False) -> None:
             # Wrap the model to use pytorch DistributedDataParallel wrapper for all reduce.
             self.model = torch.nn.parallel.DistributedDataParallel(self.model)
 
-        # Currently, it is assumed to use an SGD optimizer. **kwargs need to be used to launch this properly
-        optim_type: Type[torch.optim.Optimizer] = self.learning_params.get_optimizer()
+        # Currently, it is assumed to use an SGD optimizer, using non-federated optimizer types.
+        optim_type = get_optimizer(self.learning_params.optimizer, federated=False)
         self.optimizer = optim_type(self.model.parameters(), **self.learning_params.optimizer_args)
         self.scheduler = MinCapableStepLR(self.optimizer,
                                           self.learning_params.scheduler_step_size,
diff --git a/fltk/strategy/optimization/__init__.py b/fltk/strategy/optimization/__init__.py
index a394f55f..34720f08 100644
--- a/fltk/strategy/optimization/__init__.py
+++ b/fltk/strategy/optimization/__init__.py
@@ -7,7 +7,7 @@
 from .fed_nova import FedNova
 
 
-def get_optimizer(name: Optimizations) -> Type[torch.optim.Optimizer]:
+def get_optimizer(name: Optimizations, federated: bool = True) -> Type[torch.optim.Optimizer]:
     """
     Helper function to get specific Optimization class references.
     @param name: Optimizer class reference.
@@ -20,7 +20,10 @@ def get_optimizer(name: Optimizations) -> Type[torch.optim.Optimizer]:
             Optimizations.adam: torch.optim.Adam,
             Optimizations.adam_w: torch.optim.AdamW,
             Optimizations.sgd: torch.optim.SGD,
+        }
+    if federated:
+        optimizers.update({
             Optimizations.fedprox: FedProx,
             Optimizations.fednova: FedNova
-        }
+        })
     return optimizers[name]
diff --git a/fltk/util/config/__init__.py b/fltk/util/config/__init__.py
index 7690692f..139a1d5a 100644
--- a/fltk/util/config/__init__.py
+++ b/fltk/util/config/__init__.py
@@ -6,7 +6,7 @@
 import logging
 
 from fltk.util.config.distributed_config import DistributedConfig
-from fltk.util.config.config import FedLearningConfig, get_safe_loader, DistLearningConfig
+from fltk.util.config.learning_config import FedLearningConfig, get_safe_loader, DistLearningConfig
 
 
 def retrieve_config_network_params(conf: FedLearningConfig, nic=None, host=None):
diff --git a/fltk/util/config/config.py b/fltk/util/config/learning_config.py
similarity index 89%
rename from fltk/util/config/config.py
rename to fltk/util/config/learning_config.py
index c84e40bd..7d644333 100644
--- a/fltk/util/config/config.py
+++ b/fltk/util/config/learning_config.py
@@ -49,20 +49,26 @@ def get_safe_loader() -> yaml.SafeLoader:
     return safe_loader
 
 
+# fixme: With python 3.10, this can be done with the dataclass kw_only kwarg.
 @dataclass_json
 @dataclass
-class FedLearningConfig:
-    batch_size: int = 1
-    test_batch_size: int = 1000
+class LearningConfig:
+    batch_size: int = field(metadata=dict(required=False, missing=128))
+    test_batch_size: int = field(metadata=dict(required=False, missing=128))
+    cuda: bool = field(metadata=dict(required=False, missing=False))
+    scheduler_step_size: int = field(metadata=dict(required=False, missing=50))
+    scheduler_gamma: float = field(metadata=dict(required=False, missing=0.5))
+
+
+@dataclass_json
+@dataclass
+class FedLearningConfig(LearningConfig):
     rounds: int = 2
     epochs: int = 1
     lr: float = 0.01
     momentum: float = 0.1
-    cuda: bool = False
     shuffle: bool = False
     log_interval: int = 10
-    scheduler_step_size: int = 50
-    scheduler_gamma: float = 0.5
     min_lr: float = 1e-10
     rng_seed = 0
 
@@ -169,8 +175,8 @@ def from_yaml(path: Path):
 
 
 @dataclass_json
-@dataclass(frozen=True)
-class DistLearningConfig:  # pylint: disable=too-many-instance-attributes
+@dataclass
+class DistLearningConfig(LearningConfig):  # pylint: disable=too-many-instance-attributes
     """
     Class encapsulating LearningParameters, for now used under DistributedLearning.
     """
@@ -182,13 +188,10 @@ class DistLearningConfig:  # pylint: disable=too-many-instance-attributes
     learning_rate: float
     learning_decay: float
     loss: str
-    optimizer: str
+    optimizer: Optimizations
     optimizer_args: Dict[str, Any]
-    scheduler_step_size: int
-    scheduler_gamma: float
-    min_lr: float
 
-    cuda: bool
+    min_lr: float
     seed: int
 
     @staticmethod
@@ -216,11 +219,3 @@ def get_loss(self) -> Type:
         """
         return self.__safe_get(_available_loss, self.loss)
 
-    def get_optimizer(self) -> Type[torch.optim.Optimizer]:
-        """
-        Function to obtain the loss function Type that was given via commandline to be used during the training
-        execution.
-        @return: Type corresponding to the Optimizer to be used during training.
-        @rtype: Type[torch.optim.Optimizer]
-        """
-        return self.__safe_get(_available_optimizer, self.optimizer)