Update name of Federated learning class name

fltk/core/client.py

@@ -8,7 +8,7 @@
 from fltk.core.node import Node
 from fltk.schedulers import MinCapableStepLR
 from fltk.strategy import get_optimizer
-from fltk.util.config import Config
+from fltk.util.config import FedLearningConfig
 
 
 class Client(Node):
@@ -17,7 +17,7 @@ class Client(Node):
     """
     running = False
 
-    def __init__(self, identifier: str, rank: int, world_size: int, config: Config):
+    def __init__(self, identifier: str, rank: int, world_size: int, config: FedLearningConfig):
         super().__init__(identifier, rank, world_size, config)
 
         self.loss_function = self.config.get_loss_function()()

fltk/core/federator.py

@@ -13,7 +13,7 @@
 from fltk.core.node import Node
 from fltk.strategy import get_aggregation
 from fltk.strategy import random_selection
-from fltk.util.config import Config
+from fltk.util.config import FedLearningConfig
 from fltk.util.data_container import DataContainer, FederatorRecord, ClientRecord
 
 NodeReference = Union[Node, str]
@@ -57,7 +57,7 @@ class Federator(Node):
     num_rounds: int
     exp_data: DataContainer
 
-    def __init__(self, identifier: str, rank: int, world_size: int, config: Config):
+    def __init__(self, identifier: str, rank: int, world_size: int, config: FedLearningConfig):
         super().__init__(identifier, rank, world_size, config)
         self.loss_function = self.config.get_loss_function()()
         self.num_rounds = config.rounds

fltk/core/node.py

@@ -6,7 +6,7 @@
 from torch.distributed import rpc
 from fltk.datasets.loader_util import get_dataset
 from fltk.nets import get_net
-from fltk.util.config import Config
+from fltk.util.config import FedLearningConfig
 from fltk.util.log import getLogger
 
 # Global dictionary to enable peer to peer communication between clients
@@ -30,7 +30,7 @@ class Node(abc.ABC):
     dataset: Any
     logger = getLogger(__name__)
 
-    def __init__(self, identifier: str, rank: int, world_size: int, config: Config):
+    def __init__(self, identifier: str, rank: int, world_size: int, config: FedLearningConfig):
         self.config = config
         self.id = identifier # pylint: disable=invalid-name
         self.rank = rank
@@ -40,7 +40,7 @@ def __init__(self, identifier: str, rank: int, world_size: int, config: Config):
         global_vars['self'] = self
         self._config(config)
 
-    def _config(self, config: Config):
+    def _config(self, config: FedLearningConfig):
         self.logger.setLevel(config.log_level.value)
         self.config.rank = self.rank
         self.config.world_size = self.world_size

fltk/datasets/distributed/cifar10.py

@@ -4,15 +4,15 @@
 
 from fltk.datasets.distributed.dataset import DistDataset
 from fltk.samplers import get_sampler
-from fltk.util.config import Config
+from fltk.util.config import FedLearningConfig
 
 
 class DistCIFAR10Dataset(DistDataset):
     """
     CIFAR10 Dataset implementation for Distributed learning experiments.
     """
 
-    def __init__(self, args: Config):
+    def __init__(self, args: FedLearningConfig):
         super(DistCIFAR10Dataset, self).__init__(args)
         self.init_train_dataset()
         self.init_test_dataset()

fltk/datasets/distributed/dataset.py

@@ -2,7 +2,7 @@
 from typing import Any
 
 # from fltk.util.arguments import Arguments
-from fltk.util.config import Config
+from fltk.util.config import FedLearningConfig
 from fltk.util.log import getLogger
 
 
@@ -15,7 +15,7 @@ class DistDataset:
     test_loader = None
     logger = getLogger(__name__)
 
-    def __init__(self, args: Config):
+    def __init__(self, args: FedLearningConfig):
         self.args = args
 
     def get_args(self):

fltk/launch.py

@@ -20,7 +20,7 @@
 from fltk.nets.util.reproducability import init_reproducibility, init_learning_reproducibility
 from fltk.util.cluster.client import ClusterManager
 from fltk.util.cluster.worker import should_distribute
-from fltk.util.config import DistributedConfig, Config, retrieve_config_network_params, get_learning_param_config, \
+from fltk.util.config import DistributedConfig, FedLearningConfig, retrieve_config_network_params, get_learning_param_config, \
     DistLearningConfig
 from fltk.util.environment import retrieve_or_init_env, retrieve_env_config
 from fltk.util.task.generator.arrival_generator import SimulatedArrivalGenerator, SequentialArrivalGenerator
@@ -204,7 +204,7 @@ def launch_single(arg_path: Path, conf_path: Path, rank: Rank, nic: Optional[NIC
     # We can iterate over all the experiments in the directory and execute it, as long as the system remains the same!
     # System = machines and its configuration
     print(conf_path)
-    s_conf = Config.from_yaml(conf_path)
+    s_conf = FedLearningConfig.from_yaml(conf_path)
     s_conf.world_size = conf.num_clients + 1
     s_conf.replication_id = prefix
     federator_node = Federator('federator', 0, conf.world_size, s_conf)
@@ -238,7 +238,7 @@ def launch_remote(arg_path: Path, conf_path: Path, rank: Rank, nic: Optional[NIC
     @return: None
     @rtype: None
     """
-    r_conf = Config.from_yaml(conf_path)
+    r_conf = FedLearningConfig.from_yaml(conf_path)
     r_conf.world_size = r_conf.num_clients + 1
     r_conf.replication_id = prefix
     if rank and not (nic and host):

fltk/util/config/__init__.py

@@ -6,10 +6,10 @@
 import logging
 
 from fltk.util.config.distributed_config import DistributedConfig
-from fltk.util.config.config import Config, get_safe_loader, DistLearningConfig
+from fltk.util.config.config import FedLearningConfig, get_safe_loader, DistLearningConfig
 
 
-def retrieve_config_network_params(conf: Config, nic=None, host=None):
+def retrieve_config_network_params(conf: FedLearningConfig, nic=None, host=None):
     if hasattr(conf, 'system'):
         system_attr = getattr(conf, 'system')
         if 'federator' in system_attr:

fltk/util/config/config.py

@@ -48,9 +48,10 @@ def get_safe_loader() -> yaml.SafeLoader:
             list(u'-+0123456789.'))
     return safe_loader
 
+
 @dataclass_json
 @dataclass
-class Config:
+class FedLearningConfig:
     batch_size: int = 1
     test_batch_size: int = 1000
     rounds: int = 2
@@ -141,18 +142,18 @@ def from_yaml(path: Path):
         you prefer to create json based configuration files.
 
         >>> with open("configs/example.json") as f:
-        >>>     Config.from_json(f.read())
+        >>>     FedLearningConfig.from_json(f.read())
 
         @param path: Path pointing to configuration yaml file.
         @type path: Path
         @return: Configuration dataclass representation of the configuration file.
-        @rtype: Config
+        @rtype: FedLearningConfig
         """
         getLogger(__name__).debug(f'Loading yaml from {path.absolute()}')
         safe_loader = get_safe_loader()
         with open(path) as file:
             content = yaml.load(file, Loader=safe_loader)
-            conf = Config.from_dict(content)
+            conf = FedLearningConfig.from_dict(content)
         return conf
 
 

fltk/util/config/distributed_config.py

@@ -167,7 +167,7 @@ def get_data_path(self) -> Path:
 
     def get_default_model_folder_path(self) -> Path:
         """
-        @deprecated Function to get the default model folder path from Config, needed for non-default training in the
+        @deprecated Function to get the default model folder path from FedLearningConfig, needed for non-default training in the
         FLTK framework.
         @return: Path representation of model path.
         @rtype: Path

tests/core/client_smoke_test.py

@@ -9,7 +9,7 @@
 
 from fltk.core.client import Client
 from fltk.core.distributed import DistClient
-from fltk.util.config import DistributedConfig, get_distributed_config, get_learning_param_config, Config, \
+from fltk.util.config import DistributedConfig, get_distributed_config, get_learning_param_config, FedLearningConfig, \
     DistLearningConfig
 
 from fltk.datasets.dataset import Dataset as DS
@@ -56,10 +56,10 @@ def test_parallel_client(self, name, net: Nets, dataset: Dataset):
 
 
 class TestFederatedLearnerSmoke(unittest.TestCase):
-    learning_config: Config = None
+    learning_config: FedLearningConfig = None
 
     def setUp(self):
-        self.learning_config = Config.from_yaml(Path(TEST_PARAM_CONF_FEDERATED))
+        self.learning_config = FedLearningConfig.from_yaml(Path(TEST_PARAM_CONF_FEDERATED))
 
     @parameterized.expand(
         [[f"{x.value}-{y.value}", x, y] for x, y in MODEL_SET_PAIRING]