[revision] comments

.gitignore

@@ -173,6 +173,6 @@ codecov_upload.sh
 pypi_release.sh
 
 unit_test.ipynb
-
+/exp_logs
 # release cache
 

README.md

@@ -9,7 +9,6 @@ Federated learning (FL), proposed by Google at the very beginning, is recently a
 
 To relieve the burden of researchers in implementing FL algorithms and emancipate FL scientists from repetitive implementation of basic FL setting, we introduce highly customizable framework __FedLab__ in this work. __FedLab__ provides the necessary modules for FL simulation, including ***communication***, ***compression***, ***model optimization***, ***data partition*** and other ***functional modules***. Users can build FL simulation environment with custom modules like playing with LEGO bricks. For better understanding and easy usage, FL algorithm benchmark implemented in __FedLab__ are also presented.
 
-
 ## Documentations
 
 - [Documentation website](https://fedlab.readthedocs.io/en/master/)

fedlab/contrib/clients/fedavgclient.py

@@ -15,9 +15,5 @@
 from .client import SGDClientTrainer
 
 class FedAvgClientTrainer(SGDClientTrainer):
-    """_summary_
-
-    Args:
-        SGDClientTrainer (_type_): _description_
-    """
+    """Federated client with local SGD solver."""
     None

fedlab/contrib/clients/fedproxclient.py

@@ -19,6 +19,7 @@
 
 
 class FedProxClientTrainer(SGDClientTrainer):
+    """Federated client with local SGD with proximal term solver."""
     def train(self, model_parameters, mu, train_loader) -> None:
         """Client trains its local model on local dataset.
 

fedlab/contrib/clients/qfedavgclient.py

@@ -20,6 +20,7 @@
 
 
 class qFedAvgClientTrainer(SGDClientTrainer):
+    """Federated client with modified upload package and local SGD solver."""
     @property
     def uplink_package(self):
         return [self.delta, self.hk]

tutorial.ipynb

@@ -7,6 +7,62 @@
     "# Tutorials for FedLab users\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare your dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define clients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define server"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Choose simulation mode"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,