add transmitter, client_choose, aggregation interface

1. add transmitter, client_choose, aggregation interface to Lib.
2. add example of how to use new added interface.

Signed-off-by: Jie Pu <pujie2@huawei.com>
Signed-off-by: XinYao1994 <xyao@cs.hku.hk>

update

build/crd-samples/sedna/federatedlearningjob_yolo_v1alpha1.yaml

@@ -0,0 +1,54 @@
+apiVersion: sedna.io/v1alpha1
+kind: FederatedLearningJob
+metadata:
+  name: yolo-v5
+spec:
+  pretrainedModel: # option
+    name: "yolo-v5-pretrained-model"
+  transimitter: # option
+    ws: { } # option, by default
+    s3: # option, but at least one
+      aggDataPath: "s3://sedna/fl/aggregation_data"
+      credentialName: mysecret
+  aggregationWorker:
+    model:
+      name: "yolo-v5-model"
+    template:
+      spec:
+        nodeName: "sedna-control-plane"
+        containers:
+          - image: kubeedge/sedna-fl-aggregation:mistnetyolo
+            name: agg-worker
+            imagePullPolicy: IfNotPresent
+            env: # user defined environments
+              - name: "cut_layer"
+                value: "4"
+              - name: "epsilon"
+                value: "100"
+              - name: "aggregation_algorithm"
+                value: "mistnet"
+              - name: "batch_size"
+            resources: # user defined resources
+              limits:
+                memory: 8Gi
+  trainingWorkers:
+    - dataset:
+        name: "coco-dataset"
+      template:
+        spec:
+          nodeName: "edge-node"
+          containers:
+            - image: kubeedge/sedna-fl-train:mistnetyolo
+              name: train-worker
+              imagePullPolicy: IfNotPresent
+              args: [ "-i", "1" ]
+              env: # user defined environments
+                - name: "batch_size"
+                  value: "32"
+                - name: "learning_rate"
+                  value: "0.001"
+                - name: "epochs"
+                  value: "1"
+              resources: # user defined resources
+                limits:
+                  memory: 2Gi

examples/federated_learning/yolov5_coco128_mistnet/README.md

@@ -0,0 +1,238 @@
+# Collaboratively Train Yolo-v5 Using MistNet on COCO128 Dataset
+
+This case introduces how to train a federated learning job with an aggregation algorithm named MistNet in MNIST
+handwritten digit classification scenario. Data is scattered in different places (such as edge nodes, cameras, and
+others) and cannot be aggregated at the server due to data privacy and bandwidth. As a result, we cannot use all the
+data for training. In some cases, edge nodes have limited computing resources and even have no training capability. The
+edge cannot gain the updated weights from the training process. Therefore, traditional algorithms (e.g., federated
+average), which usually aggregate the updated weights trained by different edge clients, cannot work in this scenario.
+MistNet is proposed to address this issue.
+
+MistNet partitions a DNN model into two parts, a lightweight feature extractor at the edge side to generate meaningful
+features from the raw data, and a classifier including the most model layers at the cloud to be iteratively trained for
+specific tasks. MistNet achieves acceptable model utility while greatly reducing privacy leakage from the released
+intermediate features.
+
+## Object Detection Experiment
+
+> Assume that there are two edge nodes and a cloud node. Data on the edge nodes cannot be migrated to the cloud due to privacy issues.
+> Base on this scenario, we will demonstrate the mnist example.
+
+### Prepare Nodes
+
+```
+CLOUD_NODE="cloud-node-name"
+EDGE1_NODE="edge1-node-name"
+EDGE2_NODE="edge2-node-name"
+```
+
+### Install Sedna
+
+Follow the [Sedna installation document](/docs/setup/install.md) to install Sedna.
+
+### Prepare Dataset
+
+Download [dataset](https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip) and do data partition
+
+```
+wget https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
+unzip coco128.zip -d data
+rm coco128.zip
+python partition.py ./data 2
+```
+
+move ```./data/1``` to `/data` of ```EDGE1_NODE```.
+
+```
+mkdir -p /data
+cd /data
+mv ./data/1 ./
+```
+
+move ```./data/2``` to `/data` of ```EDGE2_NODE```.
+
+```
+mkdir -p /data
+cd /data
+mv ./data/2 ./
+```
+
+### Prepare Images
+
+This example uses these images:
+
+1. aggregation worker: ```kubeedge/sedna-example-federated-learning-mistnet:v0.3.0```
+2. train worker: ```kubeedge/sedna-example-federated-learning-mistnet-client:v0.3.0```
+
+These images are generated by the script [build_images.sh](/examples/build_image.sh).
+
+### Create Federated Learning Job
+
+#### Create Dataset
+
+create dataset for `$EDGE1_NODE`
+
+```n
+kubectl create -f - <<EOF
+apiVersion: sedna.io/v1alpha1
+kind: Dataset
+metadata:
+  name: "coco-dataset"
+spec:
+  url: "/data/test.txt"
+  format: "txt"
+  nodeName: edge-node
+EOF
+```
+
+create dataset for `$EDGE2_NODE`
+
+```
+kubectl create -f - <<EOF
+apiVersion: sedna.io/v1alpha1
+kind: Dataset
+metadata:
+  name: "coco-dataset"
+spec:
+  url: "/data/test.txt"
+  format: "txt"
+  nodeName: edge-node
+EOF
+```
+
+#### Create Model
+
+create the directory `/model` in the host of `$EDGE1_NODE`
+
+```
+mkdir /model
+```
+
+create the directory `/model` in the host of `$EDGE2_NODE`
+
+```
+mkdir /model
+```
+
+```
+TODO: put pretrained model on nodes.
+```
+
+create model
+
+```
+kubectl create -f - <<EOF
+apiVersion: sedna.io/v1alpha1
+kind: Model
+metadata:
+  name: "yolo-v5-model"
+spec:
+  url: "/model/yolo.pb"
+  format: "pb"
+EOF
+```
+
+#### Start Federated Learning Job
+
+```
+kubectl create -f - <<EOF
+apiVersion: sedna.io/v1alpha1
+kind: FederatedLearningJob
+metadata:
+  name: mistnet-on-mnist-dataset
+spec:
+  stopCondition:
+    operator: "or" # and
+      conditions:
+        - operator: ">"
+          threshold: 100
+          metric: rounds
+        - operator: ">"
+          threshold: 0.95
+          metric: targetAccuracy
+        - operator: "<"
+          threshold: 0.03
+          metric: deltaLoss
+  aggregationTrigger:
+    condition:
+      operator: ">"
+      threshold: 5
+      metric: num_of_ready_clients
+  aggregationWorker:
+    model:
+      name: "mistnet-on-mnist-model"
+    template:
+      spec:
+        nodeName: $CLOUD_NODE
+        containers:
+          - image: kubeedge/sedna-example-federated-learning-mistnet-on-mnist-dataset-aggregation:v0.4.0
+            name:  agg-worker
+            imagePullPolicy: IfNotPresent
+            env: # user defined environments
+              - name: "cut_layer"
+                value: "4"
+              - name: "epsilon"
+                value: "100"
+              - name: "aggregation_algorithm"
+                value: "mistnet"
+              - name: "batch_size"
+                value: "10"
+            resources:  # user defined resources
+              limits:
+                memory: 2Gi
+  trainingWorkers:
+    - dataset:
+        name: "edge1-surface-defect-detection-dataset"
+      template:
+        spec:
+          nodeName: $EDGE1_NODE
+          containers:
+            - image: kubeedge/sedna-example-federated-learning-mistnet-on-mnist-dataset-train:v0.4.0
+              name:  train-worker
+              imagePullPolicy: IfNotPresent
+              env:  # user defined environments
+                - name: "batch_size"
+                  value: "32"
+                - name: "learning_rate"
+                  value: "0.001"
+                - name: "epochs"
+                  value: "2"
+              resources:  # user defined resources
+                limits:
+                  memory: 2Gi
+    - dataset:
+        name: "edge2-surface-defect-detection-dataset"
+      template:
+        spec:
+          nodeName: $EDGE2_NODE
+          containers:
+            - image: kubeedge/sedna-example-federated-learning-mistnet-on-mnist-dataset-train:v0.4.0
+              name:  train-worker
+              imagePullPolicy: IfNotPresent
+              env:  # user defined environments
+                - name: "batch_size"
+                  value: "32"
+                - name: "learning_rate"
+                  value: "0.001"
+                - name: "epochs"
+                  value: "2"
+              resources:  # user defined resources
+                limits:
+                  memory: 2Gi
+EOF
+```
+
+```
+TODO: show the benifit of mistnet. for example, the compared results of fedavg & mistnet.
+
+```
+
+### Check Federated Learning Status
+
+```
+kubectl get federatedlearningjob surface-defect-detection
+```
+
+### Check Federated Learning Train Result
+
+After the job completed, you will find the model generated on the directory `/model` in `$EDGE1_NODE` and `$EDGE2_NODE`.

examples/federated_learning/yolov5_coco128_mistnet/aggregate.py

@@ -0,0 +1,35 @@
+# Copyright 2021 The KubeEdge Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from interface import mistnet, s3_transmitter, simple_chooser
+from interface import Dataset, Estimator
+from sedna.service.server import AggregationServer
+
+
+def run_server():
+    data = Dataset()
+    estimator = Estimator()
+
+    server = AggregationServer(
+        data=data,
+        estimator=estimator,
+        aggregation=mistnet,
+        transmitter=s3_transmitter,
+        chooser=simple_chooser)
+
+    server.start()
+
+
+if __name__ == '__main__':
+    run_server()

examples/federated_learning/yolov5_coco128_mistnet/interface.py

@@ -0,0 +1,149 @@
+# Copyright 2021 The KubeEdge Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from sedna.algorithms.aggregation import MistNet
+from sedna.algorithms.client_choose import SimpleClientChoose
+from sedna.common.config import Context
+from sedna.core.federated_learning import FederatedLearning
+
+simple_chooser = SimpleClientChoose(per_round=1)
+
+# It has been determined that mistnet is required here.
+mistnet = MistNet(cut_layer=Context.get_parameters("cut_layer"),
+                  epsilon=Context.get_parameters("epsilon"))
+
+# The function `get_transmitter_from_config()` returns an object instance.
+s3_transmitter = FederatedLearning.get_transmitter_from_config()
+
+
+class Dataset:
+    def __init__(self) -> None:
+        self.parameters = {
+            "datasource": "YOLO",
+            "data_params": "./coco128.yaml",
+            # Where the dataset is located
+            "data_path": "./data/COCO",
+            "train_path": "./data/COCO/coco128/images/train2017/",
+            "test_path": "./data/COCO/coco128/images/train2017/",
+            # number of training examples
+            "num_train_examples": 128,
+            # number of testing examples
+            "num_test_examples": 128,
+            # number of classes
+            "num_classes": 80,
+            # image size
+            "image_size": 640,
+            "classes":
+                [
+                    "person",
+                    "bicycle",
+                    "car",
+                    "motorcycle",
+                    "airplane",
+                    "bus",
+                    "train",
+                    "truck",
+                    "boat",
+                    "traffic light",
+                    "fire hydrant",
+                    "stop sign",
+                    "parking meter",
+                    "bench",
+                    "bird",
+                    "cat",
+                    "dog",
+                    "horse",
+                    "sheep",
+                    "cow",
+                    "elephant",
+                    "bear",
+                    "zebra",
+                    "giraffe",
+                    "backpack",
+                    "umbrella",
+                    "handbag",
+                    "tie",
+                    "suitcase",
+                    "frisbee",
+                    "skis",
+                    "snowboard",
+                    "sports ball",
+                    "kite",
+                    "baseball bat",
+                    "baseball glove",
+                    "skateboard",
+                    "surfboard",
+                    "tennis racket",
+                    "bottle",
+                    "wine glass",
+                    "cup",
+                    "fork",
+                    "knife",
+                    "spoon",
+                    "bowl",
+                    "banana",
+                    "apple",
+                    "sandwich",
+                    "orange",
+                    "broccoli",
+                    "carrot",
+                    "hot dog",
+                    "pizza",
+                    "donut",
+                    "cake",
+                    "chair",
+                    "couch",
+                    "potted plant",
+                    "bed",
+                    "dining table",
+                    "toilet",
+                    "tv",
+                    "laptop",
+                    "mouse",
+                    "remote",
+                    "keyboard",
+                    "cell phone",
+                    "microwave",
+                    "oven",
+                    "toaster",
+                    "sink",
+                    "refrigerator",
+                    "book",
+                    "clock",
+                    "vase",
+                    "scissors",
+                    "teddy bear",
+                    "hair drier",
+                    "toothbrush",
+                ],
+            "partition_size": 128,
+        }
+
+
+class Estimator:
+    def __init__(self) -> None:
+        self.model = None
+        self.hyperparameters = {
+            "type": "yolov5",
+            "rounds": 1,
+            "target_accuracy": 0.99,
+            "epochs": 500,
+            "batch_size": 16,
+            "optimizer": "SGD",
+            "linear_lr": False,
+            # The machine learning model
+            "model_name": "yolov5",
+            "model_config": "./yolov5s.yaml",
+            "train_params": "./hyp.scratch.yaml"
+        }

examples/federated_learning/yolov5_coco128_mistnet/train.py

@@ -0,0 +1,33 @@
+# Copyright 2021 The KubeEdge Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from interface import mistnet, s3_transmitter
+from interface import Dataset, Estimator
+from sedna.core.federated_learning import FederatedLearning
+
+
+def main():
+    data = Dataset()
+    estimator = Estimator()
+
+    fl_model = FederatedLearning(
+        estimator=estimator,
+        aggregation=mistnet,
+        transmitter=s3_transmitter)
+
+    fl_model.train(data)
+
+
+if __name__ == '__main__':
+    main()

lib/sedna/algorithms/aggregation/__init__.py

@@ -13,3 +13,4 @@
 # limitations under the License.
 
 from . import aggregation
+from .aggregation import FedAvg, MistNet

lib/sedna/algorithms/aggregation/aggregation.py

@@ -104,3 +104,14 @@ class FedAvg(BaseAggregation, abc.ABC):
             updates.append(row.tolist())
         self.weights = deepcopy(updates)
         return updates
+
+
+@ClassFactory.register(ClassType.FL_AGG)
+class MistNet(BaseAggregation, abc.ABC):
+    def __init__(self, cut_layer, epsilon=100):
+        super().__init__()
+        self.cut_layer = cut_layer
+        self.epsilon = epsilon
+
+    def aggregate(self, clients: List[AggClient]):
+        pass

lib/sedna/algorithms/client_choose/__init__.py

@@ -0,0 +1,15 @@
+# Copyright 2021 The KubeEdge Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .client_choose import SimpleClientChoose

lib/sedna/algorithms/client_choose/client_choose.py

@@ -0,0 +1,36 @@
+# Copyright 2021 The KubeEdge Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import abc
+
+
+class AbstractClientChoose(metaclass=abc.ABCMeta):
+    """
+    Abstract class of ClientChoose, which provides base client choose
+    algorithm interfaces in federated learning.
+    """
+
+    def __init__(self):
+        pass
+
+
+class SimpleClientChoose(AbstractClientChoose):
+    """
+    A Simple Implementation of Client Choose.
+    """
+
+    def __init__(self, per_round=1):
+        super().__init__()
+        self.per_round = per_round

lib/sedna/algorithms/transmitter/__init__.py

@@ -0,0 +1,15 @@
+# Copyright 2021 The KubeEdge Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .transmitter import S3Transmitter, WSTransmitter

lib/sedna/algorithms/transmitter/transmitter.py

@@ -0,0 +1,64 @@
+# Copyright 2021 The KubeEdge Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from abc import ABC, abstractmethod
+
+
+class AbstractTransmitter(ABC):
+    """
+    Abstract class of Transmitter, which provides base transmission
+    interfaces between edge and cloud.
+    """
+
+    @abstractmethod
+    def recv(self):
+        pass
+
+    @abstractmethod
+    def send(self, data):
+        pass
+
+
+class WSTransmitter(AbstractTransmitter, ABC):
+    """
+    An implementation of Transmitter based on WebSocket.
+    """
+
+    def recv(self):
+        pass
+
+    def send(self, data):
+        pass
+
+
+class S3Transmitter(AbstractTransmitter, ABC):
+    """
+    An implementation of Transmitter based on S3 protocol.
+    """
+
+    def __init__(self,
+                 s3_endpoint_url,
+                 access_key,
+                 secret_key,
+                 transmitter_url):
+        self.s3_endpoint_url = s3_endpoint_url
+        self.access_key = access_key
+        self.secret_key = secret_key
+        self.transmitter_url = transmitter_url
+
+    def recv(self):
+        pass
+
+    def send(self, data):
+        pass

lib/sedna/common/config.py

@@ -269,9 +269,16 @@ class BaseConfig(ConfigSerializable):
     # the name of FederatedLearningJob and others Job
     job_name = os.getenv("JOB_NAME", "sedna")
 
+    pretrained_model_url = os.getenv("PRETRAINED_MODEL_URL", "./")
     model_url = os.getenv("MODEL_URL")
     model_name = os.getenv("MODEL_NAME")
 
+    transmitter = os.getenv("TRANSMITTER", "ws")
+    agg_data_path = os.getenv("AGG_DATA_PATH", "./")
+    s3_endpoint_url = os.getenv("S3_ENDPOINT_URL", "")
+    access_key_id = os.getenv("ACCESS_KEY_ID", "")
+    secret_access_key = os.getenv("SECRET_ACCESS_KEY", "")
+
     # user parameter
     parameters = os.getenv("PARAMETERS")
 

lib/sedna/core/federated_learning/federated_learning.py

@@ -13,17 +13,23 @@
 # limitations under the License.
 
 
+import asyncio
+import sys
 import time
 
-from sedna.core.base import JobBase
-from sedna.common.config import Context
-from sedna.common.file_ops import FileOps
+from plato.clients import registry as client_registry
+from plato.config import Config
+
+from sedna.algorithms.transmitter import S3Transmitter, WSTransmitter
 from sedna.common.class_factory import ClassFactory, ClassType
-from sedna.service.client import AggregationClient
+from sedna.common.config import BaseConfig, Context
 from sedna.common.constant import K8sResourceKindStatus
+from sedna.common.file_ops import FileOps
+from sedna.core.base import JobBase
+from sedna.service.client import AggregationClient
 
 
-class FederatedLearning(JobBase):
+class FederatedLearningV0(JobBase):
     """
     Federated learning enables multiple actors to build a common, robust
     machine learning model without sharing data, thus allowing to address
@@ -50,6 +56,7 @@ class FederatedLearning(JobBase):
             aggregation="FedAvg"
         )
     """
+
     def __init__(self, estimator, aggregation="FedAvg"):
 
         protocol = Context.get_parameters("AGG_PROTOCOL", "ws")
@@ -178,3 +185,65 @@ class FederatedLearning(JobBase):
                     task_info,
                     K8sResourceKindStatus.RUNNING.value,
                     task_info_res)
+
+
+class FederatedLearning:
+    def __init__(self, data=None, estimator=None, aggregation=None, transmitter=None) -> None:
+        # set parameters
+        server = Config.server._asdict()
+        clients = Config.clients._asdict()
+        datastore = Config.data._asdict()
+        train = Config.trainer._asdict()
+
+        if data is not None:
+            for xkey in data.parameters:
+                datastore[xkey] = data.parameters[xkey]
+            Config.data = Config.namedtuple_from_dict(datastore)
+
+        self.model = None
+        if estimator is not None:
+            self.model = estimator.model
+            for xkey in estimator.hyperparameters:
+                train[xkey] = estimator.hyperparameters[xkey]
+            Config.trainer = Config.namedtuple_from_dict(train)
+
+        if aggregation is not None:
+            Config.algorithm = Config.namedtuple_from_dict(aggregation.parameters)
+            if aggregation.parameters["type"] == "mistnet":
+                clients["type"] = "mistnet"
+                server["type"] = "mistnet"
+
+        if isinstance(transmitter, S3Transmitter):
+            server["address"] = Context.get_parameters("AGG_IP")
+            server["port"] = Context.get_parameters("AGG_PORT")
+            server["s3_endpoint_url"] = transmitter.s3_endpoint_url
+            server["s3_bucket"] = transmitter.s3_bucket
+            server["access_key"] = transmitter.access_key
+            server["secret_key"] = transmitter.secret_key
+        elif isinstance(transmitter, WSTransmitter):
+            pass
+
+        Config.server = Config.namedtuple_from_dict(server)
+        Config.clients = Config.namedtuple_from_dict(clients)
+
+        # Config.store()
+        # create a client
+        self.client = client_registry.get(model=self.model)
+        self.client.configure()
+
+    @classmethod
+    def get_transmitter_from_config(cls):
+        if BaseConfig.transmitter == "ws":
+            return WSTransmitter()
+        elif BaseConfig.transmitter == "s3":
+            return S3Transmitter(s3_endpoint_url=BaseConfig.s3_endpoint_url,
+                                 access_key=BaseConfig.access_key_id,
+                                 secret_key=BaseConfig.secret_access_key,
+                                 transmitter_url=BaseConfig.agg_data_path)
+
+    def train(self):
+        if int(sys.version[2]) <= 6:
+            loop = asyncio.get_event_loop()
+            loop.run_until_complete(self.client.start_client())
+        else:
+            asyncio.run(self.client.start_client())