sedna

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 to install Sedna.

Prepare Dataset

Download dataset

Create data interface for EDGE1_NODE.

mkdir -p /data/1
cd /data/1
wget https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
unzip coco128.zip -d COCO

Create data interface for EDGE2_NODE.

mkdir -p /data/2
cd /data/2
wget https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
unzip coco128.zip -d COCO

Prepare Images

This example uses these images:

1. aggregation worker: kubeedge/sedna-example-federated-learning-mistnet-yolo-aggregator:v0.4.0
2. train worker: kubeedge/sedna-example-federated-learning-mistnet-yolo-client:v0.4.0

These images are generated by the script build_images.sh.

Create Federated Learning Job

Create Dataset

create dataset for $EDGE1_NODE and $EDGE2_NODE

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Dataset
metadata:
  name: "coco-dataset-1"
spec:
  url: "/data/1/COCO"
  format: "dir"
  nodeName: $EDGE1_NODE
EOF

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Dataset
metadata:
  name: "coco-dataset-2"
spec:
  url: "/data/2/COCO"
  format: "dir"
  nodeName: $EDGE2_NODE
EOF

Create Model

create the directory /model and /pretrained in $EDGE1_NODE and $EDGE2_NODE.

mkdir -p /model
mkdir -p /pretrained

create the directory /model and /pretrained in the host of $CLOUD_NODE (download links here)

# on the cloud side
mkdir -p /model
mkdir -p /pretrained
cd /pretrained
wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/examples/yolov5_coco128_mistnet/yolov5.pth

create model

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Model
metadata:
  name: "yolo-v5-model"
spec:
  url: "/model/yolov5.pth"
  format: "pth"
EOF

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Model
metadata:
  name: "yolo-v5-pretrained-model"
spec:
  url: "/pretrained/yolov5.pth"
  format: "pth"
EOF

Create a secret with your S3 user credential. (Optional)

kubectl create -f - <<EOF
apiVersion: v1
kind: Secret
metadata:
  name: mysecret
  annotations:
    s3-endpoint: s3.amazonaws.com 
    s3-usehttps: "1" 
stringData: 
  ACCESS_KEY_ID: XXXX
  SECRET_ACCESS_KEY: XXXXXXXX
EOF

Start Federated Learning Job

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: FederatedLearningJob
metadata:
  name: yolo-v5
spec:
  pretrainedModel: # option
    name: "yolo-v5-pretrained-model"
  transmitter: # option
    ws: { } # option, by default
    s3: # optional, but at least one
      aggDataPath: "s3://sedna/fl/aggregation_data"
      credentialName: mysecret
  aggregationWorker:
    model:
      name: "yolo-v5-model"
    template:
      spec:
        nodeName: $CLOUD_NODE
        containers:
          - image: kubeedge/sedna-example-federated-learning-mistnet-yolo-aggregator: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: "32"
              - name: "epochs"
                value: "100"
            resources: # user defined resources
              limits:
                memory: 8Gi
  trainingWorkers:
    - dataset:
        name: "coco-dataset-1"
      template:
        spec:
          nodeName: $EDGE1_NODE
          containers:
            - image: kubeedge/sedna-example-federated-learning-mistnet-yolo-client:v0.4.0
              name: train-worker
              imagePullPolicy: IfNotPresent
              args: [ "-i", "1" ]
              env: # user defined environments
                - name: "cut_layer"
                  value: "4"
                - name: "epsilon"
                  value: "100"
                - name: "aggregation_algorithm"
                  value: "mistnet"
                - name: "batch_size"
                  value: "32"
                - name: "learning_rate"
                  value: "0.001"
                - name: "epochs"
                  value: "1"
              resources: # user defined resources
                limits:
                  memory: 2Gi
    - dataset:
        name: "coco-dataset-2"
      template:
        spec:
          nodeName: $EDGE2_NODE
          containers:
            - image: kubeedge/sedna-example-federated-learning-mistnet-yolo-client:v0.4.0
              name: train-worker
              imagePullPolicy: IfNotPresent
              args: [ "-i", "2" ]
              env: # user defined environments
                - name: "cut_layer"
                  value: "4"
                - name: "epsilon"
                  value: "100"
                - name: "aggregation_algorithm"
                  value: "mistnet"
                - name: "batch_size"
                  value: "32"
                - name: "learning_rate"
                  value: "0.001"
                - name: "epochs"
                  value: "1"
              resources: # user defined resources
                limits:
                  memory: 2Gi
EOF

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