Python Client for the Federated learning system
Client software is used by data organization providing data for a particular FL problem and has the following responsibilities.
- Connect with the Selector to indicate availability to participate in the FL round
- Train the received model on the data available locally
- Report new checkpoint and weight (amount of data used) back to the Selector
- Each client for specific fl problem knows the address of the Selector to contact and sends a connection request to express availability
- After it gets selected it receives the initial files like model, checkpoint, etc. for the problem through the Selector and starts training the model with the local data
- Once training is complete the updated checkpoint and weight, no of training batches, are sent to the Selector
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Build the client docker image:
docker build -t fl-client . -
Spawn a container with device specific data mounted:
docker run -it --network="host" -v /path_to_device_data/device1:/device_data -v /path_to/config.yaml:/client/config.yaml --name client1 fl-clientFor example -
docker run -it --network="host" -v $PWD/../device1:/device_data -v $PWD/../device1/config/config.yaml:/client/config.yaml --name client1 fl-client -
To restart the container:
docker start -i client1 -
To inspect the running container, open bash using:
docker exec -t -i client1 /bin/bash -
To remove the container:
docker rm client1 -
To simply run and inspect a new container, execute:
docker run -it fl-client bash
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Create a virtual environment with
virtualenv fl -
Activate virtual environment with
source fl/bin/activate -
Install dependencies using
pip install -r requirements.txt -
Make necessary changes in
config.yaml -
Run fl-client with
python main.py
