This classic example of sentiment analysis is well suited both as a lightweight test when learning FEDn and developing on FEDn in psedo-distributed mode. A normal high-end laptop or a workstation should be able to sustain at least 5 clients. The example is also useful for general scalability tests in fully distributed mode.

To download and prepare the partitioned dataset (creates 10 partitions) in ./data/clients/0 etc:

python create_data_partitions.py

Navigate to 'https://localhost:8090' (or the url of your Reducer) and follow instructions to upload the compute package in 'package/package.tar.gz' and the initial model in 'initial_model/initial_model.npz'.

Whenever you make updates to the client code (such as altering any of the settings in the above mentioned file), you need to re-package the compute package:

tar -czvf package.tar.gz client

To clear the system and set a new compute package, see: https://github.com/scaleoutsystems/fedn/blob/master/docs/FAQ.md

For an explaination of the compute package structure and content: https://github.com/scaleoutsystems/fedn/blob/develop/docs/tutorial.md

The baseline CNN-LSTM is specified in the file 'client/init_model.py'. This script creates an untrained neural network and serializes that to a file. If you wish to alter the initial model, edit 'init_model.py' and 'models/imdb_model.py' then regenerate the initial model file (install dependencies as needed, see requirements.txt):

python init_model.py 

We have made it possible to configure a couple of settings to vary the conditions for the training. These configurations are expsosed in the file 'settings.yaml':

# Parameters for local training
test_size: 0.25
batch_size: 32
epochs: 1
#trained layers 0 means all layers in the model, otherwise just select the layers based on the identified number
trained_Layers: 1

1. First, download 'client.yaml' from the Reducer 'Network' page, and replace the content in your local 'client.yaml'.
2. Start a client. Here there are different options (see below):
   • Docker
   • docker-compose

To start N clients:

docker-compose -f docker-compose.dev.yaml -f extra-hosta.yaml up --build 

When clients are running, navigate to the 'Control' page of the Reducer to start the training.

Apache-2.0 (see LICENSE file for full information).